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Barnett B, Andersen LM, Fleming SM, Dijkstra N. Identifying content-invariant neural signatures of perceptual vividness. PNAS NEXUS 2024; 3:pgae061. [PMID: 38415219 PMCID: PMC10898512 DOI: 10.1093/pnasnexus/pgae061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 01/31/2024] [Indexed: 02/29/2024]
Abstract
Some conscious experiences are more vivid than others. Although perceptual vividness is a key component of human consciousness, how variation in this magnitude property is registered by the human brain is unknown. A striking feature of neural codes for magnitude in other psychological domains, such as number or reward, is that the magnitude property is represented independently of its sensory features. To test whether perceptual vividness also covaries with neural codes that are invariant to sensory content, we reanalyzed existing magnetoencephalography and functional MRI data from two distinct studies which quantified perceptual vividness via subjective ratings of awareness and visibility. Using representational similarity and decoding analyses, we find evidence for content-invariant neural signatures of perceptual vividness distributed across visual, parietal, and frontal cortices. Our findings indicate that the neural correlates of subjective vividness may share similar properties to magnitude codes in other cognitive domains.
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Affiliation(s)
- Benjy Barnett
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
- Department of Experimental Psychology, University College London, London WC1H 0AP, UK
| | - Lau M Andersen
- Aarhus Institute of Advanced Studies, 8000 Aarhus C, Denmark
- Center of Functionally Integrative Neuroscience, 8000 Aarhus C, Denmark
- Department for Linguistics, Cognitive Science and Semiotics, Aarhus University, 8000 Aarhus C, Denmark
| | - Stephen M Fleming
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
- Department of Experimental Psychology, University College London, London WC1H 0AP, UK
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London WC1B 5EH, UK
| | - Nadine Dijkstra
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
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Shi LJ, Li CC, Lin YC, Ding CT, Wang YP, Zhang JC. The association of magnetoencephalography high-frequency oscillations with epilepsy types and a ripple-based method with source-level connectivity for mapping epilepsy sources. CNS Neurosci Ther 2023; 29:1423-1433. [PMID: 36815318 PMCID: PMC10068465 DOI: 10.1111/cns.14115] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 01/09/2023] [Accepted: 01/25/2023] [Indexed: 02/24/2023] Open
Abstract
OBJECTIVE To explore the association between high-frequency oscillations (HFOs) and epilepsy types and to improve the accuracy of source localization. METHODS Magnetoencephalography (MEG) ripples of 63 drug-resistant epilepsy patients were detected. Ripple rates, distribution, spatial complexity, and the clustering coefficient of ripple channels were used for the preliminary classification of lateral temporal lobe epilepsy (LTLE), mesial temporal lobe epilepsy (MTLE), and nontemporal lobe epilepsy (NTLE), mainly frontal lobe epilepsy (FLE). Furthermore, the seizure site identification was improved using the Tucker LCMV method and source-level betweenness centrality. RESULTS Ripple rates were significantly higher in MTLE than in LTLE and NTLE (p < 0.05). The LTLE and MTLE were mainly distributed in the temporal lobe, followed by the parietal lobe, occipital lobe, and frontal lobe, whereas MTLE ripples were mainly distributed in the frontal lobe, then parietal lobe and occipital lobe. Nevertheless, the NTLE ripples were primarily in the frontal lobe and partially in the occipital lobe (p < 0.05). Meanwhile, the spatial complexity of NTLE was significantly higher than that of LTLE and MTLE and was lowest in MTLE (p < 0.01). However, an opposite trend was observed for the standardized clustering coefficient compared with spatial complexity (p < 0.01). Finally, the tucker algorithm showed a higher percentage of ripples at the surgical site when the betweenness centrality was added (p < 0.01). CONCLUSION This study demonstrated that HFO rates, distribution, spatial complexity, and clustering coefficient of ripple channels varied considerably among the three epilepsy types. Additionally, tucker MEG estimation combined with ripple rates based on the source-level functional connectivity is a promising approach for presurgical epilepsy evaluation.
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Affiliation(s)
- Li-Juan Shi
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Can-Cheng Li
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Yi-Cong Lin
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Brain Functional Disease and Neuromodulation of Beijing Key Laboratory, Beijing, China
| | - Cheng-Tao Ding
- Hefei Innovation Research Institute, Beihang University, Hefei, Anhui, China
| | - Yu-Ping Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Brain Functional Disease and Neuromodulation of Beijing Key Laboratory, Beijing, China
| | - Ji-Cong Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China.,Hefei Innovation Research Institute, Beihang University, Hefei, Anhui, China
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Wu S, Ramdas A, Wehbe L. Brainprints: identifying individuals from magnetoencephalograms. Commun Biol 2022; 5:852. [PMID: 35995976 PMCID: PMC9395342 DOI: 10.1038/s42003-022-03727-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 07/15/2022] [Indexed: 01/02/2023] Open
Abstract
Magnetoencephalography (MEG) is used to study a wide variety of cognitive processes. Increasingly, researchers are adopting principles of open science and releasing their MEG data. While essential for reproducibility, sharing MEG data has unforeseen privacy risks. Individual differences may make a participant identifiable from their anonymized recordings. However, our ability to identify individuals based on these individual differences has not yet been assessed. Here, we propose interpretable MEG features to characterize individual difference. We term these features brainprints (brain fingerprints). We show through several datasets that brainprints accurately identify individuals across days, tasks, and even between MEG and Electroencephalography (EEG). Furthermore, we identify consistent brainprint components that are important for identification. We study the dependence of identifiability on the amount of data available. We also relate identifiability to the level of preprocessing and the experimental task. Our findings reveal specific aspects of individual variability in MEG. They also raise concerns about unregulated sharing of brain data, even if anonymized.
