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Earl RJ, Ford TC, Lum JAG, Enticott PG, Hill AT. Exploring aperiodic activity in first episode schizophrenia spectrum psychosis: A resting-state EEG analysis. Brain Res 2024; 1840:149052. [PMID: 38844199 DOI: 10.1016/j.brainres.2024.149052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Revised: 05/29/2024] [Accepted: 06/03/2024] [Indexed: 06/10/2024]
Abstract
Abnormalities in brain oscillatory patterns have long been observed in schizophrenia and psychotic disorders more broadly. However, far less is known about aperiodic neural activity in these disorders, which has been linked to excitation/inhibition balance and neuronal population spiking within the brain. Here, we analysed resting-state electroencephalographic (EEG) recordings from 43 first episode schizophrenia spectrum psychosis (FESSP) patients and 28 healthy controls to examine whether aperiodic activity is disrupted in FESSP. We further assessed potential associations between aperiodic activity in FESSP and clinical symptom severity using the Brief Psychiatric Rating Scale (BPRS), the Scale for the Assessment of Negative Symptoms (SANS), and the Scale for the Assessment of Positive Symptoms (SAPS). We found no significant differences in either the 1/f-like aperiodic exponent or the broadband aperiodic offset between the FESSP and healthy control groups when analysing the global neural signal averaged across all EEG electrodes. Bayesian analyses further supported these non-significant findings. However, additional non-parametric cluster-based permutation analyses did identify reduced aperiodic offset in the FESSP group, relative to controls across broad central, temporal, parietal and select frontal regions. No associations were found between either exponent or offset and clinical symptom severity when examining all FESSP participants, irrespective of antipsychotic medication status. However, offset was shown to predict BPRS and SANS scores in medication naive patients. In sum, this research presents an initial analysis of aperiodic neural activity in FESSP, offering preliminary evidence of altered aperiodic offset in this disorder. This contributes to a broader understanding of disrupted neural dynamics in early psychosis.
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Affiliation(s)
- Ruby J Earl
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Burwood, Australia
| | - Talitha C Ford
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Burwood, Australia; Centre for Human Psychopharmacology, Faculty of Health, Arts and Design, Swinburne University of Technology, Melbourne, Victoria, Australia
| | - Jarrad A G Lum
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Burwood, Australia
| | - Peter G Enticott
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Burwood, Australia
| | - Aron T Hill
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Burwood, Australia.
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Stanyard RA, Mason D, Ellis C, Dickson H, Short R, Batalle D, Arichi T. Aperiodic and Hurst EEG exponents across early human brain development: A systematic review. Dev Cogn Neurosci 2024; 68:101402. [PMID: 38917647 PMCID: PMC11254951 DOI: 10.1016/j.dcn.2024.101402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 04/12/2024] [Accepted: 06/03/2024] [Indexed: 06/27/2024] Open
Abstract
In electroencephalographic (EEG) data, power-frequency slope exponents (1/f_β) can provide non-invasive markers of in vivo neural activity excitation-inhibition (E:I) balance. E:I balance may be altered in neurodevelopmental conditions; hence, understanding how 1/fβ evolves across infancy/childhood has implications for developing early assessments/interventions. This systematic review (PROSPERO-ID: CRD42023363294) explored the early maturation (0-26 yrs) of resting-state EEG 1/f measures (aperiodic [AE], power law [PLE] and Hurst [HE] exponents), including studies containing ≥1 1/f measures and ≥10 typically developing participants. Five databases (including Embase and Scopus) were searched during March 2023. Forty-two studies were identified (Nparticipants=3478). Risk of bias was assessed using the Quality Assessment with Diverse Studies tool. Narrative synthesis of HE data suggests non-stationary EEG activity occurs throughout development. Age-related trends were complex, with rapid decreases in AEs during infancy and heterogenous changes thereafter. Regionally, AE maxima shifted developmentally, potentially reflecting spatial trends in maturing brain connectivity. This work highlights the importance of further characterising the development of 1/f measures to better understand how E:I balance shapes brain and cognitive development.
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Affiliation(s)
- R A Stanyard
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom.
| | - D Mason
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - C Ellis
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - H Dickson
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - R Short
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - D Batalle
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - T Arichi
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, United Kingdom; Children's Neurosciences, Evelina London Children's Hospital, Guy's and St Thomas' NHS Foundation Trust, United Kingdom; Department of Bioengineering, Imperial College London, United Kingdom
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Arutiunian V, Arcara G, Buyanova I, Fedorov M, Davydova E, Pereverzeva D, Sorokin A, Tyushkevich S, Mamokhina U, Danilina K, Dragoy O. Abnormalities in both stimulus-induced and baseline MEG alpha oscillations in the auditory cortex of children with Autism Spectrum Disorder. Brain Struct Funct 2024; 229:1225-1242. [PMID: 38683212 DOI: 10.1007/s00429-024-02802-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 04/22/2024] [Indexed: 05/01/2024]
Abstract
The neurobiology of Autism Spectrum Disorder (ASD) is hypothetically related to the imbalance between neural excitation (E) and inhibition (I). Different studies have revealed that alpha-band (8-12 Hz) activity in magneto- and electroencephalography (MEG and EEG) may reflect E and I processes and, thus, can be of particular interest in ASD research. Previous findings indicated alterations in event-related and baseline alpha activity in different cortical systems in individuals with ASD, and these abnormalities were associated with core and co-occurring conditions of ASD. However, the knowledge on auditory alpha oscillations in this population is limited. This MEG study investigated stimulus-induced (Event-Related Desynchronization, ERD) and baseline alpha-band activity (both periodic and aperiodic) in the auditory cortex and also the relationships between these neural activities and behavioral measures of children with ASD. Ninety amplitude-modulated tones were presented to two groups of children: 20 children with ASD (5 girls, Mage = 10.03, SD = 1.7) and 20 typically developing controls (9 girls, Mage = 9.11, SD = 1.3). Children with ASD had a bilateral reduction of alpha-band ERD, reduced baseline aperiodic-adjusted alpha power, and flattened aperiodic exponent in comparison to TD children. Moreover, lower raw baseline alpha power and aperiodic offset in the language-dominant left auditory cortex were associated with better language skills of children with ASD measured in formal assessment. The findings highlighted the alterations of E / I balance metrics in response to basic auditory stimuli in children with ASD and also provided evidence for the contribution of low-level processing to language difficulties in ASD.
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Affiliation(s)
- Vardan Arutiunian
- Center for Child Health, Behavior and Development, Seattle Children's Research Institute, 1920 Terry Ave, Seattle, WA, 98101, United States of America.
| | | | - Irina Buyanova
- Center for Language and Brain, HSE University, Moscow, Russia
- University of Otago, Dunedin, New Zealand
| | - Makar Fedorov
- Center for Language and Brain, HSE University, Nizhny Novgorod, Russia
| | - Elizaveta Davydova
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
- Chair of Differential Psychology and Psychophysiology, Moscow State University of Psychology and Education, Moscow, Russia
| | - Darya Pereverzeva
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
| | - Alexander Sorokin
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
- Haskins Laboratories, New Haven, CT, United States of America
| | - Svetlana Tyushkevich
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
| | - Uliana Mamokhina
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
| | - Kamilla Danilina
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
- Scientific Research and Practical Center of Pediatric Psychoneurology, Moscow, Russia
| | - Olga Dragoy
- Center for Language and Brain, HSE University, Moscow, Russia
- Institute of Linguistics, Russian Academy of Sciences, Moscow, Russia
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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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Pollak M, Leroy S, Röhr V, Brown EN, Spies C, Koch S. Electroencephalogram Biomarkers from Anesthesia Induction to Identify Vulnerable Patients at Risk for Postoperative Delirium. Anesthesiology 2024; 140:979-989. [PMID: 38295384 DOI: 10.1097/aln.0000000000004929] [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: 02/02/2024]
Abstract
BACKGROUND Postoperative delirium is a common complication in elderly patients undergoing anesthesia. Even though it is increasingly recognized as an important health issue, the early detection of patients at risk for postoperative delirium remains a challenge. This study aims to identify predictors of postoperative delirium by analyzing frontal electroencephalogram at propofol-induced loss of consciousness. METHODS This prospective, observational single-center study included patients older than 70 yr undergoing general anesthesia for a planned surgery. Frontal electroencephalogram was recorded on the day before surgery (baseline) and during anesthesia induction (1, 2, and 15 min after loss of consciousness). Postoperative patients were screened for postoperative delirium twice daily for 5 days. Spectral analysis was performed using the multitaper method. The electroencephalogram spectrum was decomposed in periodic and aperiodic (correlates to asynchronous spectrum wide activity) components. The aperiodic component is characterized by its offset (y intercept) and exponent (the slope of the curve). Computed electroencephalogram parameters were compared between patients who developed postoperative delirium and those who did not. Significant electroencephalogram parameters were included in a binary logistic regression analysis to predict vulnerability for postoperative delirium. RESULTS Of 151 patients, 50 (33%) developed postoperative delirium. At 1 min after loss of consciousness, postoperative delirium patients demonstrated decreased alpha (postoperative delirium: 0.3 μV2 [0.21 to 0.71], no postoperative delirium: 0.55 μV2 [0.36 to 0.74]; P = 0.019] and beta band power [postoperative delirium: 0.27 μV2 [0.12 to 0.38], no postoperative delirium: 0.38 μV2 [0.25 to 0.48]; P = 0.003) and lower spectral edge frequency (postoperative delirium: 10.45 Hz [5.65 to 15.04], no postoperative delirium: 14.56 Hz [9.51 to 16.65]; P = 0.01). At 15 min after loss of consciousness, postoperative delirium patients displayed a decreased aperiodic offset (postoperative delirium: 0.42 μV2 (0.11 to 0.69), no postoperative delirium: 0.62 μV2 [0.37 to 0.79]; P = 0.004). The logistic regression model predicting postoperative delirium vulnerability demonstrated an area under the curve of 0.73 (0.69 to 0.75). CONCLUSIONS The findings suggest that electroencephalogram markers obtained during loss of consciousness at anesthesia induction may serve as electroencephalogram-based biomarkers to identify at an early time patients at risk of developing postoperative delirium. EDITOR’S PERSPECTIVE
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Affiliation(s)
- Marie Pollak
- Department of Anesthesiology and Operative Intensive Care Medicine, Charité University Medicine Berlin, Berlin, Germany
| | - Sophie Leroy
- Department of Anesthesiology and Operative Intensive Care Medicine, Charité University Medicine Berlin, Berlin, Germany
| | - Vera Röhr
- Neurotechnology Group, Technical University Berlin, Berlin, Germany
| | - Emery Neal Brown
- Harvard-MIT Health Sciences and Technology Program, Massachusetts Institute of Technology, Cambridge, Massachusetts; and Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Claudia Spies
- Department of Anesthesiology and Operative Intensive Care Medicine, Charité University Medicine Berlin, Berlin, Germany
| | - Susanne Koch
- Department of Anesthesiology and Operative Intensive Care Medicine, Charité University Medicine Berlin, Berlin, Germany; and Department of Anesthesia, University of Southern Denmark, Odense, Denmark
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6
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Kawaguchi T, Ono K, Hikawa H. Electroencephalogram-Based Facial Gesture Recognition Using Self-Organizing Map. SENSORS (BASEL, SWITZERLAND) 2024; 24:2741. [PMID: 38732846 PMCID: PMC11085705 DOI: 10.3390/s24092741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/18/2024] [Accepted: 04/23/2024] [Indexed: 05/13/2024]
Abstract
Brain-computer interfaces (BCIs) allow information to be transmitted directly from the human brain to a computer, enhancing the ability of human brain activity to interact with the environment. In particular, BCI-based control systems are highly desirable because they can control equipment used by people with disabilities, such as wheelchairs and prosthetic legs. BCIs make use of electroencephalograms (EEGs) to decode the human brain's status. This paper presents an EEG-based facial gesture recognition method based on a self-organizing map (SOM). The proposed facial gesture recognition uses α, β, and θ power bands of the EEG signals as the features of the gesture. The SOM-Hebb classifier is utilized to classify the feature vectors. We utilized the proposed method to develop an online facial gesture recognition system. The facial gestures were defined by combining facial movements that are easy to detect in EEG signals. The recognition accuracy of the system was examined through experiments. The recognition accuracy of the system ranged from 76.90% to 97.57% depending on the number of gestures recognized. The lowest accuracy (76.90%) occurred when recognizing seven gestures, though this is still quite accurate when compared to other EEG-based recognition systems. The implemented online recognition system was developed using MATLAB, and the system took 5.7 s to complete the recognition flow.
