1
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Gattas S, Larson MS, Mnatsakanyan L, Sen-Gupta I, Vadera S, Swindlehurst AL, Rapp PE, Lin JJ, Yassa MA. Theta mediated dynamics of human hippocampal-neocortical learning systems in memory formation and retrieval. Nat Commun 2023; 14:8505. [PMID: 38129375 PMCID: PMC10739909 DOI: 10.1038/s41467-023-44011-6] [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: 09/17/2020] [Accepted: 11/23/2023] [Indexed: 12/23/2023] Open
Abstract
Episodic memory arises as a function of dynamic interactions between the hippocampus and the neocortex, yet the mechanisms have remained elusive. Here, using human intracranial recordings during a mnemonic discrimination task, we report that 4-5 Hz (theta) power is differentially recruited during discrimination vs. overgeneralization, and its phase supports hippocampal-neocortical when memories are being formed and correctly retrieved. Interactions were largely bidirectional, with small but significant net directional biases; a hippocampus-to-neocortex bias during acquisition of new information that was subsequently correctly discriminated, and a neocortex-to-hippocampus bias during accurate discrimination of new stimuli from similar previously learned stimuli. The 4-5 Hz rhythm may facilitate the initial stages of information acquisition by neocortex during learning and the recall of stored information from cortex during retrieval. Future work should further probe these dynamics across different types of tasks and stimuli and computational models may need to be expanded accordingly to accommodate these findings.
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Affiliation(s)
- Sandra Gattas
- Department of Electrical Engineering and Computer Science, School of Engineering, University of California, Irvine, CA, 92617, USA
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, 92697, USA
| | - Myra Sarai Larson
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, 92697, USA
- Department of Neurobiology and Behavior, School of Biological Sciences, University of California, Irvine, CA, 92697, USA
| | - Lilit Mnatsakanyan
- Department of Neurology, School of Medicine, University of California, Irvine, CA, 92697, USA
| | - Indranil Sen-Gupta
- Department of Neurology, School of Medicine, University of California, Irvine, CA, 92697, USA
| | - Sumeet Vadera
- Department of Neurological Surgery, School of Medicine, University of California, Irvine, CA, 92697, USA
| | - A Lee Swindlehurst
- Department of Electrical Engineering and Computer Science, School of Engineering, University of California, Irvine, CA, 92617, USA
| | - Paul E Rapp
- Department of Military & Emergency Medicine, Uniformed Services University, Bethesda, MD, 20814, USA
| | - Jack J Lin
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, 92697, USA
- Department of Neurology, School of Medicine, University of California, Irvine, CA, 92697, USA
| | - Michael A Yassa
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, 92697, USA.
- Department of Neurobiology and Behavior, School of Biological Sciences, University of California, Irvine, CA, 92697, USA.
- Department of Neurology, School of Medicine, University of California, Irvine, CA, 92697, USA.
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2
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Rossi C, Vidaurre D, Costers L, Akbarian F, Woolrich M, Nagels G, Van Schependom J. A data-driven network decomposition of the temporal, spatial, and spectral dynamics underpinning visual-verbal working memory processes. Commun Biol 2023; 6:1079. [PMID: 37872313 PMCID: PMC10593846 DOI: 10.1038/s42003-023-05448-z] [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: 02/20/2023] [Accepted: 10/11/2023] [Indexed: 10/25/2023] Open
Abstract
The brain dynamics underlying working memory (WM) unroll via transient frequency-specific large-scale brain networks. This multidimensionality (time, space, and frequency) challenges traditional analyses. Through an unsupervised technique, the time delay embedded-hidden Markov model (TDE-HMM), we pursue a functional network analysis of magnetoencephalographic data from 38 healthy subjects acquired during an n-back task. Here we show that this model inferred task-specific networks with unique temporal (activation), spectral (phase-coupling connections), and spatial (power spectral density distribution) profiles. A theta frontoparietal network exerts attentional control and encodes the stimulus, an alpha temporo-occipital network rehearses the verbal information, and a broad-band frontoparietal network with a P300-like temporal profile leads the retrieval process and motor response. Therefore, this work provides a unified and integrated description of the multidimensional working memory dynamics that can be interpreted within the neuropsychological multi-component model of WM, improving the overall neurophysiological and neuropsychological comprehension of WM functioning.
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Affiliation(s)
- Chiara Rossi
- AIMS lab, Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium.
