1
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Shintaki R, Tanaka D, Suzuki S, Yoshimoto T, Sadato N, Chikazoe J, Jimura K. Continuous decision to wait for a future reward is guided by fronto-hippocampal anticipatory dynamics. Cereb Cortex 2024; 34:bhae217. [PMID: 38798003 DOI: 10.1093/cercor/bhae217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 05/02/2024] [Accepted: 05/08/2024] [Indexed: 05/29/2024] Open
Abstract
Deciding whether to wait for a future reward is crucial for surviving in an uncertain world. While seeking rewards, agents anticipate a reward in the present environment and constantly face a trade-off between staying in their environment or leaving it. It remains unclear, however, how humans make continuous decisions in such situations. Here, we show that anticipatory activity in the anterior prefrontal cortex, ventrolateral prefrontal cortex, and hippocampus underpins continuous stay-leave decision-making. Participants awaited real liquid rewards available after tens of seconds, and their continuous decision was tracked by dynamic brain activity associated with the anticipation of a reward. Participants stopped waiting more frequently and sooner after they experienced longer delays and received smaller rewards. When the dynamic anticipatory brain activity was enhanced in the anterior prefrontal cortex, participants remained in their current environment, but when this activity diminished, they left the environment. Moreover, while experiencing a delayed reward in a novel environment, the ventrolateral prefrontal cortex and hippocampus showed anticipatory activity. Finally, the activity in the anterior prefrontal cortex and ventrolateral prefrontal cortex was enhanced in participants adopting a leave strategy, whereas those remaining stationary showed enhanced hippocampal activity. Our results suggest that fronto-hippocampal anticipatory dynamics underlie continuous decision-making while anticipating a future reward.
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Affiliation(s)
- Reiko Shintaki
- Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan
| | - Daiki Tanaka
- Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan
| | - Shinsuke Suzuki
- Centre for Brain, Mind and Markets, The University of Melbourne, Grattan Street, Parkville, Victoria, 3010, Australia
- Faculty of Social Data Science and HIAS Brain Research Center, Hitotsubashi University, 2-1 Naka, Kunitachi, 186-8601, Japan
| | - Takaaki Yoshimoto
- Research Organization of Science and Technology, Ritsumeikan University, 1-1-1, Nojihigashi, Kusatsu, 525-8577, Japan
- Section of Brain Function Information, Supportive Center for Brain Research, National Institute for Physiological Sciences, 38 Nishigonaka, Myodaiji, Okazaki, 444-8585, Japan
| | - Norihiro Sadato
- Research Organization of Science and Technology, Ritsumeikan University, 1-1-1, Nojihigashi, Kusatsu, 525-8577, Japan
- Section of Brain Function Information, Supportive Center for Brain Research, National Institute for Physiological Sciences, 38 Nishigonaka, Myodaiji, Okazaki, 444-8585, Japan
| | - Junichi Chikazoe
- Section of Brain Function Information, Supportive Center for Brain Research, National Institute for Physiological Sciences, 38 Nishigonaka, Myodaiji, Okazaki, 444-8585, Japan
- Araya, Inc., 1-11 Kanda Sakuma-cho, Chiyoda, Tokyo, 101-0025, Japan
| | - Koji Jimura
- Department of Informatics, Gunma University, 4-2 Aramaki-machi, Maebashi, 371-8510, Japan
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2
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Ezzyat Y, Kragel JE, Solomon EA, Lega BC, Aronson JP, Jobst BC, Gross RE, Sperling MR, Worrell GA, Sheth SA, Wanda PA, Rizzuto DS, Kahana MJ. Functional and anatomical connectivity predict brain stimulation's mnemonic effects. Cereb Cortex 2024; 34:bhad427. [PMID: 38041253 PMCID: PMC10793570 DOI: 10.1093/cercor/bhad427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 10/05/2023] [Accepted: 10/06/2023] [Indexed: 12/03/2023] Open
Abstract
Closed-loop direct brain stimulation is a promising tool for modulating neural activity and behavior. However, it remains unclear how to optimally target stimulation to modulate brain activity in particular brain networks that underlie particular cognitive functions. Here, we test the hypothesis that stimulation's behavioral and physiological effects depend on the stimulation target's anatomical and functional network properties. We delivered closed-loop stimulation as 47 neurosurgical patients studied and recalled word lists. Multivariate classifiers, trained to predict momentary lapses in memory function, triggered the stimulation of the lateral temporal cortex (LTC) during the study phase of the task. We found that LTC stimulation specifically improved memory when delivered to targets near white matter pathways. Memory improvement was largest for targets near white matter that also showed high functional connectivity to the brain's memory network. These targets also reduced low-frequency activity in this network, an established marker of successful memory encoding. These data reveal how anatomical and functional networks mediate stimulation's behavioral and physiological effects, provide further evidence that closed-loop LTC stimulation can improve episodic memory, and suggest a method for optimizing neuromodulation through improved stimulation targeting.