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Affiliation(s)
| | | | - Hiroomi Hikawa
- Faculty of Engineering Science, Kansai University, Osaka 564-8680, Japan
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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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Xia Y, Wang X, You W, Hua L, Dai Z, Tang H, Yan R, Yao Z, Lu Q. Impulsivity and neural correlates of response inhibition in bipolar disorder and their unaffected relatives: A MEG study. J Affect Disord 2024; 351:430-441. [PMID: 38246283 DOI: 10.1016/j.jad.2024.01.131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 12/29/2023] [Accepted: 01/14/2024] [Indexed: 01/23/2024]
Abstract
BACKGROUND Response inhibition is a core cognitive impairment in bipolar disorder (BD), leading to increased impulsivity in BD. However, the relationship between the neural mechanisms underlying impaired response inhibition and impulsivity in BD is not yet clear. Individuals who are genetically predisposed to BD give a way of identifying potential endophenotypes. METHODS A total of 97 participants, including 39 patients with BD, 22 unaffected relatives (UR) of patients with BD, and 36 healthy controls performed a Go/No-Go task during magnetoencephalography. We carried out time-frequency and connectivity analysis on MEG data. RESULTS Decreased beta power, prolonged latency and increased peak frequency in rIFG, decreased beta power in pre-SMA and reduced rIFG-to-pre-SMA connectivity were found in BD relative to healthy controls. In the UR group, we found a decrease in the beta power of pre-SMA and prolonged latency of rIFG. Furthermore, increased motor impulsiveness in BD was related to abnormal alterations in beta oscillatory activity of rIFG and functional connectivity between rIFG and pre-SMA. CONCLUSIONS Hypoactivity activity in rIFG and impaired dominant role of rIFG in the prefrontal control network may underlie the neuropathology of response inhibition dysfunction, resulting increased motor impulsivity in BD. Our findings point to measuring rIFG dysfunction as a potential means of identifying individuals at genetic high risk for transition to BD disease expression.
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Affiliation(s)
- Yi Xia
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Xiaoqin Wang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Wei You
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Lingling Hua
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Zhongpeng Dai
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, Southeast University, Nanjing 210096, China
| | - Hao Tang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Rui Yan
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - ZhiJian Yao
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China; School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China.
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, Southeast University, Nanjing 210096, China.
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Fang Z, Sack AT, Leunissen I. The phase of tACS-entrained pre-SMA beta oscillations modulates motor inhibition. Neuroimage 2024; 290:120572. [PMID: 38490584 DOI: 10.1016/j.neuroimage.2024.120572] [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] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 03/17/2024] Open
Abstract
Inhibitory control has been linked to beta oscillations in the fronto-basal ganglia network. Here we aim to investigate the functional role of the phase of this oscillatory beta rhythm for successful motor inhibition. We applied 20 Hz transcranial alternating current stimulation (tACS) to the pre-supplementary motor area (pre-SMA) while presenting stop signals at 4 (Experiment 1) and 8 (Experiment 2) equidistant phases of the tACS entrained beta oscillations. Participants showed better inhibitory performance when stop signals were presented at the trough of the beta oscillation whereas their inhibitory control performance decreased with stop signals being presented at the oscillatory beta peak. These results are consistent with the communication through coherence theory, in which postsynaptic effects are thought to be greater when an input arrives at an optimal phase within the oscillatory cycle of the target neuronal population. The current study provides mechanistic insights into the neural communication principles underlying successful motor inhibition and may have implications for phase-specific interventions aimed at treating inhibitory control disorders such as PD or OCD.
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Affiliation(s)
- Zhou Fang
- Section Brain Stimulation and Cognition, Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands; Maastricht Brain Imaging Centre (MBIC), Maastricht University, Oxfordlaan 55, 6229EV, Maastricht, The Netherlands
| | - Alexander T Sack
- Section Brain Stimulation and Cognition, Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands; Maastricht Brain Imaging Centre (MBIC), Maastricht University, Oxfordlaan 55, 6229EV, Maastricht, The Netherlands; Centre for Integrative Neuroscience, Faculty of Psychology and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands
| | - Inge Leunissen
- Section Brain Stimulation and Cognition, Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands; Maastricht Brain Imaging Centre (MBIC), Maastricht University, Oxfordlaan 55, 6229EV, Maastricht, The Netherlands.
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McKeon SD, Perica MI, Parr AC, Calabro FJ, Foran W, Hetherington H, Moon CH, Luna B. Aperiodic EEG and 7T MRSI evidence for maturation of E/I balance supporting the development of working memory through adolescence. Dev Cogn Neurosci 2024; 66:101373. [PMID: 38574406 PMCID: PMC11000172 DOI: 10.1016/j.dcn.2024.101373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 04/01/2024] [Accepted: 04/02/2024] [Indexed: 04/06/2024] Open
Abstract
Adolescence has been hypothesized to be a critical period for the development of human association cortex and higher-order cognition. A defining feature of critical period development is a shift in the excitation: inhibition (E/I) balance of neural circuitry, however how changes in E/I may enhance cortical circuit function to support maturational improvements in cognitive capacities is not known. Harnessing ultra-high field 7 T MR spectroscopy and EEG in a large, longitudinal cohort of youth (N = 164, ages 10-32 years old, 347 neuroimaging sessions), we delineate biologically specific associations between age-related changes in excitatory glutamate and inhibitory GABA neurotransmitters and EEG-derived measures of aperiodic neural activity reflective of E/I balance in prefrontal association cortex. Specifically, we find that developmental increases in E/I balance reflected in glutamate:GABA balance are linked to changes in E/I balance assessed by the suppression of prefrontal aperiodic activity, which in turn facilitates robust improvements in working memory. These findings indicate a role for E/I-engendered changes in prefrontal signaling mechanisms in the maturation of cognitive maintenance. More broadly, this multi-modal imaging study provides evidence that human association cortex undergoes physiological changes consistent with critical period plasticity during adolescence.
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Affiliation(s)
- Shane D McKeon
- Department of Bioengineering, University of Pittsburgh, PA, USA; The Center for the Neural Basis of Cognition, University of Pittsburgh, PA, USA.
| | - Maria I Perica
- The Center for the Neural Basis of Cognition, University of Pittsburgh, PA, USA; Department of Psychology, University of Pittsburgh, PA, USA
| | - Ashley C Parr
- The Center for the Neural Basis of Cognition, University of Pittsburgh, PA, USA; Department of Psychiatry, University of Pittsburgh, PA, USA
| | - Finnegan J Calabro
- Department of Bioengineering, University of Pittsburgh, PA, USA; The Center for the Neural Basis of Cognition, University of Pittsburgh, PA, USA; Department of Psychiatry, University of Pittsburgh, PA, USA
| | - Will Foran
- Department of Psychiatry, University of Pittsburgh, PA, USA
| | - Hoby Hetherington
- Resonance Research Incorporated, Billerica, MA, USA; Department of Radiology, University of Missouri, Columbia, MO, USA
| | - Chan-Hong Moon
- Department of Radiology, University of Pittsburgh, PA, USA
| | - Beatriz Luna
- The Center for the Neural Basis of Cognition, University of Pittsburgh, PA, USA; Department of Psychiatry, University of Pittsburgh, PA, USA.
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11
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Finley AJ, Angus DJ, Knight EL, van Reekum CM, Lachman ME, Davidson RJ, Schaefer SM. Resting EEG Periodic and Aperiodic Components Predict Cognitive Decline Over 10 Years. J Neurosci 2024; 44:e1332232024. [PMID: 38373849 PMCID: PMC10977020 DOI: 10.1523/jneurosci.1332-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 01/03/2024] [Accepted: 01/23/2024] [Indexed: 02/21/2024] Open
Abstract
Measures of intrinsic brain function at rest show promise as predictors of cognitive decline in humans, including EEG metrics such as individual α peak frequency (IAPF) and the aperiodic exponent, reflecting the strongest frequency of α oscillations and the relative balance of excitatory/inhibitory neural activity, respectively. Both IAPF and the aperiodic exponent decrease with age and have been associated with worse executive function and working memory. However, few studies have jointly examined their associations with cognitive function, and none have examined their association with longitudinal cognitive decline rather than cross-sectional impairment. In a preregistered secondary analysis of data from the longitudinal Midlife in the United States (MIDUS) study, we tested whether IAPF and aperiodic exponent measured at rest predict cognitive function (N = 235; age at EEG recording M = 55.10, SD = 10.71) over 10 years. The IAPF and the aperiodic exponent interacted to predict decline in overall cognitive ability, even after controlling for age, sex, education, and lag between data collection time points. Post hoc tests showed that "mismatched" IAPF and aperiodic exponents (e.g., higher exponent with lower IAPF) predicted greater cognitive decline compared to "matching" IAPF and aperiodic exponents (e.g., higher exponent with higher IAPF; lower IAPF with lower aperiodic exponent). These effects were largely driven by measures of executive function. Our findings provide the first evidence that IAPF and the aperiodic exponent are joint predictors of cognitive decline from midlife into old age and thus may offer a useful clinical tool for predicting cognitive risk in aging.
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Affiliation(s)
- Anna J Finley
- Institute on Aging, University of Wisconsin-Madison, Madison, Wisconsin 53706
| | - Douglas J Angus
- School of Psychology, Bond University, Robina, Queensland 4226, Australia
| | - Erik L Knight
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado 80309
| | - Carien M van Reekum
- School of Psychology and Clinical Language Sciences, University of Reading, Reading RG6 6ES, United Kingdom
| | - Margie E Lachman
- Department of Psychology, Brandeis University, Waltham, Massachusetts 02453
| | - Richard J Davidson
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, Wisconsin 53706
| | - Stacey M Schaefer
- Institute on Aging, University of Wisconsin-Madison, Madison, Wisconsin 53706
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12
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Carmichael DW, Vulliemoz S, Murta T, Chaudhary U, Perani S, Rodionov R, Rosa MJ, Friston KJ, Lemieux L. Measurement of the Mapping between Intracranial EEG and fMRI Recordings in the Human Brain. Bioengineering (Basel) 2024; 11:224. [PMID: 38534498 DOI: 10.3390/bioengineering11030224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/15/2023] [Accepted: 12/20/2023] [Indexed: 03/28/2024] Open
Abstract
There are considerable gaps in our understanding of the relationship between human brain activity measured at different temporal and spatial scales. Here, electrocorticography (ECoG) measures were used to predict functional MRI changes in the sensorimotor cortex in two brain states: at rest and during motor performance. The specificity of this relationship to spatial co-localisation of the two signals was also investigated. We acquired simultaneous ECoG-fMRI in the sensorimotor cortex of three patients with epilepsy. During motor activity, high gamma power was the only frequency band where the electrophysiological response was co-localised with fMRI measures across all subjects. The best model of fMRI changes across states was its principal components, a parsimonious description of the entire ECoG spectrogram. This model performed much better than any others that were based either on the classical frequency bands or on summary measures of cross-spectral changes. The region-specific fMRI signal is reflected in spatially and spectrally distributed EEG activity.
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Affiliation(s)
- David W Carmichael
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
- Developmental Imaging and Biophysics section, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London WC1E 6BT, UK
| | - Serge Vulliemoz
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London WC1E 6BT, UK
- Epilepsy Unit, Neurology Department, University Hospital and University of Geneva, 1211 Geneva 14, Switzerland
| | - Teresa Murta
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London WC1E 6BT, UK
- Department of Bioengineering, Institute for Systems and Robotics, Instituto Superior Tecnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
| | - Umair Chaudhary
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London WC1E 6BT, UK
| | - Suejen Perani
- Developmental Imaging and Biophysics section, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK
| | - Roman Rodionov
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London WC1E 6BT, UK
| | - Maria Joao Rosa
- Department of Computer Science, University College London, London WC1E 6BT, UK
| | - Karl J Friston
- Wellcome Trust Centre for Human Neuroimaging, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Louis Lemieux
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London WC1E 6BT, UK
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13
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Hassan A, Deshun Z. Nature's therapeutic power: a study on the psychophysiological effects of touching ornamental grass in Chinese women. JOURNAL OF HEALTH, POPULATION, AND NUTRITION 2024; 43:23. [PMID: 38310320 PMCID: PMC10838459 DOI: 10.1186/s41043-024-00514-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 01/25/2024] [Indexed: 02/05/2024]
Abstract
The health of city residents is at risk due to the high rate of urbanization and the extensive use of electronics. In the context of urbanization, individuals have become increasingly disconnected from nature, resulting in elevated stress levels among adults. The goal of this study was to investigate the physical and psychological benefits of spending time in nature. The benefits of touching real grass and artificial turf (the control activity) outdoors with the palm of the hand for five minutes were measured. Blood pressure and electroencephalography (EEG) as well as State-trait Anxiety Inventory (STAI) scores, and the semantic differential scale (SDM) were used to investigate psychophysiological responses. Touching real grass was associated with significant changes in brainwave rhythms and a reduction in both systolic and diastolic blood pressure compared to touching artificial turf. In addition, SDM scores revealed that touching real grass increased relaxation, comfort, and a sense of naturalness while decreasing anxiety levels. Compared to the control group, the experimental group had higher mean scores in both meditation and attentiveness. Our findings indicate that contact with real grass may reduce physiological and psychological stress in adults.
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Affiliation(s)
- Ahmad Hassan
- College of Architecture and Urban Planning, Tongji University, 1239 Siping Rd, Shanghai, People's Republic of China.
| | - Zhang Deshun
- College of Architecture and Urban Planning, Tongji University, 1239 Siping Rd, Shanghai, People's Republic of China.