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Brussels, Belgium.
| | - Diego Vidaurre
- Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Aarhus university, Aarhus, Denmark
- Department of Psychiatry, Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Lars Costers
- AIMS lab, Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
- icometrix, Leuven, Belgium
| | - Fahimeh Akbarian
- AIMS lab, Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Brussels, Belgium
| | - Mark Woolrich
- Department of Psychiatry, Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Guy Nagels
- AIMS lab, Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
- Department of Neurology, Universitair Ziekenhuis Brussel, Brussels, Belgium
- St Edmund Hall, University of Oxford, Oxford, UK
| | - Jeroen Van Schependom
- AIMS lab, Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium.
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Brussels, Belgium.
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3
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Lu Y, Guo X, Weng X, Jiang H, Yan H, Shen X, Feng Z, Zhao X, Li L, Zheng L, Liu Z, Men W, Gao JH. Theta Signal Transfer from Parietal to Prefrontal Cortex Ignites Conscious Awareness of Implicit Knowledge during Sequence Learning. J Neurosci 2023; 43:6760-6778. [PMID: 37607820 PMCID: PMC10552945 DOI: 10.1523/jneurosci.2172-22.2023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 08/08/2023] [Accepted: 08/13/2023] [Indexed: 08/24/2023] Open
Abstract
Unconscious acquisition of sequence structure from experienced events can lead to explicit awareness of the pattern through extended practice. Although the implicit-to-explicit transition has been extensively studied in humans using the serial reaction time (SRT) task, the subtle neural activity supporting this transition remains unclear. Here, we investigated whether frequency-specific neural signal transfer contributes to this transition. A total of 208 participants (107 females) learned a sequence pattern through a multisession SRT task, allowing us to observe the transitions. Session-by-session measures of participants' awareness for sequence knowledge were conducted during the SRT task to identify the session when the transition occurred. By analyzing time course RT data using switchpoint modeling, we identified an increase in learning benefit specifically at the transition session. Electroencephalogram (EEG)/magnetoencephalogram (MEG) recordings revealed increased theta power in parietal (precuneus) regions one session before the transition (pretransition) and a prefrontal (superior frontal gyrus; SFG) one at the transition session. Phase transfer entropy (PTE) analysis confirmed that directional theta transfer from precuneus → SFG occurred at the pretransition session and its strength positively predicted learning improvement at the subsequent transition session. Furthermore, repetitive transcranial magnetic stimulation (TMS) modulated precuneus theta power and altered transfer strength from precuneus to SFG, resulting in changes in both transition rate and learning benefit at that specific point of transition. Our brain-stimulation evidence supports a role for parietal → prefrontal theta signal transfer in igniting conscious awareness of implicitly acquired knowledge.SIGNIFICANCE STATEMENT There exists a pervasive phenomenon wherein individuals unconsciously acquire sequence patterns from their environment, gradually becoming aware of the underlying regularities through repeated practice. While previous studies have established the robustness of this implicit-to-explicit transition in humans, the refined neural mechanisms facilitating conscious access to implicit knowledge remain poorly understood. Here, we demonstrate that prefrontal activity, known to be crucial for conscious awareness, is triggered by neural signal transfer originating from the posterior brain region, specifically the precuneus. By employing brain stimulation techniques, we establish a causal link between neural signal transfer and the occurrence of awareness. Our findings unveil a mechanism by which implicit knowledge becomes consciously accessible in human cognition.