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Affiliation(s)
- Shenghao Wu
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.,Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Aaditya Ramdas
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.,Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Leila Wehbe
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA. .,Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.
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Kheirkhah M, Baumbach P, Leistritz L, Witte OW, Walter M, Gilbert JR, Zarate Jr. CA, Klingner CM. The Right Hemisphere Is Responsible for the Greatest Differences in Human Brain Response to High-Arousing Emotional versus Neutral Stimuli: A MEG Study. Brain Sci 2021; 11:960. [PMID: 34439579 PMCID: PMC8412101 DOI: 10.3390/brainsci11080960] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 07/09/2021] [Accepted: 07/16/2021] [Indexed: 11/17/2022] Open
Abstract
Studies investigating human brain response to emotional stimuli-particularly high-arousing versus neutral stimuli-have obtained inconsistent results. The present study was the first to combine magnetoencephalography (MEG) with the bootstrapping method to examine the whole brain and identify the cortical regions involved in this differential response. Seventeen healthy participants (11 females, aged 19 to 33 years; mean age, 26.9 years) were presented with high-arousing emotional (pleasant and unpleasant) and neutral pictures, and their brain responses were measured using MEG. When random resampling bootstrapping was performed for each participant, the greatest differences between high-arousing emotional and neutral stimuli during M300 (270-320 ms) were found to occur in the right temporo-parietal region. This finding was observed in response to both pleasant and unpleasant stimuli. The results, which may be more robust than previous studies because of bootstrapping and examination of the whole brain, reinforce the essential role of the right hemisphere in emotion processing.
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Affiliation(s)
- Mina Kheirkhah
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20814, USA; (J.R.G.); (C.A.Z.)
- Biomagnetic Center, Jena University Hospital, 07747 Jena, Germany;
- Department of Psychiatry and Psychotherapy, Jena University Hospital, 07743 Jena, Germany;
| | - Philipp Baumbach
- Department of Anesthesiology and Intensive Care Medicine, Jena University Hospital, 07747 Jena, Germany;
| | - Lutz Leistritz
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, 07740 Jena, Germany;
| | - Otto W. Witte
- Hans Berger Department of Neurology, Jena University Hospital, 07747 Jena, Germany;
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Jena University Hospital, 07743 Jena, Germany;
| | - Jessica R. Gilbert
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20814, USA; (J.R.G.); (C.A.Z.)
| | - Carlos A. Zarate Jr.
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20814, USA; (J.R.G.); (C.A.Z.)
| | - Carsten M. Klingner
- Biomagnetic Center, Jena University Hospital, 07747 Jena, Germany;
- Hans Berger Department of Neurology, Jena University Hospital, 07747 Jena, Germany;
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Yang CY, Lin CP. Classification of cognitive reserve in healthy older adults based on brain activity using support vector machine. Physiol Meas 2020; 41:065009. [PMID: 32464620 DOI: 10.1088/1361-6579/ab979e] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
OBJECTIVE Cognitive reserve (CR) refers to the capacity of the brain to actively cope with damage via the implementation of remedial cognitive processes. Traditional CR measurements focus on static proxies, which may not be able to appropriately estimate dynamic changes in CR. This study therefore investigated the cognitive performance and characteristics of brain activity of low- and high-CR healthy adults during resting and n-back task states and categorized subjects according to magnetoencephalographic (MEG) information using a support vector machine (SVM) classifier. APPROACH Forty-one volunteers were divided into groups with low and high CR indexes based on their education, occupational attainment, leisure and social activities. MAIN RESULTS The results can be summarized as follows. First, subjects with a higher CR had higher accuracies and faster reaction times in the task. Second, subjects with a lower CR had a higher M300 intensity but a constant M300 latency. Third, subjects with a higher CR had a higher beta intensity in the parietal and occipital regions during the task, whereas subjects with a higher CR had a higher gamma intensity in the right temporal region in the resting state. Finally, subjects with a higher CR had negative gamma asymmetry between the right and left occipital regions, whereas subjects with a lower CR had positive values in the resting state. SIGNIFICANCE These MEG results were subsequently used to classify subjects into high-/low-CR subjects using an SVM classifier, and a mean accuracy of 88.89% was obtained. This objective and nonstatic method for determining CR level warrants further research for a wider variety of future clinical applications.
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Affiliation(s)
- Chia-Yen Yang
- Department of Biomedical Engineering, Ming-Chuan University, Taoyuan, Taiwan
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