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14
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Lesser RP, Webber WRS, Miglioretti DL. Pan-cortical electrophysiologic changes underlying attention. Sci Rep 2024; 14:2680. [PMID: 38302535 PMCID: PMC10834435 DOI: 10.1038/s41598-024-52717-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 01/23/2024] [Indexed: 02/03/2024] Open
Abstract
We previously reported that pan-cortical effects occur when cognitive tasks end afterdischarges. For this report, we analyzed wavelet cross-coherence changes during cognitive tasks used to terminate afterdischarges studying multiple time segments and multiple groups of inter-electrode-con distances. We studied 12 patients with intractable epilepsy, with 970 implanted electrode contacts, and 39,871 electrode contact combinations. When cognitive tasks ended afterdischarges, coherence varied similarly across the cortex throughout the tasks, but there were gradations with time, distance, and frequency: (1) They tended to progressively decrease relative to baseline with time and then to increase toward baseline when afterdischarges ended. (2) During most time segments, decreases from baseline were largest for the closest inter-contact distances, moderate for intermediate inter-contact distances, and smallest for the greatest inter-contact distances. With respect to our patients' intractable epilepsy, the changes found suggest that future therapies might treat regions beyond those closest to regions of seizure onset and treat later in a seizure's evolution. Similar considerations might apply to other disorders. Our findings also suggest that cognitive tasks can result in pan-cortical coherence changes that participate in underlying attention, perhaps complementing the better-known regional mechanisms.
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Affiliation(s)
- Ronald P Lesser
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.
- Department of Neurological Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.
| | - W R S Webber
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Diana L Miglioretti
- Department of Public Health Sciences, Davis, School of Medicine, University of California, Davis, CA, 95616, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, 98101, USA
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15
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Nie JZ, Flint RD, Prakash P, Hsieh JK, Mugler EM, Tate MC, Rosenow JM, Slutzky MW. High-Gamma Activity Is Coupled to Low-Gamma Oscillations in Precentral Cortices and Modulates with Movement and Speech. eNeuro 2024; 11:ENEURO.0163-23.2023. [PMID: 38242691 PMCID: PMC10867721 DOI: 10.1523/eneuro.0163-23.2023] [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: 05/16/2023] [Revised: 10/26/2023] [Accepted: 12/06/2023] [Indexed: 01/21/2024] Open
Abstract
Planning and executing motor behaviors requires coordinated neural activity among multiple cortical and subcortical regions of the brain. Phase-amplitude coupling between the high-gamma band amplitude and the phase of low frequency oscillations (theta, alpha, beta) has been proposed to reflect neural communication, as has synchronization of low-gamma oscillations. However, coupling between low-gamma and high-gamma bands has not been investigated. Here, we measured phase-amplitude coupling between low- and high-gamma in monkeys performing a reaching task and in humans either performing finger-flexion or word-reading tasks. We found significant coupling between low-gamma phase and high-gamma amplitude in multiple sensorimotor and premotor cortices of both species during all tasks. This coupling modulated with the onset of movement. These findings suggest that interactions between the low and high gamma bands are markers of network dynamics related to movement and speech generation.
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Affiliation(s)
- Jeffrey Z Nie
- Southern Illinois University School of Medicine, Springfield 62794, Illinois
- Departments of Neurology, Northwestern University, Chicago 60611, Illinois
| | - Robert D Flint
- Departments of Neurology, Northwestern University, Chicago 60611, Illinois
| | - Prashanth Prakash
- Departments of Neurology, Northwestern University, Chicago 60611, Illinois
| | - Jason K Hsieh
- Departments of Neurology, Northwestern University, Chicago 60611, Illinois
- Neurological Surgery, Northwestern University, Chicago 60611, Illinois
- Department of Neurosurgery, Neurological Institute, Cleveland Clinic Foundation, Cleveland, Ohio
| | - Emily M Mugler
- Departments of Neurology, Northwestern University, Chicago 60611, Illinois
| | - Matthew C Tate
- Departments of Neurology, Northwestern University, Chicago 60611, Illinois
- Neurological Surgery, Northwestern University, Chicago 60611, Illinois
| | - Joshua M Rosenow
- Departments of Neurology, Northwestern University, Chicago 60611, Illinois
- Neurological Surgery, Northwestern University, Chicago 60611, Illinois
- Physical Medicine & Rehabilitation, Northwestern University, Chicago 60611, Illinois
- Shirley Ryan AbilityLab, Chicago 60611, Illinois
| | - Marc W Slutzky
- Departments of Neurology, Northwestern University, Chicago 60611, Illinois
- Physical Medicine & Rehabilitation, Northwestern University, Chicago 60611, Illinois
- Neuroscience, Northwestern University, Chicago 60611, Illinois
- Shirley Ryan AbilityLab, Chicago 60611, Illinois
- Department of Biomedical Engineering, Northwestern University, Evanston 60201, Illinois
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16
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Finley AJ, Angus DJ, Knight E, van Reekum CM, Lachman ME, Davidson RJ, Schaefer SM. Resting EEG Periodic and Aperiodic Components Predict Cognitive Decline Over 10 Years. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.17.549371. [PMID: 37503078 PMCID: PMC10370116 DOI: 10.1101/2023.07.17.549371] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Measures of intrinsic brain function at rest show promise as predictors of cognitive decline in humans, including EEG metrics such as individual alpha peak frequency (IAPF) and the aperiodic exponent, reflecting the strongest frequency of alpha oscillations and the relative balance of excitatory:inhibitory neural activity, respectively. Both IAPF and the aperiodic exponent decrease with age and have been associated with worse executive function and working memory. However, few studies have jointly examined their associations with cognitive function, and none have examined their association with longitudinal cognitive decline rather than cross-sectional impairment. In a preregistered secondary analysis of data from the longitudinal Midlife in the United States (MIDUS) study, we tested whether IAPF and aperiodic exponent measured at rest predict cognitive function (N = 235; age at EEG recording M = 55.10, SD = 10.71) over 10 years. The IAPF and the aperiodic exponent interacted to predict decline in overall cognitive ability, even after controlling for age, sex, education, and lag between data collection timepoints. Post-hoc tests showed that "mismatched" IAPF and aperiodic exponents (e.g., higher exponent with lower IAPF) predicted greater cognitive decline compared to "matching" IAPF and aperiodic exponents (e.g., higher exponent with higher IAPF; lower IAPF with lower aperiodic exponent). These effects were largely driven by measures of executive function. Our findings provide the first evidence that IAPF and the aperiodic exponent are joint predictors of cognitive decline from midlife into old age and thus may offer a useful clinical tool for predicting cognitive risk in aging.
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Affiliation(s)
| | | | - Erik Knight
- Department of Psychology and Neuroscience, University of Colorado Boulder
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17
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De Pascalis V. Brain Functional Correlates of Resting Hypnosis and Hypnotizability: A Review. Brain Sci 2024; 14:115. [PMID: 38391691 PMCID: PMC10886478 DOI: 10.3390/brainsci14020115] [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: 12/04/2023] [Revised: 01/12/2024] [Accepted: 01/18/2024] [Indexed: 02/24/2024] Open
Abstract
This comprehensive review delves into the cognitive neuroscience of hypnosis and variations in hypnotizability by examining research employing functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and electroencephalography (EEG) methods. Key focus areas include functional brain imaging correlations in hypnosis, EEG band oscillations as indicators of hypnotic states, alterations in EEG functional connectivity during hypnosis and wakefulness, drawing critical conclusions, and suggesting future research directions. The reviewed functional connectivity findings support the notion that disruptions in the available integration between different components of the executive control network during hypnosis may correspond to altered subjective appraisals of the agency during the hypnotic response, as per dissociated and cold control theories of hypnosis. A promising exploration avenue involves investigating how frontal lobes' neurochemical and aperiodic components of the EEG activity at waking-rest are linked to individual differences in hypnotizability. Future studies investigating the effects of hypnosis on brain function should prioritize examining distinctive activation patterns across various neural networks.
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Affiliation(s)
- Vilfredo De Pascalis
- Department of Psychology, La Sapienza University of Rome, 00185 Rome, Italy
- School of Psychology, University of New England, Armidale, NSW 2351, Australia
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18
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Yang Y, Chen D, Wang J, Wang J, Yan Z, Deng Q, Zhang L, Luan G, Wang M, Li T. Dynamic evolution of the anterior cingulate-insula network during seizures. CNS Neurosci Ther 2023; 29:3901-3912. [PMID: 37309272 PMCID: PMC10651990 DOI: 10.1111/cns.14310] [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: 04/22/2023] [Revised: 05/28/2023] [Accepted: 05/31/2023] [Indexed: 06/14/2023] Open
Abstract
OBJECTIVES In physiological situations, the anterior cingulate cortex (ACC) and anterior insular cortex (AIC) are prone to coactivation. The functional connectivity and interaction between ACC and AIC in the context of epilepsy remain unclear. This study aimed to investigate the dynamic coupling between these two brain regions during seizures. METHODS Patients who underwent stereoelectroencephalography (SEEG) recording were included in this study. The SEEG data were visually inspected and quantitatively analyzed. The narrowband oscillations and aperiodic components at seizure onset were parameterized. The frequency-specific non-linear correlation analysis was applied to the functional connectivity. The excitation/inhibition ratio (E:I ratio) reflected by the aperiodic slope was performed to evaluate the excitability. RESULTS Twenty patients were included in the study, with 10 diagnosed with anterior cingulate epilepsy and 10 with anterior insular epilepsy. In both types of epilepsy, the correlation coefficient (h2 ) between the ACC and AIC at seizure onset exhibited a significantly higher value than that during interictal and preictal periods (p < 0.05). The direction index (D) showed a significant increase at seizure onset, serving as an indicator for the direction of information flow between these two brain regions with up to 90% accuracy. The E:I ratio increased significantly at seizure onset, with the seizure-onset zone (SOZ) demonstrating a more pronounced increase compared to non-SOZ (p < 0.05). For seizures originating from AIC, the E:I ratio was significantly higher in the AIC than in the ACC (p = 0.0364). CONCLUSIONS In the context of epilepsy, the ACC and AIC are dynamically coupled during seizures. The functional connectivity and excitability exhibit a significant increase at seizure onset. By analyzing connectivity and excitability, the SOZ in ACC and AIC can be identified. The direction index (D) serves as an indicator for the direction of information flow from SOZ to non-SOZ. Notably, the excitability of SOZ changes more significantly than that of non-SOZ.
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Affiliation(s)
- Yujiao Yang
- Department of Neurology, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
| | - Dong Chen
- Key Laboratory of Mental HealthInstitute of Psychology, Chinese Academy of SciencesBeijingChina
| | - Jing Wang
- Department of Neurology, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
| | - Jie Wang
- Department of ElectrophysiologyCapital Institute of PediatricsBeijingChina
| | - Zhaofen Yan
- Department of Neurology, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
| | - Qinqin Deng
- Department of Neurology, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
| | - Liping Zhang
- Department of Neurology, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
| | - Guoming Luan
- Department of Functional Neurosurgery, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Epilepsy, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
- Beijing Institute for Brain Disorders, Capital Medical UniversityBeijingChina
| | - Mengyang Wang
- Department of Neurology, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
| | - Tianfu Li
- Department of Neurology, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Epilepsy, Sanbo Brain HospitalCapital Medical UniversityBeijingChina
- Beijing Institute for Brain Disorders, Capital Medical UniversityBeijingChina
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19
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Merino EC, Faes A, Van Hulle MM. The role of distinct ECoG frequency features in decoding finger movement. J Neural Eng 2023; 20:066014. [PMID: 37963397 DOI: 10.1088/1741-2552/ad0c5e] [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: 06/26/2023] [Accepted: 11/14/2023] [Indexed: 11/16/2023]
Abstract
Objective.To identify the electrocorticography (ECoG) frequency features that encode distinct finger movement states during repeated finger flexions.Approach.We used the publicly available Stanford ECoG dataset of cue-based, repeated single finger flexions. Using linear regression, we identified the spectral features that contributed most to the encoding of movement dynamics and discriminating movement events from rest, and combined them to predict finger movement trajectories. Furthermore, we also looked into the effect of the used frequency range and the spatial distribution of the identified features.Main results.Two frequency features generate superior performance, each one for a different movement aspect: high gamma band activity distinguishes movement events from rest, whereas the local motor potential (LMP) codes for movement dynamics. Combining these two features in a finger movement decoder outperformed comparable prior work where the entire spectrum was used as the average correlation coefficient with the true trajectories increased from 0.45 to 0.5, both applied to the Stanford dataset, and erroneous predictions during rest were demoted. In addition, for the first time, our results show the influence of the upper cut-off frequency used to extract LMP, yielding a higher performance when this range is adjusted to the finger movement rate.Significance.This study shows the benefit of a detailed feature analysis prior to designing the finger movement decoder.