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Affiliation(s)
- Yang Lu
- Fudan Institute on Ageing, Fudan University, Shanghai, China, 200433
- Ministry of education (MOE) Laboratory for National Development and Intelligent Governance, Fudan University, Shanghai, China, 200433
- School of Psychology and cognitive science, East China Normal University, Shanghai, China, 200062
| | - Xiuyan Guo
- Fudan Institute on Ageing, Fudan University, Shanghai, China, 200433
- Ministry of education (MOE) Laboratory for National Development and Intelligent Governance, Fudan University, Shanghai, China, 200433
| | - Xue Weng
- School of Psychology and cognitive science, East China Normal University, Shanghai, China, 200062
| | - Haoran Jiang
- Fudan Institute on Ageing, Fudan University, Shanghai, China, 200433
- Ministry of education (MOE) Laboratory for National Development and Intelligent Governance, Fudan University, Shanghai, China, 200433
| | - Huidan Yan
- School of Psychology and cognitive science, East China Normal University, Shanghai, China, 200062
| | - Xianting Shen
- Fudan Institute on Ageing, Fudan University, Shanghai, China, 200433
- Department of Psychology, Fudan University, Shanghai, China, 200433
| | - Zhengning Feng
- School of Psychology and cognitive science, East China Normal University, Shanghai, China, 200062
| | - Xinyue Zhao
- School of Psychology and cognitive science, East China Normal University, Shanghai, China, 200062
| | - Lin Li
- School of Psychology and cognitive science, East China Normal University, Shanghai, China, 200062
| | - Li Zheng
- Fudan Institute on Ageing, Fudan University, Shanghai, China, 200433
- Ministry of education (MOE) Laboratory for National Development and Intelligent Governance, Fudan University, Shanghai, China, 200433
| | - Zhiyuan Liu
- Shaanxi Key Laboratory of Behavior and Cognitive Neuroscience, School of Psychology, Shaanxi Normal University, Xi'an, China, 710062
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China, 100871
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China, 100871
| | - Jia-Hong Gao
- Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China, 100871
- Center for MRI Research and McGovern Institute for Brain Research, Peking University, Beijing, China, 100871
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4
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Gattas S, Larson MS, Mnatsakanyan L, Sen-Gupta I, Vadera S, Swindlehurst L, Rapp PE, Lin JJ, Yassa MA. Theta mediated dynamics of human hippocampal-neocortical learning systems in memory formation and retrieval. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.20.558688. [PMID: 37790541 PMCID: PMC10542525 DOI: 10.1101/2023.09.20.558688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Episodic memory arises as a function of dynamic interactions between the hippocampus and the neocortex, yet the mechanisms have remained elusive. Here, using human intracranial recordings during a mnemonic discrimination task, we report that 4-5 Hz (theta) power is differentially recruited during discrimination vs. overgeneralization, and its phase supports hippocampal-neocortical when memories are being formed and correctly retrieved. Interactions were largely bidirectional, with small but significant net directional biases; a hippocampus-to-neocortex bias during acquisition of new information that was subsequently correctly discriminated, and a neocortex-to-hippocampus bias during accurate discrimination of new stimuli from similar previously learned stimuli. The 4-5 Hz rhythm may facilitate the initial stages of information acquisition by neocortex during learning and the recall of stored information from cortex during retrieval. Future work should further probe these dynamics across different types of tasks and stimuli and computational models may need to be expanded accordingly to accommodate these findings.
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Affiliation(s)
- Sandra Gattas
- Department of Electrical Engineering and Computer Science, School of Engineering, University of California, Irvine, Irvine, CA, 92617, USA
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, California, 92697, USA
| | - Myra Sarai Larson
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, California, 92697, USA
- Department of Neurobiology and Behavior, School of Biological Sciences, University of California, Irvine, Irvine, CA, 92697, USA
| | - Lilit Mnatsakanyan
- Department of Neurology, School of Medicine, University of California, Irvine, CA, 92697, USA
| | - Indranil Sen-Gupta
- Department of Neurology, School of Medicine, University of California, Irvine, CA, 92697, USA
| | - Sumeet Vadera
- Department of Neurological Surgery, School of Medicine, University of California, Irvine, Irvine, CA, 92697, USA
| | - Lee Swindlehurst
- Department of Electrical Engineering and Computer Science, School of Engineering, University of California, Irvine, Irvine, CA, 92617, USA
| | - Paul E. Rapp
- Department of Military & Emergency Medicine, Uniformed Services University, Bethesda, MD, 20814, USA
| | - Jack J. Lin
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, California, 92697, USA
- Department of Neurology, School of Medicine, University of California, Irvine, CA, 92697, USA
| | - Michael A. Yassa
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, California, 92697, USA
- Department of Neurobiology and Behavior, School of Biological Sciences, University of California, Irvine, Irvine, CA, 92697, USA
- Department of Neurology, School of Medicine, University of California, Irvine, CA, 92697, USA
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5
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Nonlinear directed information flow estimation for fNIRS brain network analysis based on the modified multivariate transfer entropy. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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6
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Transcranial stimulation of alpha oscillations up-regulates the default mode network. Proc Natl Acad Sci U S A 2022; 119:2110868119. [PMID: 34969856 PMCID: PMC8740757 DOI: 10.1073/pnas.2110868119] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/12/2021] [Indexed: 12/26/2022] Open
Abstract
The default mode network (DMN) is the most-prominent intrinsic connectivity network, serving as a key architecture of the brain's functional organization. Conversely, dysregulated DMN is characteristic of major neuropsychiatric disorders. However, the field still lacks mechanistic insights into the regulation of the DMN and effective interventions for DMN dysregulation. The current study approached this problem by manipulating neural synchrony, particularly alpha (8 to 12 Hz) oscillations, a dominant intrinsic oscillatory activity that has been increasingly associated with the DMN in both function and physiology. Using high-definition alpha-frequency transcranial alternating current stimulation (α-tACS) to stimulate the cortical source of alpha oscillations, in combination with simultaneous electroencephalography and functional MRI (EEG-fMRI), we demonstrated that α-tACS (versus Sham control) not only augmented EEG alpha oscillations but also strengthened fMRI and (source-level) alpha connectivity within the core of the DMN. Importantly, increase in alpha oscillations mediated the DMN connectivity enhancement. These findings thus identify a mechanistic link between alpha oscillations and DMN functioning. That transcranial alpha modulation can up-regulate the DMN further highlights an effective noninvasive intervention to normalize DMN functioning in various disorders.
