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Chen C, Liang Y, Xu S, Yi C, Li Y, Chen B, Yang L, Liu Q, Yao D, Li F, Xu P. The dynamic causality brain network reflects whether the working memory is solidified. Cereb Cortex 2024; 34:bhad467. [PMID: 38061696 DOI: 10.1093/cercor/bhad467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 01/19/2024] Open
Abstract
Working memory, which is foundational to higher cognitive function, is the "sketchpad of volitional control." Successful working memory is the inevitable outcome of the individual's active control and manipulation of thoughts and turning them into internal goals during which the causal brain processes information in real time. However, little is known about the dynamic causality among distributed brain regions behind thought control that underpins successful working memory. In our present study, given that correct responses and incorrect ones did not differ in either contralateral delay activity or alpha suppression, further rooting on the high-temporal-resolution EEG time-varying directed network analysis, we revealed that successful working memory depended on both much stronger top-down connections from the frontal to the temporal lobe and bottom-up linkages from the occipital to the temporal lobe, during the early maintenance period, as well as top-down flows from the frontal lobe to the central areas as the delay behavior approached. Additionally, the correlation between behavioral performance and casual interactions increased over time, especially as memory-guided delayed behavior approached. Notably, when using the network metrics as features, time-resolved multiple linear regression of overall behavioral accuracy was exactly achieved as delayed behavior approached. These results indicate that accurate memory depends on dynamic switching of causal network connections and shifting to more task-related patterns during which the appropriate intervention may help enhance memory.
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Affiliation(s)
- Chunli Chen
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yi Liang
- Department of Neurology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 610072, China
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu 610072, China
| | - Shiyun Xu
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Chanlin Yi
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yuqin Li
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Baodan Chen
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Lei Yang
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Qiang Liu
- Institute of Brain and Psychological Science, Sichuan Normal University, Chengdu 610000, China
| | - Dezhong Yao
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Fali Li
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Peng Xu
- MOE Key Lab for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China
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Studenova A, Forster C, Engemann DA, Hensch T, Sanders C, Mauche N, Hegerl U, Loffler M, Villringer A, Nikulin V. Event-related modulation of alpha rhythm explains the auditory P300-evoked response in EEG. eLife 2023; 12:RP88367. [PMID: 38038725 PMCID: PMC10691803 DOI: 10.7554/elife.88367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2023] Open
Abstract
Evoked responses and oscillations represent two major electrophysiological phenomena in the human brain yet the link between them remains rather obscure. Here we show how most frequently studied EEG signals: the P300-evoked response and alpha oscillations (8-12 Hz) can be linked with the baseline-shift mechanism. This mechanism states that oscillations generate evoked responses if oscillations have a non-zero mean and their amplitude is modulated by the stimulus. Therefore, the following predictions should hold: (1) the temporal evolution of P300 and alpha amplitude is similar, (2) spatial localisations of the P300 and alpha amplitude modulation overlap, (3) oscillations are non-zero mean, (4) P300 and alpha amplitude correlate with cognitive scores in a similar fashion. To validate these predictions, we analysed the data set of elderly participants (N=2230, 60-82 years old), using (a) resting-state EEG recordings to quantify the mean of oscillations, (b) the event-related data, to extract parameters of P300 and alpha rhythm amplitude envelope. We showed that P300 is indeed linked to alpha rhythm, according to all four predictions. Our results provide an unifying view on the interdependency of evoked responses and neuronal oscillations and suggest that P300, at least partly, is generated by the modulation of alpha oscillations.
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Affiliation(s)
- Alina Studenova
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Max Planck School of CognitionLeipzigGermany
| | - Carina Forster
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Bernstein Center for Computational Neuroscience, Charité – Universitätsmedizin BerlinBerlinGermany
| | - Denis Alexander Engemann
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann–La Roche Ltd.BaselSwitzerland
| | - Tilman Hensch
- LIFE – Leipzig Research Center for Civilization Diseases, University of LeipzigLeipzigGermany
- Department of Psychology, IU International University of Applied SciencesErfurtGermany
- Department of Psychiatry and Psychotherapy, University of Leipzig Medical CenterLeipzigGermany
| | - Christian Sanders
- LIFE – Leipzig Research Center for Civilization Diseases, University of LeipzigLeipzigGermany
- Department of Psychiatry and Psychotherapy, University of Leipzig Medical CenterLeipzigGermany
| | - Nicole Mauche
- Department of Psychiatry and Psychotherapy, University of Leipzig Medical CenterLeipzigGermany
| | - Ulrich Hegerl
- Department of Psychiatry, Psychosomatics and Psychotherapy, Goethe University FrankfurtFrankfurtGermany
| | - Markus Loffler
- LIFE – Leipzig Research Center for Civilization Diseases, University of LeipzigLeipzigGermany
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of LeipzigLeipzigGermany
| | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Clinic for Cognitive Neurology, University Hospital LeipzigLeipzigGermany
| | - Vadim Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Bernstein Center for Computational Neuroscience BerlinBerlinGermany
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Liu W, Qiu JL, Zheng WL, Lu BL. Comparing Recognition Performance and Robustness of Multimodal Deep Learning Models for Multimodal Emotion Recognition. IEEE Trans Cogn Dev Syst 2022. [DOI: 10.1109/tcds.2021.3071170] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Wei Liu
- Department of Computer Science and Engineering, the Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Brain Science and Technology Research Center, Center for Brain-Like Computing and Machine Intelligence, Qing Yuan Research Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Jie-Lin Qiu
- Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Wei-Long Zheng
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Bao-Liang Lu
- Department of Computer Science and Engineering, the Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Brain Science and Technology Research Center, Center for Brain-Like Computing and Machine Intelligence, Qing Yuan Research Institute, Shanghai Jiao Tong University, Shanghai, China
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Ahumada-Méndez F, Lucero B, Avenanti A, Saracini C, Muñoz-Quezada MT, Cortés-Rivera C, Canales-Johnson A. Affective modulation of cognitive control: A systematic review of EEG studies. Physiol Behav 2022; 249:113743. [DOI: 10.1016/j.physbeh.2022.113743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 01/24/2022] [Accepted: 02/11/2022] [Indexed: 10/19/2022]
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