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Affiliation(s)
- Eva Calvo Merino
- Laboratory for Neuro- and Psychophysiology, KU Leuven, Leuven, Belgium
| | - A Faes
- Laboratory for Neuro- and Psychophysiology, KU Leuven, Leuven, Belgium
| | - M M Van Hulle
- Laboratory for Neuro- and Psychophysiology, KU Leuven, Leuven, Belgium
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20
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Liu S, Wang J, Li S, Cai L. Epileptic Seizure Detection and Prediction in EEGs Using Power Spectra Density Parameterization. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3884-3894. [PMID: 37725738 DOI: 10.1109/tnsre.2023.3317093] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/21/2023]
Abstract
Power spectrum analysis is one of the effective tools for classifying epileptic signals based on electroencephalography (EEG) recordings. However, the conflation of periodic and aperiodic components within the EEG may presents an obstacle to epilepsy detection or prediction. In this paper, we explored the significance of the periodic and aperiodic components of the EEG power spectrum for the detection and prediction of epilepsy respectively. We use a power spectrum density parameterization method to separate the periodic and aperiodic components of the signals, and validate their roles in epilepsy detection and prediction on two public datasets. The average classification accuracy of the periodic and aperiodic components for 10 clinical tasks on the Bonn EEG database were 73.9% and 96.68%, respectively, and increases to 98.88% when combined. For 22 patients on the CHB-MIT Long-term EEG database, the combined features achieve an average detection accuracy of 99.95% and successfully predict all seizures with low false prediction rates. We conclude that both the periodic and aperiodic components of the EEG power spectrum contributed to discriminating different stages of epilepsy, but the aperiodic neural activity played a decisive role in classification. This discovery has significant implications for diagnosing epileptic seizures and providing personalized brain activity information to improve the accuracy and efficiency of epilepsy detection.
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21
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Olguin SL, Cavanagh JF, Young JW, Brigman JL. Impaired cognitive control after moderate prenatal alcohol exposure corresponds to altered EEG power during a rodent touchscreen continuous performance task. Neuropharmacology 2023; 236:109599. [PMID: 37217074 PMCID: PMC10330662 DOI: 10.1016/j.neuropharm.2023.109599] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/12/2023] [Accepted: 05/14/2023] [Indexed: 05/24/2023]
Abstract
Although it is well established that alcohol consumption during pregnancy can lead to lifelong difficulties in offspring, Fetal Alcohol Spectrum Disorders (FASD) remain a common neurodevelopmental syndrome. Translational behavioral tools that target similar brain circuits across species can facilitate understanding of these cognitive consequences. Touchscreen behavioral tasks for rodents enable easy integration of dura recordings of electroencephalographic (EEG) activity in awake behaving animals, with clear translational generalizability. Recently, we showed that Prenatal Alcohol Exposure (PAE) impairs cognitive control on the touchscreen 5-Choice Continuous Performance Task (5C-CPT) which requires animals to touch on target trials (hit) and withhold responding on non-target trials (correct rejection). Here, we extended these findings to determine whether dura EEG recordings would detect task-relevant differences in medial prefrontal cortex (mPFC) and posterior parietal cortex (PPC) corresponding with behavioral alterations in PAE animals. Replicating previous findings, PAE mice made more false alarm responses versus controls and had a significantly lower sensitivity index. All mice, regardless of sex or treatment, demonstrated increased frontal theta-band power during correct trials that followed an error (similar to post-error monitoring commonly seen in human participants). All mice showed a significant decrease in parietal beta-band power when performing a correct rejection versus a hit. PAE mice of both sexes showed a significantly larger decrease in parietal beta-band power when successfully rejecting non-target stimuli. These findings suggest that moderate exposure to alcohol during development can have long lasting effects on cognitive control, and task-relevant neural signals may provide a biomarker of impaired function across species.
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Affiliation(s)
- Sarah L Olguin
- Department of Neurosciences, University of New Mexico School of Medicine, Albuquerque, NM, 87131, USA; New Mexico Alcohol Research Center, UNM Health Sciences Center, Albuquerque, NM, USA
| | - James F Cavanagh
- Department of Psychology, University of New Mexico, Albuquerque, NM, USA
| | - Jared W Young
- Department of Psychiatry, School of Medicine, University of California San Diego, La Jolla, CA, USA; Research Service, VA San Diego Healthcare System, San Diego, CA, USA
| | - Jonathan L Brigman
- Department of Neurosciences, University of New Mexico School of Medicine, Albuquerque, NM, 87131, USA; New Mexico Alcohol Research Center, UNM Health Sciences Center, Albuquerque, NM, USA.
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22
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Kim B, Erickson BA, Fernandez-Nunez G, Rich R, Mentzelopoulos G, Vitale F, Medaglia JD. EEG Phase Can Be Predicted with Similar Accuracy across Cognitive States after Accounting for Power and Signal-to-Noise Ratio. eNeuro 2023; 10:ENEURO.0050-23.2023. [PMID: 37558464 PMCID: PMC10481640 DOI: 10.1523/eneuro.0050-23.2023] [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: 02/13/2023] [Revised: 05/25/2023] [Accepted: 06/15/2023] [Indexed: 08/11/2023] Open
Abstract
EEG phase is increasingly used in cognitive neuroscience, brain-computer interfaces, and closed-loop stimulation devices. However, it is unknown how accurate EEG phase prediction is across cognitive states. We determined the EEG phase prediction accuracy of parieto-occipital alpha waves across rest and task states in 484 participants over 11 public datasets. We were able to track EEG phase accurately across various cognitive conditions and datasets, especially during periods of high instantaneous alpha power and signal-to-noise ratio (SNR). Although resting states generally have higher accuracies than task states, absolute accuracy differences were small, with most of these differences attributable to EEG power and SNR. These results suggest that experiments and technologies using EEG phase should focus more on minimizing external noise and waiting for periods of high power rather than inducing a particular cognitive state.
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Affiliation(s)
- Brian Kim
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania 19104
| | - Brian A Erickson
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania 19104
| | | | - Ryan Rich
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania 19104
| | - Georgios Mentzelopoulos
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania 19104
| | - Flavia Vitale
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania 19104
- Departments of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Physical Medicine and Rehabilitation, University of Pennsylvania, Philadelphia, Pennsylvania 19146
| | - John D Medaglia
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania 19104
- Departments of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Neurology, Drexel University, Philadelphia, Pennsylvania 19104
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23
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Cadwallader CJ, Steiniger J, Cooper PS, Zhou SH, Hendrikse J, Sumner RL, Kirk IJ, Chong TTJ, Coxon JP. Acute exercise as a modifier of neocortical plasticity and aperiodic activity in the visual cortex. Sci Rep 2023; 13:7491. [PMID: 37161049 PMCID: PMC10169840 DOI: 10.1038/s41598-023-34749-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 05/06/2023] [Indexed: 05/11/2023] Open
Abstract
Long-term potentiation (LTP) is a form of neuroplasticity commonly implicated in mechanistic models of learning and memory. Acute exercise can boost LTP in the motor cortex, and is associated with a shift in excitation/inhibition (E:I) balance, but whether this extends to other regions such as the visual cortex is unknown. We investigated the effect of a preceding bout of exercise on LTP induction and the E:I balance in the visual cortex using electroencephalography (EEG). Young adults (N = 20, mean age = 24.20) engaged in 20 min of high-intensity interval training (HIIT) exercise and rest across two counterbalanced sessions. LTP was induced using a high frequency presentation of a visual stimulus; a "visual tetanus". Established EEG markers of visual LTP, the N1b and P2 component of the visual evoked potential, and an EEG-derived measure of the E:I balance, the aperiodic exponent, were measured before and after the visual tetanus. As expected, there was a potentiation of the N1b following the visual tetanus, with specificity to the tetanised stimulus, and a non-specific potentiation of the P2. These effects were not sensitive to a preceding bout of exercise. However, the E:I balance showed a late shift towards inhibition following the visual tetanus. A preceding bout of exercise resulted in specificity of this E:I balance shift to the tetanised stimulus, that was not seen following rest. This novel finding suggests a possible exercise-induced tuning of the visual cortex to stimulus details following LTP induction.
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Affiliation(s)
- Claire J Cadwallader
- School of Psychological Sciences, The Turner Institute for Brain and Mental Health, Monash University, Victoria, 3800, Australia.
| | - Jennifer Steiniger
- School of Psychological Sciences, The Turner Institute for Brain and Mental Health, Monash University, Victoria, 3800, Australia
| | - Patrick S Cooper
- School of Psychological Sciences, The Turner Institute for Brain and Mental Health, Monash University, Victoria, 3800, Australia
| | - Shou-Han Zhou
- School of Psychology, James Cook University, Townsville, QLD, 4810, Australia
| | - Joshua Hendrikse
- School of Psychological Sciences, The Turner Institute for Brain and Mental Health, Monash University, Victoria, 3800, Australia
| | - Rachael L Sumner
- School of Pharmacy, The University of Auckland, Auckland, New Zealand
| | - Ian J Kirk
- School of Psychology, The University of Auckland, Auckland, New Zealand
| | - Trevor T-J Chong
- School of Psychological Sciences, The Turner Institute for Brain and Mental Health, Monash University, Victoria, 3800, Australia
- Department of Neurology, Alfred Health, Melbourne, VIC, 3004, Australia
- Department of Clinical Neurosciences, St Vincent's Hospital, Melbourne, VIC, 3065, Australia
| | - James P Coxon
- School of Psychological Sciences, The Turner Institute for Brain and Mental Health, Monash University, Victoria, 3800, Australia.
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24
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Nie JZ, Flint RD, Prakash P, Hsieh JK, Mugler EM, Tate MC, Rosenow JM, Slutzky MW. High-gamma activity is coupled to low-gamma oscillations in precentral cortices and modulates with movement and speech. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.13.528325. [PMID: 36824850 PMCID: PMC9949043 DOI: 10.1101/2023.02.13.528325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
Planning and executing motor behaviors requires coordinated neural activity among multiple cortical and subcortical regions of the brain. Phase-amplitude coupling between the high-gamma band amplitude and the phase of low frequency oscillations (theta, alpha, beta) has been proposed to reflect neural communication, as has synchronization of low-gamma oscillations. However, coupling between low-gamma and high-gamma bands has not been investigated. Here, we measured phase-amplitude coupling between low- and high-gamma in monkeys performing a reaching task and in humans either performing finger movements or speaking words aloud. We found significant coupling between low-gamma phase and high-gamma amplitude in multiple sensorimotor and premotor cortices of both species during all tasks. This coupling modulated with the onset of movement. These findings suggest that interactions between the low and high gamma bands are markers of network dynamics related to movement and speech generation.
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25
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Dziego CA, Bornkessel-Schlesewsky I, Jano S, Chatburn A, Schlesewsky M, Immink MA, Sinha R, Irons J, Schmitt M, Chen S, Cross ZR. Neural and cognitive correlates of performance in dynamic multi-modal settings. Neuropsychologia 2023; 180:108483. [PMID: 36638860 DOI: 10.1016/j.neuropsychologia.2023.108483] [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: 05/10/2022] [Revised: 11/28/2022] [Accepted: 01/09/2023] [Indexed: 01/12/2023]
Abstract
The endeavour to understand human cognition has largely relied upon investigation of task-related brain activity. However, resting-state brain activity can also offer insights into individual information processing and performance capabilities. Previous research has identified electroencephalographic resting-state characteristics (most prominently: the individual alpha frequency; IAF) that predict cognitive function. However, it has largely overlooked a second component of electrophysiological signals: aperiodic 1/ƒ activity. The current study examined how both oscillatory and aperiodic resting-state EEG measures, alongside traditional cognitive tests, can predict performance in a dynamic and complex, semi-naturalistic cognitive task. Participants' resting-state EEG was recorded prior to engaging in a Target Motion Analysis (TMA) task in a simulated submarine control room environment (CRUSE), which required participants to integrate dynamically changing information over time. We demonstrated that the relationship between IAF and cognitive performance extends from simple cognitive tasks (e.g., digit span) to complex, dynamic measures of information processing. Further, our results showed that individual 1/ƒ parameters (slope and intercept) differentially predicted performance across practice and testing sessions, whereby flatter slopes and higher intercepts were associated with improved performance during learning. In addition to the EEG predictors, we demonstrate a link between cognitive skills most closely related to the TMA task (i.e., spatial imagery) and subsequent performance. Overall, the current study highlights (1) how resting-state metrics - both oscillatory and aperiodic - have the potential to index higher-order cognitive capacity, while (2) emphasising the importance of examining these electrophysiological components within more dynamic settings and over time.