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7
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Wang Y, Chen W. A modified phase transfer entropy for cross-frequency directed coupling estimation in brain network. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:27-30. [PMID: 34891231 DOI: 10.1109/embc46164.2021.9629730] [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
Cross-frequency coupling of neural oscillation is widespread during the complex cognitive process. Therefore, identifying cross-frequency information flow is essential for revealing neural dynamics mechanisms in the brain network. A current method based on the information theory, phase transfer entropy (PTE), has been proved its effectiveness in estimating directional coupling in several recent studies. However, there remains some limits in PTE: (1)lack of multivariable effect, (2) poor robustness, (3)curse of dimensionality in the high dimensional system. This study introduced a novel multivariate phase transfer entropy method named "MPTENUE" to solve the above issues. In MPTENUE, it considered the influence of remaining confounding variables, which guaranteed its applicability in a multivariable system. Meanwhile, a nonuniform embedding (NUE) approach for state reconstruction was adopted to eliminate the dimensional curse problem. We performed a series of numerical simulations based on the typical Hénon map model. The results proved that the MPTENUE achieved better noise robustness and effectively avoided the curse of dimension; meanwhile, the accuracy and sensitivity can reach 96.9% and 99.2%, respectively.
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8
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Zhang S, Yan X, Wang Y, Liu B, Gao X. Modulation of brain states on fractal and oscillatory power of EEG in brain-computer interfaces. J Neural Eng 2021; 18. [PMID: 34517346 DOI: 10.1088/1741-2552/ac2628] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 09/13/2021] [Indexed: 11/11/2022]
Abstract
Objective. Electroencephalogram (EEG) is an objective reflection of the brain activities, which provides potential possibilities for brain state estimation based on EEG characteristics. However, how to mine the effective EEG characteristics is still a distressing problem in brain state monitoring.Approach. The phase-scrambled method was used to generate images with different noise levels. Images were encoded into a rapid serial visual presentation paradigm. N-back working memory method was employed to induce and assess fatigue state. The irregular-resampling auto-spectral analysis method was adopted to extract and parameterize (exponent and offset) the characteristics of EEG fractal components, which were analyzed in the four dimensions: fatigue, sustained attention, visual noise and experimental tasks.Main results. The degree of fatigue and visual noise level had positive effects on exponent and offset in the prefrontal lobe, and the ability of sustained attention negatively affected exponent and offset. Compared with visual stimuli task, rest task induced even larger values of exponent and offset and statistically significant in the most cerebral cortex. In addition, the steady-state visual evoked potential amplitudes were negatively and positively affected by the degree of fatigue and noise levels, respectively.Significance. The conclusions of this study provide insights into the relationship between brain states and EEG characteristics. In addition, this study has the potential to provide objective methods for brain states monitoring and EEG modeling.