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Affiliation(s)
- Chloe A Dziego
- Cognitive Neuroscience Laboratory - Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, South Australia, Australia.
| | - Ina Bornkessel-Schlesewsky
- Cognitive Neuroscience Laboratory - Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, South Australia, Australia
| | - Sophie Jano
- Cognitive Neuroscience Laboratory - Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, South Australia, Australia
| | - Alex Chatburn
- Cognitive Neuroscience Laboratory - Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, South Australia, Australia
| | - Matthias Schlesewsky
- Cognitive Neuroscience Laboratory - Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, South Australia, Australia
| | - Maarten A Immink
- Cognitive Neuroscience Laboratory - Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, South Australia, Australia; Sport, Health, Activity, Performance and Exercise (SHAPE) Research Centre, Flinders University, South Australia, Australia
| | - Ruchi Sinha
- Centre for Workplace Excellence, University of South Australia, 61-68 North Terrace, Adelaide, South Australia, Australia
| | - Jessica Irons
- Undersea Command & Control Maritime Division, Defence Science and Technology Group, Australia
| | - Megan Schmitt
- Undersea Command & Control Maritime Division, Defence Science and Technology Group, Australia
| | - Steph Chen
- Human and Decision Sciences Division, Defence Science and Technology Group, Australia
| | - Zachariah R Cross
- Cognitive Neuroscience Laboratory - Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, South Australia, Australia
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26
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West TO, Duchet B, Farmer SF, Friston KJ, Cagnan H. When do bursts matter in the primary motor cortex? Investigating changes in the intermittencies of beta rhythms associated with movement states. Prog Neurobiol 2023; 221:102397. [PMID: 36565984 PMCID: PMC7614511 DOI: 10.1016/j.pneurobio.2022.102397] [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: 07/29/2022] [Revised: 11/04/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
Brain activity exhibits significant temporal structure that is not well captured in the power spectrum. Recently, attention has shifted to characterising the properties of intermittencies in rhythmic neural activity (i.e. bursts), yet the mechanisms that regulate them are unknown. Here, we present evidence from electrocorticography recordings made over the motor cortex to show that the statistics of bursts, such as duration or amplitude, in the beta frequency (14-30 Hz) band, significantly aid the classification of motor states such as rest, movement preparation, execution, and imagery. These features reflect nonlinearities not detectable in the power spectrum, with states increasing in nonlinearity from movement execution to preparation to rest. Further, we show using a computational model of the cortical microcircuit, constrained to account for burst features, that modulations of laminar specific inhibitory interneurons are responsible for the temporal organisation of activity. Finally, we show that the temporal characteristics of spontaneous activity can be used to infer the balance of cortical integration between incoming sensory information and endogenous activity. Critically, we contribute to the understanding of how transient brain rhythms may underwrite cortical processing, which in turn, could inform novel approaches for brain state classification, and modulation with novel brain-computer interfaces.
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Affiliation(s)
- Timothy O West
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK; Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK.
| | - Benoit Duchet
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK
| | - Simon F Farmer
- Department of Neurology, National Hospital for Neurology & Neurosurgery, Queen Square, London WC1N 3BG, UK; Department of Clinical and Movement Neurosciences, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Hayriye Cagnan
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK; Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
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27
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Li J, Qi Y, Pan G. Phase-amplitude coupling-based adaptive filters for neural signal decoding. Front Neurosci 2023; 17:1153568. [PMID: 37205052 PMCID: PMC10185763 DOI: 10.3389/fnins.2023.1153568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 04/06/2023] [Indexed: 05/21/2023] Open
Abstract
Bandpass filters play a core role in ECoG signal processing. Commonly used frequency bands such as alpha, beta, and gamma bands can reflect the normal rhythm of the brain. However, the universally predefined bands might not be optimal for a specific task. Especially the gamma band usually covers a wide frequency span (i.e., 30-200 Hz) which can be too coarse to capture features that appear in narrow bands. An ideal option is to find the optimal frequency bands for specific tasks in real-time and dynamically. To tackle this problem, we propose an adaptive band filter that selects the useful frequency band in a data-driven way. Specifically, we leverage the phase-amplitude coupling (PAC) of the coupled working mechanism of synchronizing neuron and pyramidal neurons in neuronal oscillations, in which the phase of slower oscillations modulates the amplitude of faster ones, to help locate the fine frequency bands from the gamma range, in a task-specific and individual-specific way. Thus, the information can be more precisely extracted from ECoG signals to improve neural decoding performance. Based on this, an end-to-end decoder (PACNet) is proposed to construct a neural decoding application with adaptive filter banks in a uniform framework. Experiments show that PACNet can improve neural decoding performance universally with different tasks.
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Affiliation(s)
- Jiajun Li
- State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Yu Qi
- State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China
- Affiliated Mental Health Center and Hangzhou Seventh Peoples Hospital, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Yu Qi
| | - Gang Pan
- State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
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28
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Weiss SA, Sheybani L, Seenarine N, Fried I, Wu C, Sharan A, Engel J, Sperling MR, Nir Y, Staba RJ. Delta oscillation coupled propagating fast ripples precede epileptiform discharges in patients with focal epilepsy. Neurobiol Dis 2022; 175:105928. [DOI: 10.1016/j.nbd.2022.105928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/13/2022] [Accepted: 11/15/2022] [Indexed: 11/18/2022] Open
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Seymour RA, Alexander N, Maguire EA. Robust estimation of 1/f activity improves oscillatory burst detection. Eur J Neurosci 2022; 56:5836-5852. [PMID: 36161675 PMCID: PMC9828710 DOI: 10.1111/ejn.15829] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 09/13/2022] [Indexed: 02/06/2023]
Abstract
Neural oscillations often occur as transient bursts with variable amplitude and frequency dynamics. Quantifying these effects is important for understanding brain-behaviour relationships, especially in continuous datasets. To robustly measure bursts, rhythmical periods of oscillatory activity must be separated from arrhythmical background 1/f activity, which is ubiquitous in electrophysiological recordings. The Better OSCillation (BOSC) framework achieves this by defining a power threshold above the estimated background 1/f activity, combined with a duration threshold. Here we introduce a modification to this approach called fBOSC, which uses a spectral parametrisation tool to accurately model background 1/f activity in neural data. fBOSC (which is openly available as a MATLAB toolbox) is robust to power spectra with oscillatory peaks and can also model non-linear spectra. Through a series of simulations, we show that fBOSC more accurately models the 1/f power spectrum compared with existing methods. fBOSC was especially beneficial where power spectra contained a 'knee' below ~.5-10 Hz, which is typical in neural data. We also found that, unlike other methods, fBOSC was unaffected by oscillatory peaks in the neural power spectrum. Moreover, by robustly modelling background 1/f activity, the sensitivity for detecting oscillatory bursts was standardised across frequencies (e.g., theta- and alpha-bands). Finally, using openly available resting state magnetoencephalography and intracranial electrophysiology datasets, we demonstrate the application of fBOSC for oscillatory burst detection in the theta-band. These simulations and empirical analyses highlight the value of fBOSC in detecting oscillatory bursts, including in datasets that are long and continuous with no distinct experimental trials.
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Affiliation(s)
- Robert A. Seymour
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Nicholas Alexander
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Eleanor A. Maguire
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
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30
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Finley AJ, Angus DJ, van Reekum CM, Davidson RJ, Schaefer SM. Periodic and aperiodic contributions to theta-beta ratios across adulthood. Psychophysiology 2022; 59:e14113. [PMID: 35751645 PMCID: PMC9532351 DOI: 10.1111/psyp.14113] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 04/15/2022] [Accepted: 05/04/2022] [Indexed: 12/31/2022]
Abstract
The ratio of fronto-central theta (4-7 Hz) to beta oscillations (13-30 Hz), known as the theta-beta ratio, is negatively correlated with attentional control, reinforcement learning, executive function, and age. Although theta-beta ratios have been found to decrease with age in adolescents and young adults, theta has been found to increase with age in older adults. Moreover, age-related decrease in individual alpha peak frequency and flattening of the 1/f aperiodic component may artifactually inflate the association between theta-beta ratio and age. These factors lead to an incomplete understanding of how theta-beta ratio varies across the lifespan and the extent to which variation is due to a conflation of aperiodic and periodic activity. We conducted a partially preregistered analysis examining the cross-sectional associations between age and resting canonical fronto-central theta-beta ratio, individual alpha peak frequency, and aperiodic component (n = 268; age 36-84, M = 55.8, SD = 11.0). Age was negatively associated with theta-beta ratios, individual peak alpha frequencies, and the aperiodic exponent. The correlation between theta-beta ratios and age remained after controlling for individual peak alpha frequencies, but was nonsignificant when controlling for the aperiodic exponent. Aperiodic exponent fully mediated the relationship between theta-beta ratio and age, although beta remained significantly associated with age after controlling for theta, individual peak alpha, and aperiodic exponent. Results replicate previous observations and show age-related decreases in theta-beta ratios are not due to age-related decrease in individual peak alpha frequencies but primarily explained by flattening of the aperiodic component with age.
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Affiliation(s)
- Anna J. Finley
- Center for Healthy MindsUniversity of WisconsinMadisonWisconsinUSA
| | - Douglas J. Angus
- School of PsychologyBond UniversityGold CoastQueenslandAustralia
| | - Carien M. van Reekum
- School of Psychology and Clinical Language SciencesUniversity of ReadingReadingUK
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31
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Lee HS, Schreiner L, Jo SH, Sieghartsleitner S, Jordan M, Pretl H, Guger C, Park HS. Individual finger movement decoding using a novel ultra-high-density electroencephalography-based brain-computer interface system. Front Neurosci 2022; 16:1009878. [PMID: 36340769 PMCID: PMC9627315 DOI: 10.3389/fnins.2022.1009878] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 10/04/2022] [Indexed: 11/13/2022] Open
Abstract
Brain-Computer Interface (BCI) technology enables users to operate external devices without physical movement. Electroencephalography (EEG) based BCI systems are being actively studied due to their high temporal resolution, convenient usage, and portability. However, fewer studies have been conducted to investigate the impact of high spatial resolution of EEG on decoding precise body motions, such as finger movements, which are essential in activities of daily living. Low spatial sensor resolution, as found in common EEG systems, can be improved by omitting the conventional standard of EEG electrode distribution (the international 10-20 system) and ordinary mounting structures (e.g., flexible caps). In this study, we used newly proposed flexible electrode grids attached directly to the scalp, which provided ultra-high-density EEG (uHD EEG). We explored the performance of the novel system by decoding individual finger movements using a total of 256 channels distributed over the contralateral sensorimotor cortex. Dense distribution and small-sized electrodes result in an inter-electrode distance of 8.6 mm (uHD EEG), while that of conventional EEG is 60 to 65 mm on average. Five healthy subjects participated in the experiment, performed single finger extensions according to a visual cue, and received avatar feedback. This study exploits mu (8-12 Hz) and beta (13-25 Hz) band power features for classification and topography plots. 3D ERD/S activation plots for each frequency band were generated using the MNI-152 template head. A linear support vector machine (SVM) was used for pairwise finger classification. The topography plots showed regular and focal post-cue activation, especially in subjects with optimal signal quality. The average classification accuracy over subjects was 64.8 (6.3)%, with the middle versus ring finger resulting in the highest average accuracy of 70.6 (9.4)%. Further studies are required using the uHD EEG system with real-time feedback and motor imagery tasks to enhance classification performance and establish the basis for BCI finger movement control of external devices.
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Affiliation(s)
- Hyemin S. Lee
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
| | - Leonhard Schreiner
- g.tec Medical Engineering GmbH, Schiedlberg, Upper Austria, Austria
- Institute for Integrated Circuits, Johannes Kepler University, Linz, Austria
| | - Seong-Hyeon Jo
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
| | | | - Michael Jordan
- g.tec Medical Engineering GmbH, Schiedlberg, Upper Austria, Austria
| | - Harald Pretl
- Institute for Integrated Circuits, Johannes Kepler University, Linz, Austria
| | - Christoph Guger
- g.tec Medical Engineering GmbH, Schiedlberg, Upper Austria, Austria
| | - Hyung-Soon Park
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
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32
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Shin U, Ding C, Zhu B, Vyza Y, Trouillet A, Revol ECM, Lacour SP, Shoaran M. NeuralTree: A 256-Channel 0.227-μJ/Class Versatile Neural Activity Classification and Closed-Loop Neuromodulation SoC. IEEE JOURNAL OF SOLID-STATE CIRCUITS 2022; 57:3243-3257. [PMID: 36744006 PMCID: PMC9897226 DOI: 10.1109/jssc.2022.3204508] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Closed-loop neural interfaces with on-chip machine learning can detect and suppress disease symptoms in neurological disorders or restore lost functions in paralyzed patients. While high-density neural recording can provide rich neural activity information for accurate disease-state detection, existing systems have low channel counts and poor scalability, which could limit their therapeutic efficacy. This work presents a highly scalable and versatile closed-loop neural interface SoC that can overcome these limitations. A 256-channel time-division multiplexed (TDM) front-end with a two-step fast-settling mixed-signal DC servo loop (DSL) is proposed to record high-spatial-resolution neural activity and perform channel-selective brain-state inference. A tree-structured neural network (NeuralTree) classification processor extracts a rich set of neural biomarkers in a patient- and disease-specific manner. Trained with an energy-aware learning algorithm, the NeuralTree classifier detects the symptoms of underlying disorders (e.g., epilepsy and movement disorders) at an optimal energy-accuracy trade-off. A 16-channel high-voltage (HV) compliant neurostimulator closes the therapeutic loop by delivering charge-balanced biphasic current pulses to the brain. The proposed SoC was fabricated in 65nm CMOS and achieved a 0.227μJ/class energy efficiency in a compact area of 0.014mm2/channel. The SoC was extensively verified on human electroencephalography (EEG) and intracranial EEG (iEEG) epilepsy datasets, obtaining 95.6%/94% sensitivity and 96.8%/96.9% specificity, respectively. In-vivo neural recordings using soft μECoG arrays and multi-domain biomarker extraction were further performed on a rat model of epilepsy. In addition, for the first time in literature, on-chip classification of rest-state tremor in Parkinson's disease (PD) from human local field potentials (LFPs) was demonstrated.
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Affiliation(s)
- Uisub Shin
- Institute of Electrical and Micro Engineering, EPFL, 1202 Geneva, Switzerland, and the School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Cong Ding
- Institute of Electrical and Micro Engineering and Center for Neuroprosthetics, EPFL, 1202 Geneva, Switzerland
| | - Bingzhao Zhu
- Institute of Electrical and Micro Engineering, EPFL, 1202 Geneva, Switzerland, and the School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14853, USA
| | - Yashwanth Vyza
- Institute of Electrical and Micro Engineering and Center for Neuroprosthetics, EPFL, 1202 Geneva, Switzerland
| | - Alix Trouillet
- Institute of Electrical and Micro Engineering and Center for Neuroprosthetics, EPFL, 1202 Geneva, Switzerland
| | - Emilie C M Revol
- Institute of Electrical and Micro Engineering and Center for Neuroprosthetics, EPFL, 1202 Geneva, Switzerland
| | - Stéphanie P Lacour
- Institute of Electrical and Micro Engineering and Center for Neuroprosthetics, EPFL, 1202 Geneva, Switzerland
| | - Mahsa Shoaran
- Institute of Electrical and Micro Engineering and Center for Neuroprosthetics, EPFL, 1202 Geneva, Switzerland
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33
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The phase of sensorimotor mu and beta oscillations has the opposite effect on corticospinal excitability. Brain Stimul 2022; 15:1093-1100. [PMID: 35964870 DOI: 10.1016/j.brs.2022.08.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 08/05/2022] [Accepted: 08/06/2022] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Neural oscillations in the primary motor cortex (M1) shape corticospinal excitability. Power and phase of ongoing mu (8-13 Hz) and beta (14-30 Hz) activity may mediate motor cortical output. However, the functional dynamics of both mu and beta phase and power relationships and their interaction, are largely unknown. OBJECTIVE Here, we employ recently developed real-time targeting of the mu and beta rhythm, to apply phase-specific brain stimulation and probe motor corticospinal excitability non-invasively. For this, we used instantaneous read-out and analysis of ongoing oscillations, targeting four different phases (0°, 90°, 180°, and 270°) of mu and beta rhythms with suprathreshold single-pulse transcranial magnetic stimulation (TMS) to M1. Ensuing motor evoked potentials (MEPs) in the right first dorsal interossei muscle were recorded. Twenty healthy adults took part in this double-blind randomized crossover study. RESULTS Mixed model regression analyses showed significant phase-dependent modulation of corticospinal output by both mu and beta rhythm. Strikingly, these modulations exhibit a double dissociation. MEPs are larger at the mu trough and rising phase and smaller at the peak and falling phase. For the beta rhythm we found the opposite behavior. Also, mu power, but not beta power, was positively correlated with corticospinal output. Power and phase effects did not interact for either rhythm, suggesting independence between these aspects of oscillations. CONCLUSION Our results provide insights into real-time motor cortical oscillation dynamics, which offers the opportunity to improve the effectiveness of TMS by specifically targeting different frequency bands.
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Peterson SM, Rao RPN, Brunton BW. Learning neural decoders without labels using multiple data streams. J Neural Eng 2022; 19. [PMID: 35905727 DOI: 10.1088/1741-2552/ac857c] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Accepted: 07/29/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Recent advances in neural decoding have accelerated the development of brain-computer interfaces aimed at assisting users with everyday tasks such as speaking, walking, and manipulating objects. However, current approaches for training neural decoders commonly require large quantities of labeled data, which can be laborious or infeasible to obtain in real-world settings. Alternatively, self-supervised models that share self-generated pseudo-labels between two data streams have shown exceptional performance on unlabeled audio and video data, but it remains unclear how well they extend to neural decoding. APPROACH We learn neural decoders without labels by leveraging multiple simultaneously recorded data streams, including neural, kinematic, and physiological signals. Specifically, we apply cross-modal, self-supervised deep clustering to train decoders that can classify movements from brain recordings. After training, we then isolate the decoders for each input data stream and compare the accuracy of decoders trained using cross-modal deep clustering against supervised and unimodal, self-supervised models. MAIN RESULTS We find that sharing pseudo-labels between two data streams during training substantially increases decoding performance compared to unimodal, self-supervised models, with accuracies approaching those of supervised decoders trained on labeled data. Next, we extend cross-modal decoder training to three or more modalities, achieving state-of-the-art neural decoding accuracy that matches or slightly exceeds the performance of supervised models. Significance: We demonstrate that cross-modal, self-supervised decoding can be applied to train neural decoders when few or no labels are available and extend the cross-modal framework to share information among three or more data streams, further improving self-supervised training.
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Affiliation(s)
- Steven M Peterson
- Biology, University of Washington, 4000 15th Ave NE, Seattle, Washington, 98195, UNITED STATES
| | - Rajesh P N Rao
- Department of Computer Science and Engineering College of Engineering, University of Washington, Box 352350, Seattle, Washington, 98195, UNITED STATES
| | - Bingni W Brunton
- University of Washington, 4000 15th Ave NE, Seattle, Washington, 98195, UNITED STATES
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Hussain SJ, Vollmer MK, Iturrate I, Quentin R. Voluntary Motor Command Release Coincides with Restricted Sensorimotor Beta Rhythm Phases. J Neurosci 2022; 42:5771-5781. [PMID: 35701160 PMCID: PMC9302459 DOI: 10.1523/jneurosci.1495-21.2022] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 05/09/2022] [Accepted: 05/11/2022] [Indexed: 01/22/2023] Open
Abstract
Sensory perception and memory are enhanced during restricted phases of ongoing brain rhythms, but whether voluntary movement is constrained by brain rhythm phase is not known. Voluntary movement requires motor commands to be released from motor cortex (M1) and transmitted to spinal motoneurons and effector muscles. Here, we tested the hypothesis that motor commands are preferentially released from M1 during circumscribed phases of ongoing sensorimotor rhythms. Healthy humans of both sexes performed a self-paced finger movement task during electroencephalography (EEG) and electromyography (EMG) recordings. We first estimated the time of motor command release preceding each finger movement by subtracting individually measured corticomuscular transmission latencies from EMG-determined movement onset times. Then, we determined the phase of ipsilateral and contralateral sensorimotor mu (8-12 Hz) and beta (13-35 Hz) rhythms during release of each motor command. We report that motor commands were most often released between 120 and 140° along the contralateral beta cycle but were released uniformly along the contralateral mu cycle. Motor commands were also released uniformly along ipsilateral mu and beta cycles. Results demonstrate that motor command release coincides with restricted phases of the contralateral sensorimotor beta rhythm, suggesting that sensorimotor beta rhythm phase may sculpt the timing of voluntary human movement.SIGNIFICANCE STATEMENT Perceptual and cognitive function is optimal during specific brain rhythm phases. Although brain rhythm phase influences motor cortical neuronal activity and communication between the motor cortex and spinal cord, its role in voluntary movement is poorly understood. Here, we show that the motor commands needed to produce voluntary movements are preferentially released from the motor cortex during contralateral sensorimotor beta rhythm phases. Our findings are consistent with the notion that sensorimotor rhythm phase influences the timing of voluntary human movement.
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Affiliation(s)
- Sara J Hussain
- Movement and Cognitive Rehabilitation Science Program, Department of Kinesiology and Health Education, University of Texas at Austin, Austin, Texas 78712
- Human Cortical Physiology and Neurorehabilitation Section, National Institute of Neurological Disorders and Stroke, Bethesda, Maryland 20892
| | - Mary K Vollmer
- Human Cortical Physiology and Neurorehabilitation Section, National Institute of Neurological Disorders and Stroke, Bethesda, Maryland 20892
| | - Iñaki Iturrate
- Human Cortical Physiology and Neurorehabilitation Section, National Institute of Neurological Disorders and Stroke, Bethesda, Maryland 20892
- Amazon EU, Spain
| | - Romain Quentin
- Human Cortical Physiology and Neurorehabilitation Section, National Institute of Neurological Disorders and Stroke, Bethesda, Maryland 20892
- MEL Group, EDUWELL Team, Lyon Neuroscience Research Center, Institut National de la Santé et de la Recherche Médicale U1028, Centre National de la Recherche Scientifique UMR5292, Université Claude Bernard Lyon 1, 69500 Bron, France
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Wang Z, Mo Y, Sun Y, Hu K, Peng C, Zhang S, Xue S. Separating the aperiodic and periodic components of neural activity in Parkinson's disease. Eur J Neurosci 2022; 56:4889-4900. [PMID: 35848719 DOI: 10.1111/ejn.15774] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 06/23/2022] [Accepted: 07/11/2022] [Indexed: 11/27/2022]
Abstract
Most studies on electrophysiology have not separated aperiodic activity from the spectra but have rather evaluated a combined periodic oscillatory component and the aperiodic component. As the understanding of aperiodic activity gradually deepens, its potential physiological significance has acquired increased appreciation. Herein, we investigated the two components in scalp electroencephalogram in 16 healthy controls and 15 patients with Parkinson's disease (PD); the results revealed that aperiodic parameters were approximately symmetrically distributed in topography in patients with PD and were significantly modulated by dopaminergic medication in channels C4, C3, CP5 and FC5. In sum, our findings might provide indicators for evaluating treatment response in PD and highlight the importance of re-evaluating the neuronal power spectra parameterization.
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Affiliation(s)
- Zhuyong Wang
- Neurosurgery Center, Department of Functional Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yixiang Mo
- Neurosurgery Center, Department of Functional Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yujia Sun
- Neurosurgery Center, Department of Functional Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Kai Hu
- Neurosurgery Center, Department of Functional Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Chunkai Peng
- Neurosurgery Center, Department of Functional Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Shizhong Zhang
- Neurosurgery Center, Department of Functional Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Sha Xue
- Neurosurgery Center, Department of Functional Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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Zrenner C, Belardinelli P, Ermolova M, Gordon PC, Stenroos M, Zrenner B, Ziemann U. µ-rhythm phase from somatosensory but not motor cortex correlates with corticospinal excitability in EEG-triggered TMS. J Neurosci Methods 2022; 379:109662. [PMID: 35803405 DOI: 10.1016/j.jneumeth.2022.109662] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 05/18/2022] [Accepted: 07/04/2022] [Indexed: 10/17/2022]
Abstract
BACKGROUND Sensorimotor µ-rhythm phase is correlated with corticospinal excitability. Transcranial magnetic stimulation (TMS) of motor cortex results in larger motor evoked potentials (MEPs) during the negative peak of the EEG oscillation as extracted with a surface Laplacian. However, the anatomical source of the relevant oscillation is not clear and demonstration of the relationship is sensitive to the choice of EEG montage. OBJECTIVE/HYPOTHESIS Here, we compared two EEG montages preferentially sensitive to oscillations originating from the crown of precentral gyrus (dorsal premotor cortex) vs. postcentral gyrus (secondary somatosensory cortex). We hypothesized that the EEG signal from precentral gyrus would correlate more strongly with MEP amplitude, given that the corticospinal neurons are located in the anterior wall of the sulcus and the corticospinal tract has input from premotor cortex. NEW METHOD Real-time EEG-triggered TMS of motor cortex was applied in 6 different conditions in randomly interleaved order, 3 phase conditions (positive peak, negative peak, random phase of the ongoing µ-oscillation), and each phase condition for 2 different EEG montages corresponding to oscillations preferentially originating in precentral gyrus (premotor cortex) vs. postcentral gyrus (somatosensory cortex), extracted using FCC3h vs. C3 centered EEG montages. RESULTS The negative vs. positive peak of sensorimotor µ-rhythm as extracted from the C3 montage (postcentral gyrus, somatosensory cortex) correlated with states of high vs. low corticospinal excitability (p < 0.001), replicating previous findings. However, no significant correlation was found for sensorimotor µ-rhythm as extracted from the neighboring FCC3 montage (precentral gyrus, premotor cortex). This implies that EEG-signals from the somatosensory cortex are better predictors of corticospinal excitability than EEG-signals from the motor areas. CONCLUSIONS The extraction of a brain oscillation whose phase corresponds to corticospinal excitability is highly sensitive to the selected EEG montage and the location of the EEG sensors on the scalp. Here, the cortical source of EEG oscillations predicting response amplitude does not correspond to the cortical target of the stimulation, indicating that even in this simple case, a specific neuronal pathway from somatosensory cortex to primary motor cortex is involved.
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Affiliation(s)
- Christoph Zrenner
- Department of Neurology & Stroke, University of Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen, Germany; Department of Psychiatry, University of Toronto, Toronto, Canada; Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Canada; Institute for Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Paolo Belardinelli
- Department of Neurology & Stroke, University of Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen, Germany; Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, Italy
| | - Maria Ermolova
- Department of Neurology & Stroke, University of Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen, Germany
| | - Pedro Caldana Gordon
- Department of Neurology & Stroke, University of Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen, Germany
| | - Matti Stenroos
- Department of Neuroscience and Biomedical Engineering, Aalto University, Finland
| | - Brigitte Zrenner
- Department of Neurology & Stroke, University of Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen, Germany; Department of Psychiatry, University of Toronto, Toronto, Canada; Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Canada
| | - Ulf Ziemann
- Department of Neurology & Stroke, University of Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen, Germany.