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Affiliation(s)
- Shangen Zhang
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, People's Republic of China
| | - Xinyi Yan
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, People's Republic of China
| | - Yijun Wang
- China State Key Laboratory on Integrated Optoelectronics, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, People's Republic of China
| | - Baolin Liu
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, People's Republic of China
| | - Xiaorong Gao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, People's Republic of China
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9
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Fogelson N, Diaz-Brage P. Altered directed connectivity during processing of predictive stimuli in psychiatric patient populations. Clin Neurophysiol 2021; 132:2739-2750. [PMID: 34571367 DOI: 10.1016/j.clinph.2021.07.025] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 07/06/2021] [Accepted: 07/20/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVES The study investigated the role of top-down versus bottom-up connectivity, during the processing of predictive information, in three different psychiatric disorders. METHODS Electroencephalography (EEG) was recorded during the performance of a task, which evaluates the ability to use predictive information in order to facilitate predictable versus random target detection. We evaluated EEG event-related directed connectivity, in patients with schizophrenia (SZ), major depressive disorder (MDD), and autism spectrum disorder (ASD), compared with healthy age-matched controls. Directed connectivity was evaluated using phase transfer entropy. RESULTS We showed that top-down frontal-parietal connectivity was weaker in SZ (theta and beta bands) and ASD (alpha band) compared to control subjects, during the processing of stimuli consisting of the predictive sequence. In SZ patients, top-down connectivity was also attenuated, during the processing of predictive targets in the beta frequency band. In contrast, compared with controls, MDD patients displayed an increased top-down flow of information, during the processing of predicted targets (alpha band). CONCLUSIONS The findings suggest that top-down frontal-parietal connectivity is altered differentially across three major psychiatric disorders, specifically during the processing of predictive stimuli. SIGNIFICANCE Altered top-down connectivity may contribute to the specific prediction deficits observed in each of the patient populations.
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Affiliation(s)
- Noa Fogelson
- EEG and Cognition Laboratory, Department of Humanities, University Rey Juan Carlos, Madrid, Spain.
| | - Pablo Diaz-Brage
- EEG and Cognition Laboratory, Department of Humanities, University Rey Juan Carlos, Madrid, Spain
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10
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Local Oscillatory Brain Dynamics of Mind Wandering in Schizophrenia. Brain Sci 2021; 11:brainsci11070910. [PMID: 34356145 PMCID: PMC8304325 DOI: 10.3390/brainsci11070910] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 07/05/2021] [Accepted: 07/06/2021] [Indexed: 11/29/2022] Open
Abstract
A number of studies have focused on brain dynamics underlying mind wandering (MW) states in healthy people. However, there is limited understanding of how the oscillatory dynamics accompanying MW states and task-focused states are characterized in clinical populations. In this study, we explored EEG local synchrony of MW associated with schizophrenia, under the premise that changes in attention that arise during MW are associated with a different pattern of brain activity. To this end, we measured the power of EEG oscillations in different frequency bands, recorded while participants watched short video clips. In the group of participants diagnosed with schizophrenia, the power in MW states was significantly lower than during task-focused states, mainly in the frontal and posterior regions. However, in the group of healthy controls, the differences in power between the task-focused and MW states occurred exclusively in the posterior region. Furthermore, the power of the frequency bands during MW and during episodes of task-focused attention correlated with cognitive variables such as processing speed and working memory. These findings on dynamic changes of local synchronization in different frequency bands and areas of the cortex can improve our understanding of mental disorders, such as schizophrenia.
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11
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Altered directed connectivity during processing of implicit versus explicit predictive stimuli in Parkinson's disease patients. Brain Cogn 2021; 152:105773. [PMID: 34225173 DOI: 10.1016/j.bandc.2021.105773] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 05/06/2021] [Accepted: 06/25/2021] [Indexed: 11/22/2022]
Abstract
The study investigated the role of top-down versus bottom-up connectivity, during the processing of implicit or explicit predictive information, in Parkinson's disease (PD). EEG was recorded during the performance of a task, which evaluated the ability to utilize either implicit or explicit predictive contextual information in order to facilitate the detection of predictable versus random targets. Thus, subjects performed an implicit and explicit session, where subjects were either unaware or made aware of a predictive sequence that signals the presentation of a subsequent target, respectively. We evaluated EEG event-related directed connectivity, in PD patients compared with healthy age-matched controls, using phase transfer entropy. PD patients showed increased top-down frontal-parietal connectivity, compared to control subjects, during the processing of the last (most informative) stimulus of the predictive sequence and of random standards, in the implicit and explicit session, respectively. These findings suggest that PD is associated with compensatory top-down connectivity, specifically during the processing of implicit predictive stimuli. During the explicit session, PD patients seem to allocate more attentional resources to non-informative standard stimuli, compared to controls. These connectivity changes shed further light on the cognitive deficits, associated with the processing of predictive contextual information, that are observed in PD patients.