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Echeverria-Altuna I, Quinn AJ, Zokaei N, Woolrich MW, Nobre AC, van Ede F. Transient beta activity and cortico-muscular connectivity during sustained motor behaviour. Prog Neurobiol 2022; 214:102281. [PMID: 35550908 PMCID: PMC9742854 DOI: 10.1016/j.pneurobio.2022.102281] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 03/13/2022] [Accepted: 05/02/2022] [Indexed: 12/15/2022]
Abstract
Neural oscillations are thought to play a central role in orchestrating activity states between distant neural populations. For example, during isometric contraction, 13-30 Hz beta activity becomes phase coupled between the motor cortex and the contralateral muscle. This and related observations have led to the proposal that beta activity and connectivity sustain stable cognitive and motor states - or the 'status quo' - in the brain. Recently, however, beta activity at the single-trial level has been shown to be short-lived - though so far this has been reported for regional beta activity in tasks without sustained motor demands. Here, we measured magnetoencephalography (MEG) and electromyography (EMG) in 18 human participants performing a sustained isometric contraction (gripping) task. If cortico-muscular beta connectivity is directly responsible for sustaining a stable motor state, then beta activity within single trials should be (or become) sustained in this context. In contrast, we found that motor beta activity and connectivity with the downstream muscle were transient. Moreover, we found that sustained motor requirements did not prolong beta-event duration in comparison to rest. These findings suggest that neural synchronisation between the brain and the muscle involves short 'bursts' of frequency-specific connectivity, even when task demands - and motor behaviour - are sustained.
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Affiliation(s)
- Irene Echeverria-Altuna
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, United Kingdom,Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom,Corresponding authors at: Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Andrew J. Quinn
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Nahid Zokaei
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, United Kingdom,Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Mark W. Woolrich
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Anna C. Nobre
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, United Kingdom,Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom,Corresponding authors at: Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Freek van Ede
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, United Kingdom,Institute for Brain and Behavior Amsterdam, Department of Experimental and Applied Psychology, Vrije University Amsterdam, Amsterdam, The Netherlands
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Mercier MR, Dubarry AS, Tadel F, Avanzini P, Axmacher N, Cellier D, Vecchio MD, Hamilton LS, Hermes D, Kahana MJ, Knight RT, Llorens A, Megevand P, Melloni L, Miller KJ, Piai V, Puce A, Ramsey NF, Schwiedrzik CM, Smith SE, Stolk A, Swann NC, Vansteensel MJ, Voytek B, Wang L, Lachaux JP, Oostenveld R. Advances in human intracranial electroencephalography research, guidelines and good practices. Neuroimage 2022; 260:119438. [PMID: 35792291 DOI: 10.1016/j.neuroimage.2022.119438] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/23/2022] [Accepted: 06/30/2022] [Indexed: 12/11/2022] Open
Abstract
Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.
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Grassini S, Segurini GV, Koivisto M. Watching Nature Videos Promotes Physiological Restoration: Evidence From the Modulation of Alpha Waves in Electroencephalography. Front Psychol 2022; 13:871143. [PMID: 35747675 PMCID: PMC9210930 DOI: 10.3389/fpsyg.2022.871143] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 05/06/2022] [Indexed: 12/02/2022] Open
Abstract
Various lines of evidence have shown that nature exposure is beneficial for humans. Despite several empirical findings pointing out to cognitive and emotional positive effects, most of the evidence of these effects are correlational, and it has been challenging to identify a cause-effect relationship between nature exposure and cognitive and emotional benefits. Only few of the published studies use psychophysiological methods to assess the biological correlates of these positive effects. Establishing a connection between human physiology and contact with natural settings is important for identifying cause-effect relationships between exposure to natural environments and the positive effects commonly reported in connection to nature exposure. In the present study, we recorded physiological indexes of brain activity (electroencephalography) and sympathetic nervous system (electrodermal activity), while the participants were presented with a series of videos displaying natural, urban, or neutral (non-environmental, computerized) scenes. Participants rated the scenes for their perceived relaxing value, and after each experimental condition, they performed a cognitive task (digit span backward). Participants rated natural videos as the most relaxing. Spectral analyses of EEG showed that natural scenes promoted alpha waves, especially over the central brain. The results suggest that experiencing natural environments virtually produces measurable and reliable brain activity markers which are known to be related to restorative processes.
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Affiliation(s)
- Simone Grassini
- Department of Social Studies, University of Stavanger, Stavanger, Norway
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
| | | | - Mika Koivisto
- Department of Psychology, University of Turku, Turku, Finland
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Dopamine depletion can be predicted by the aperiodic component of subthalamic local field potentials. Neurobiol Dis 2022; 168:105692. [DOI: 10.1016/j.nbd.2022.105692] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 03/06/2022] [Accepted: 03/11/2022] [Indexed: 12/19/2022] Open
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Predicting Math Ability Using Working Memory, Number Sense, and Neurophysiology in Children and Adults. Brain Sci 2022; 12:brainsci12050550. [PMID: 35624937 PMCID: PMC9139259 DOI: 10.3390/brainsci12050550] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 04/21/2022] [Accepted: 04/22/2022] [Indexed: 11/16/2022] Open
Abstract
Previous work has shown relations between domain-general processes, domain-specific processes, and mathematical ability. However, the underlying neurophysiological effects of mathematical ability are less clear. Recent evidence highlighted the potential role of beta oscillations in mathematical ability. Here we investigate whether domain-general (working memory) and domain-specific (number sense) processes mediate the relation between resting-state beta oscillations and mathematical ability, and how this may differ as a function of development (children vs. adults). We compared a traditional analysis method normally used in EEG studies with a more recently developed parameterization method that separates periodic from aperiodic activity. Regardless of methods chosen, we found no support for mediation of working memory and number sense, neither for children nor for adults. However, we found subtle differences between the methods. Additionally, we showed that the traditional EEG analysis method conflates periodic activity with aperiodic activity; in addition, the latter is strongly related to mathematical ability and this relation differs between children and adults. At the cognitive level, our findings do not support previous suggestions of a mediation of working memory and number sense. At the neurophysiological level our findings suggest that aperiodic, rather than periodic, activity is linked to mathematical ability as a function of development.
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Yao L, Zhu B, Shoaran M. Fast and accurate decoding of finger movements from ECoG through Riemannian features and modern machine learning techniques. J Neural Eng 2022; 19. [DOI: 10.1088/1741-2552/ac4ed1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 01/25/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Objective: Accurate decoding of individual finger movements is crucial for advanced prosthetic control. In this work, we introduce the use of Riemannian-space features and temporal dynamics of electrocorticography (ECoG) signal combined with modern machine learning tools to improve the motor decoding accuracy at the level of individual fingers. Approach: We selected a set of informative biomarkers that correlated with finger movements and evaluated the performance of state-of-the-art machine learning algorithms on the BCI competition IV dataset (ECoG, three subjects) and a second ECoG dataset with a similar recording paradigm (Stanford, 9 subjects). We further explored the temporal concatenation of features to effectively capture the history of ECoG signal, which led to a significant improvement over single-epoch decoding in both classification (p<0.01) and regression tasks (p<0.01). Main results: Using feature concatenation and gradient boosted trees (the top-performing model), we achieved a classification accuracy of 77.0% in detecting individual finger movements (6-class task, including rest state), improving over the state-of-the-art conditional random fields (CRF) by 11.7% on the 3 BCI competition subjects. In continuous decoding of movement trajectory, our approach resulted in an average Pearson's correlation coefficient (r) of 0.537 across subjects and fingers, outperforming both the BCI competition winner and the state-of-the-art approach reported on the same dataset (CNN+LSTM). Furthermore, our proposed method features a low time complexity, with only <17.2s required for training and <50ms for inference. This enables about 250× speed-up in training compared to previously reported deep learning method with state-of-the-art performance. Significance: The proposed techniques enable fast, reliable, and high-performance prosthetic control through minimally-invasive cortical signals.
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Yin Z, Zhu G, Liu Y, Zhao B, Liu D, Bai Y, Zhang Q, Shi L, Feng T, Yang A, Liu H, Meng F, Neumann WJ, Kühn AA, Jiang Y, Zhang J. OUP accepted manuscript. Brain 2022; 145:2407-2421. [PMID: 35441231 PMCID: PMC9337810 DOI: 10.1093/brain/awac121] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/11/2022] [Accepted: 03/24/2022] [Indexed: 11/30/2022] Open
Abstract
Freezing of gait is a debilitating symptom in advanced Parkinson’s disease and responds heterogeneously to treatments such as deep brain stimulation. Recent studies indicated that cortical dysfunction is involved in the development of freezing, while evidence depicting the specific role of the primary motor cortex in the multi-circuit pathology of freezing is lacking. Since abnormal beta-gamma phase-amplitude coupling recorded from the primary motor cortex in patients with Parkinson’s disease indicates parkinsonian state and responses to therapeutic deep brain stimulation, we hypothesized this metric might reveal unique information on understanding and improving therapy for freezing of gait. Here, we directly recorded potentials in the primary motor cortex using subdural electrocorticography and synchronously captured gait freezing using optoelectronic motion-tracking systems in 16 freely-walking patients with Parkinson’s disease who received subthalamic nucleus deep brain stimulation surgery. Overall, we recorded 451 timed up-and-go walking trials and quantified 7073 s of stable walking and 3384 s of gait freezing in conditions of on/off-stimulation and with/without dual-tasking. We found that (i) high beta-gamma phase-amplitude coupling in the primary motor cortex was detected in freezing trials (i.e. walking trials that contained freezing), but not non-freezing trials, and the high coupling in freezing trials was not caused by dual-tasking or the lack of movement; (ii) non-freezing episodes within freezing trials also demonstrated abnormally high couplings, which predicted freezing severity; (iii) deep brain stimulation of subthalamic nucleus reduced these abnormal couplings and simultaneously improved freezing; and (iv) in trials that were at similar coupling levels, stimulation trials still demonstrated lower freezing severity than no-stimulation trials. These findings suggest that elevated phase-amplitude coupling in the primary motor cortex indicates higher probabilities of freezing. Therapeutic deep brain stimulation alleviates freezing by both decoupling cortical oscillations and enhancing cortical resistance to abnormal coupling. We formalized these findings to a novel ‘bandwidth model,’ which specifies the role of cortical dysfunction, cognitive burden and therapeutic stimulation on the emergence of freezing. By targeting key elements in the model, we may develop next-generation deep brain stimulation approaches for freezing of gait.
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Affiliation(s)
| | | | | | - Baotian Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Defeng Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Yutong Bai
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Quan Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Lin Shi
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tao Feng
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Anchao Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Huanguang Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Fangang Meng
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Wolf Julian Neumann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité—Campus Mitte, Charite—Universitatsmedizin Berlin, Chariteplatz 1, 10117 Berlin, Germany
| | - Andrea A Kühn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité—Campus Mitte, Charite—Universitatsmedizin Berlin, Chariteplatz 1, 10117 Berlin, Germany
- Berlin School of Mind and Brain, Charite—Universitatsmedizin Berlin, Unter den Linden 6, 10099 Berlin, Germany
- NeuroCure, Charite—Universitatsmedizin Berlin, Chariteplatz 1, 10117 Berlin, Germany
| | - Yin Jiang
- Correspondence may also be addressed to: Dr Yin Jiang Capital Medical University Department of Functional Neurosurgery, Beijing Neurosurgical Institute No. 119 South 4208 Ring West Road Fengtai District, 100070 Beijing, China E-mail:
| | - Jianguo Zhang
- Correspondence to: Prof. Dr Jianguo Zhang Capital Medical University Department of Neurosurgery, Beijing Tiantan Hospital No. 119 South 4th Ring West Road Fengtai District, 100070 Beijing, China E-mail:
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Pradeepkumar J, Anandakumar M, Kugathasan V, Lalitharatne TD, De Silva AC, Kappel SL. Decoding of Hand Gestures from Electrocorticography with LSTM Based Deep Neural Network. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:420-423. [PMID: 34891323 DOI: 10.1109/embc46164.2021.9630958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Hand gesture decoding is a key component of controlling prosthesis in the area of Brain Computer Interface (BCI). This study is concerned with classification of hand gestures, based on Electrocorticography (ECoG) recordings. Recent studies have utilized the temporal information in ECoG signals for robust hand gesture decoding. In our preliminary analysis on ECoG recordings of hand gestures, we observed different power variations in six frequency bands ranging from 4 to 200 Hz. Therefore, the current trend of including temporal information in the classifier was extended to provide equal importance to power variations in each of these frequency bands. Statistical and Principal Component Analysis (PCA) based feature reduction was implemented for each frequency band separately, and classification was performed with a Long Short-Term Memory (LSTM) based neural network to utilize both temporal and spatial information of each frequency band. The proposed architecture along with each feature reduction method was tested on ECoG recordings of five finger flexions performed by seven subjects from the publicly available 'fingerflex' dataset. An average classification accuracy of 82.4% was achieved with the statistical based channel selection method which is an improvement compared to state-of-the-art methods.