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12
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Syrjälä J, Basti A, Guidotti R, Marzetti L, Pizzella V. Decoding working memory task condition using magnetoencephalography source level long-range phase coupling patterns. J Neural Eng 2021; 18:016027. [PMID: 33624612 DOI: 10.1088/1741-2552/abcefe] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE The objective of the study is to identify phase coupling patterns that are shared across subjects via a machine learning approach that utilises source space magnetoencephalography (MEG) phase coupling data from a working memory (WM) task. Indeed, phase coupling of neural oscillations is putatively a key factor for communication between distant brain areas and is therefore crucial in performing cognitive tasks, including WM. Previous studies investigating phase coupling during cognitive tasks have often focused on a few a priori selected brain areas or a specific frequency band, and the need for data-driven approaches has been recognised. Machine learning techniques have emerged as valuable tools for the analysis of neuroimaging data since they catch fine-grained differences in the multivariate signal distribution. Here, we expect that these techniques applied to MEG phase couplings can reveal WM-related processes that are shared across individuals. APPROACH We analysed WM data collected as part of the Human Connectome Project. The MEG data were collected while subjects (n = 83) performed N-back WM tasks in two different conditions, namely 2-back (WM condition) and 0-back (control condition). We estimated phase coupling patterns (multivariate phase slope index) for both conditions and for theta, alpha, beta, and gamma bands. The obtained phase coupling data were then used to train a linear support vector machine in order to classify which task condition the subject was performing with an across-subject cross-validation approach. The classification was performed separately based on the data from individual frequency bands and with all bands combined (multiband). Finally, we evaluated the relative importance of the different features (phase couplings) for classification by the means of feature selection probability. MAIN RESULTS The WM condition and control condition were successfully classified based on the phase coupling patterns in the theta (62% accuracy) and alpha bands (60% accuracy) separately. Importantly, the multiband classification showed that phase coupling patterns not only in the theta and alpha but also in the gamma bands are related to WM processing, as testified by improvement in classification performance (71%). SIGNIFICANCE Our study successfully decoded WM tasks using MEG source space functional connectivity. Our approach, combining across-subject classification and a multidimensional metric recently developed by our group, is able to detect patterns of connectivity that are shared across individuals. In other words, the results are generalisable to new individuals and allow meaningful interpretation of task-relevant phase coupling patterns.
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Affiliation(s)
- Jaakko Syrjälä
- Department of Neuroscience, Imaging and Clinical Sciences, 'Gabriele d'Annunzio' University of Chieti-Pescara, Chieti 66013, Italy
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13
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Wang Y, Chen W. Effective brain connectivity for fNIRS data analysis based on multi-delays symbolic phase transfer entropy. J Neural Eng 2020; 17:056024. [PMID: 33055365 DOI: 10.1088/1741-2552/abb4a4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Recently, effective connectivity (EC) calculation methods for functional near-infrared spectroscopy (fNIRS) data mainly face two problems: the first problem is that noise can seriously affect the EC calculation and even lead to false connectivity; the second problem is that it ignores the various real neurotransmission delays between the brain region, and instead uses a fixed delay coefficient for calculation. APPROACH To overcome these two issues, a delay symbolic phase transfer entropy (dSPTE) is proposed by developing traditional transfer entropy (TE) to estimate EC for fNIRS. Firstly, the phase time sequence was obtained from the original sequence by the Hilbert transform and state-space reconstruction was realized using a uniform embedding scheme. Then, a symbolization technique was applied based on a neural-gas algorithm to improve its noise robustness. Finally, the EC was calculated on multiple time delay scales to match different inter-region neurotransmission delays. MAIN RESULTS A linear AR model, a nonlinear model and a multivariate hybrid model were introduced to simulate the performance of dSPTE, and the results showed that the accuracy of dSPTE was the highest, up to 74.27%, and specificity was 100% which means no false connectivity. The results confirmed that the dSPTE method realized better noise robustness, higher accuracy, and correct identification even if there was a long delay between series. Finally, we applied dSPTE to fNIRS dataset to analyse the EC during the finger-tapping task, the results showed that EC strength of task state significantly increased compared with the resting state. SIGNIFICANCE The proposed dSPTE method is a promising way to measure the EC for fNIRS. It incorporates the phase information TE with a symbolic process for fNIRS analysis for the first time. It has been confirmed to be noise robust and suitable for the complex network with different coupling delays.
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Affiliation(s)
- Yalin Wang
- Department of Electronic Engineering, Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai, People's Republic of China. Human Phenome Institute, Fudan University, Shanghai, People's Republic of China
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