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Liu S, Li G, Jiang S, Wu X, Hu J, Zhang D, Chen L. Investigating Data Cleaning Methods to Improve Performance of Brain-Computer Interfaces Based on Stereo-Electroencephalography. Front Neurosci 2021; 15:725384. [PMID: 34690673 PMCID: PMC8528199 DOI: 10.3389/fnins.2021.725384] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 09/01/2021] [Indexed: 11/13/2022] Open
Abstract
Stereo-electroencephalography (SEEG) utilizes localized and penetrating depth electrodes to directly measure electrophysiological brain activity. The implanted electrodes generally provide a sparse sampling of multiple brain regions, including both cortical and subcortical structures, making the SEEG neural recordings a potential source for the brain–computer interface (BCI) purpose in recent years. For SEEG signals, data cleaning is an essential preprocessing step in removing excessive noises for further analysis. However, little is known about what kinds of effect that different data cleaning methods may exert on BCI decoding performance and, moreover, what are the reasons causing the differentiated effects. To address these questions, we adopted five different data cleaning methods, including common average reference, gray–white matter reference, electrode shaft reference, bipolar reference, and Laplacian reference, to process the SEEG data and evaluated the effect of these methods on improving BCI decoding performance. Additionally, we also comparatively investigated the changes of SEEG signals induced by these different methods from multiple-domain (e.g., spatial, spectral, and temporal domain). The results showed that data cleaning methods could improve the accuracy of gesture decoding, where the Laplacian reference produced the best performance. Further analysis revealed that the superiority of the data cleaning method with excellent performance might be attributed to the increased distinguishability in the low-frequency band. The findings of this work highlighted the importance of applying proper data clean methods for SEEG signals and proposed the application of Laplacian reference for SEEG-based BCI.
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Affiliation(s)
- Shengjie Liu
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Guangye Li
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Shize Jiang
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaolong Wu
- Department of Electronic and Electrical Engineering, University of Bath, Bath, United Kingdom
| | - Jie Hu
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
| | - Dingguo Zhang
- Department of Electronic and Electrical Engineering, University of Bath, Bath, United Kingdom
| | - Liang Chen
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
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Local field potentials in a pre-motor region predict learned vocal sequences. PLoS Comput Biol 2021; 17:e1008100. [PMID: 34555020 PMCID: PMC8460039 DOI: 10.1371/journal.pcbi.1008100] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 07/08/2021] [Indexed: 11/19/2022] Open
Abstract
Neuronal activity within the premotor region HVC is tightly synchronized to, and crucial for, the articulate production of learned song in birds. Characterizations of this neural activity detail patterns of sequential bursting in small, carefully identified subsets of neurons in the HVC population. The dynamics of HVC are well described by these characterizations, but have not been verified beyond this scale of measurement. There is a rich history of using local field potentials (LFP) to extract information about behavior that extends beyond the contribution of individual cells. These signals have the advantage of being stable over longer periods of time, and they have been used to study and decode human speech and other complex motor behaviors. Here we characterize LFP signals presumptively from the HVC of freely behaving male zebra finches during song production to determine if population activity may yield similar insights into the mechanisms underlying complex motor-vocal behavior. Following an initial observation that structured changes in the LFP were distinct to all vocalizations during song, we show that it is possible to extract time-varying features from multiple frequency bands to decode the identity of specific vocalization elements (syllables) and to predict their temporal onsets within the motif. This demonstrates the utility of LFP for studying vocal behavior in songbirds. Surprisingly, the time frequency structure of HVC LFP is qualitatively similar to well-established oscillations found in both human and non-human mammalian motor areas. This physiological similarity, despite distinct anatomical structures, may give insight into common computational principles for learning and/or generating complex motor-vocal behaviors. Vocalizations, such as speech and song, are a motor process that requires the coordination of numerous muscle groups receiving instructions from specific brain regions. In songbirds, HVC is a premotor brain region required for singing; it is populated by a set of neurons that fire sparsely during song. How HVC enables song generation is not well understood. Here we describe network activity presumptively from HVC that precedes the initiation of each vocal element during singing. This network activity can be used to predict both the identity of each vocal element (syllable) and when it will occur during song. In addition, this network activity is similar to activity that has been documented in human, non-human primate, and mammalian premotor regions tied to muscle movements. These similarities add to a growing body of literature that finds parallels between songbirds and humans in respect to the motor control of vocal organs. Furthermore, given the similarities of the songbird and human motor-vocal systems, these results suggest that the songbird model could be leveraged to accelerate the development of clinically translatable speech prosthesis.
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Larzabal C, Bonnet S, Costecalde T, Auboiroux V, Charvet G, Chabardes S, Aksenova T, Sauter-Starace F. Long-term stability of the chronic epidural wireless recorder WIMAGINE in tetraplegic patients. J Neural Eng 2021; 18. [PMID: 34425566 DOI: 10.1088/1741-2552/ac2003] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 08/23/2021] [Indexed: 11/12/2022]
Abstract
Objective.The evaluation of the long-term stability of ElectroCorticoGram (ECoG) signals is an important scientific question as new implantable recording devices can be used for medical purposes such as Brain-Computer Interface (BCI) or brain monitoring.Approach.The long-term clinical validation of wireless implantable multi-channel acquisition system for generic interface with neurons (WIMAGINE), a wireless 64-channel epidural ECoG recorder was investigated. The WIMAGINE device was implanted in two quadriplegic patients within the context of a BCI protocol. This study focused on the ECoG signal stability in two patients bilaterally implanted in June 2017 (P1) and in November 2019 (P2).Methods. The ECoG signal was recorded at rest prior to each BCI session resulting in a 32 month and in a 14 month follow-up for P1 and P2 respectively. State-of-the-art signal evaluation metrics such as root mean square (RMS), the band power (BP), the signal to noise ratio (SNR), the effective bandwidth (EBW) and the spectral edge frequency (SEF) were used to evaluate stability of signal over the implantation time course. The time-frequency maps obtained from task-related motor activations were also studied to investigate the long-term selectivity of the electrodes.Mainresults.Based on temporal linear regressions, we report a limited decrease of the signal average level (RMS), spectral distribution (BP) and SNR, and a remarkable steadiness of the EBW and SEF. Time-frequency maps obtained during motor imagery, showed a high level of discrimination 1 month after surgery and also after 2 years.Conclusions.The WIMAGINE epidural device showed high stability over time. The signal evaluation metrics of two quadriplegic patients during 32 months and 14 months respectively provide strong evidence that this wireless implant is well-suited for long-term ECoG recording.Significance.These findings are relevant for the future of implantable BCIs, and could benefit other patients with spinal cord injury, amyotrophic lateral sclerosis, neuromuscular diseases or drug-resistant epilepsy.
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Affiliation(s)
| | - Stéphane Bonnet
- University Grenoble Alpes, CEA, LETI, DTBS, Grenoble 38000, France
| | - Thomas Costecalde
- University Grenoble Alpes, CEA, LETI, Clinatec, Grenoble 38000, France
| | - Vincent Auboiroux
- University Grenoble Alpes, CEA, LETI, Clinatec, Grenoble 38000, France
| | - Guillaume Charvet
- University Grenoble Alpes, CEA, LETI, Clinatec, Grenoble 38000, France
| | - Stéphan Chabardes
- University Grenoble Alpes, Grenoble University Hospital, Grenoble 38000, France
| | - Tetiana Aksenova
- University Grenoble Alpes, CEA, LETI, Clinatec, Grenoble 38000, France
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Schulz R, Bönstrup M, Guder S, Liu J, Frey B, Quandt F, Krawinkel LA, Cheng B, Thomalla G, Gerloff C. Corticospinal Tract Microstructure Correlates With Beta Oscillatory Activity in the Primary Motor Cortex After Stroke. Stroke 2021; 52:3839-3847. [PMID: 34412514 DOI: 10.1161/strokeaha.121.034344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND AND PURPOSE Cortical beta oscillations are reported to serve as robust measures of the integrity of the human motor system. Their alterations after stroke, such as reduced movement-related beta desynchronization in the primary motor cortex, have been repeatedly related to the level of impairment. However, there is only little data whether such measures of brain function might directly relate to structural brain changes after stroke. METHODS This multimodal study investigated 18 well-recovered patients with stroke (mean age 65 years, 12 males) by means of task-related EEG and diffusion-weighted structural MRI 3 months after stroke. Beta power at rest and movement-related beta desynchronization was assessed in 3 key motor areas of the ipsilesional hemisphere that are the primary motor cortex (M1), the ventral premotor area and the supplementary motor area. Template trajectories of corticospinal tracts (CST) originating from M1, premotor cortex, and supplementary motor area were used to quantify the microstructural state of CST subcomponents. Linear mixed-effects analyses were used to relate tract-related mean fractional anisotropy to EEG measures. RESULTS In the present cohort, we detected statistically significant reductions in ipsilesional CST fractional anisotropy but no alterations in EEG measures when compared with healthy controls. However, in patients with stroke, there was a significant association between both beta power at rest (P=0.002) and movement-related beta desynchronization (P=0.003) in M1 and fractional anisotropy of the CST specifically originating from M1. Similar structure-function relationships were neither evident for ventral premotor area and supplementary motor area, particularly with respect to their CST subcomponents originating from premotor cortex and supplementary motor area, in patients with stroke nor in controls. CONCLUSIONS These data suggest there might be a link connecting microstructure of the CST originating from M1 pyramidal neurons and beta oscillatory activity, measures which have already been related to motor impairment in patients with stroke by previous reports.
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Affiliation(s)
- Robert Schulz
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Germany (R.S., M.B., S.G., B.F., F.Q., L.A.K., B.C., G.T., C.G.)
| | - Marlene Bönstrup
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Germany (R.S., M.B., S.G., B.F., F.Q., L.A.K., B.C., G.T., C.G.).,Department of Neurology, University Medical Centre, Leipzig, Germany (M.B.)
| | - Stephanie Guder
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Germany (R.S., M.B., S.G., B.F., F.Q., L.A.K., B.C., G.T., C.G.)
| | - Jingchun Liu
- Department of Radiology, Tianjin Medical University General Hospital, China (J.L.)
| | - Benedikt Frey
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Germany (R.S., M.B., S.G., B.F., F.Q., L.A.K., B.C., G.T., C.G.)
| | - Fanny Quandt
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Germany (R.S., M.B., S.G., B.F., F.Q., L.A.K., B.C., G.T., C.G.)
| | - Lutz A Krawinkel
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Germany (R.S., M.B., S.G., B.F., F.Q., L.A.K., B.C., G.T., C.G.)
| | - Bastian Cheng
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Germany (R.S., M.B., S.G., B.F., F.Q., L.A.K., B.C., G.T., C.G.)
| | - Götz Thomalla
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Germany (R.S., M.B., S.G., B.F., F.Q., L.A.K., B.C., G.T., C.G.)
| | - Christian Gerloff
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Germany (R.S., M.B., S.G., B.F., F.Q., L.A.K., B.C., G.T., C.G.)
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Gwon D, Ahn M. Alpha and high gamma phase amplitude coupling during motor imagery and weighted cross-frequency coupling to extract discriminative cross-frequency patterns. Neuroimage 2021; 240:118403. [PMID: 34280525 DOI: 10.1016/j.neuroimage.2021.118403] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 06/27/2021] [Accepted: 07/15/2021] [Indexed: 11/27/2022] Open
Abstract
Motor imagery modulates specific neural oscillations like actual movement does. Representatively, suppression of the alpha power (e.g., event-related desynchronization [ERD]) is the typical pattern of motor imagery in the motor cortex. However, in addition to this amplitude-based feature, the coupling across frequencies includes important information about the brain functions and the existence of such complex information has been reported in various invasive studies. Yet, the interaction across multiple frequencies during motor imagery processing is still unclear and has not been widely studied, particularly concerning the non-invasive signals. In this study, we provide empirical evidence of the comodulation between the phase of alpha rhythm and the amplitude of high gamma rhythm during the motor imagery process. We used electroencephalography (EEG) in our investigation during the imagination of left- or right-hand movement recorded from 52 healthy subjects, and quantified the ERD of alpha and phase-amplitude coupling (PAC) which is a relative change of modulation index to the base line period (before the cue). As a result, we found that the coupling between the phase of alpha (8-12 Hz) and the amplitude of high gamma (70-120 Hz) and this PAC decreases during motor imagery and then rebounds to the baseline like alpha ERD (r = 0.29 to 0.42). This correlation between PAC and ERD was particularly stronger in the ipsilateral area. In addition, trials that demonstrated higher alpha power during the ready period (before the cue) showed a larger ERD during motor imagery and similarly, trials with higher modulation index during the ready period yielded a greater decrease in PAC during imagery. In the classification analysis, we found that the effective phase frequency that showed better decoding accuracy in left and right-hand imagery, varied across subjects. Motivated by result, we proposed a weighted cross-frequency coupling (WCFC) method that extracts the maximal discriminative feature by combining band power and CFC. In the evaluation, WCFC with only two electrodes yielded a performance comparable to the conventional algorithm with 64 electrodes in classifying left and right-hand motor imagery. These results indicate that the phase-amplitude frequency plays an important role in motor imagery, and that optimizing this frequency ranges is crucial for extracting information features to decode the motor imagery types.
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Affiliation(s)
- Daeun Gwon
- Department of Information and Communication Engineering, Handong Global University, 37554 South Korea
| | - Minkyu Ahn
- Department of Information and Communication Engineering, Handong Global University, 37554 South Korea; School of Computer Science and Electrical Engineering, Handong Global University, 37554 South Korea.
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