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Affiliation(s)
- Youssef Ezzyat
- Dept. of Psychology, Wesleyan University, Middletown, CT 06459, USA
| | - James E Kragel
- Dept. of Neurology, University of Chicago, Chicago, IL 60637, USA
| | - Ethan A Solomon
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bradley C Lega
- Dept. of Neurosurgery, University of Texas Southwestern, Dallas, TX 75390, USA
| | - Joshua P Aronson
- Dept. of Neurosurgery, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Barbara C Jobst
- Dept. of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756, USA
| | - Robert E Gross
- Dept. of Neurosurgery, Emory University Hospital, Atlanta, GA 30322, USA
| | - Michael R Sperling
- Dept. of Neurology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA 19107, USA
| | | | - Sameer A Sheth
- Dept. of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Paul A Wanda
- Dept. of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daniel S Rizzuto
- Dept. of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael J Kahana
- Dept. of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
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3
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Capouskova K, Zamora‐López G, Kringelbach ML, Deco G. Integration and segregation manifolds in the brain ensure cognitive flexibility during tasks and rest. Hum Brain Mapp 2023; 44:6349-6363. [PMID: 37846551 PMCID: PMC10681658 DOI: 10.1002/hbm.26511] [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: 05/23/2023] [Revised: 09/14/2023] [Accepted: 09/25/2023] [Indexed: 10/18/2023] Open
Abstract
Adapting to a constantly changing environment requires the human brain to flexibly switch among many demanding cognitive tasks, processing both specialized and integrated information associated with the activity in functional networks over time. In this study, we investigated the nature of the temporal alternation between segregated and integrated states in the brain during rest and six cognitive tasks using functional MRI. We employed a deep autoencoder to explore the 2D latent space associated with the segregated and integrated states. Our results show that the integrated state occupies less space in the latent space manifold compared to the segregated states. Moreover, the integrated state is characterized by lower entropy of occupancy than the segregated state, suggesting that integration plays a consolidating role, while segregation may serve as cognitive expertness. Comparing rest and the tasks, we found that rest exhibits higher entropy of occupancy, indicating a more random wandering of the mind compared to the expected focus during task performance. Our study demonstrates that both transient, short-lived integrated and segregated states are present during rest and task performance, flexibly switching between them, with integration serving as information compression and segregation related to information specialization.
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Affiliation(s)
- Katerina Capouskova
- Center for Brain and Cognition, Computational Neuroscience Group, DTICUniversitat Pompeu FabraBarcelonaSpain
| | - Gorka Zamora‐López
- Center for Brain and Cognition, Computational Neuroscience Group, DTICUniversitat Pompeu FabraBarcelonaSpain
| | - Morten L. Kringelbach
- Department of PsychiatryUniversity of OxfordOxfordUnited Kingdom
- Center for Music in the Brain, Department of Clinical MedicineAarhus UniversityAarhusDenmark
- Centre for Eudaimonia and Human Flourishing, Linacre CollegeUniversity of OxfordOxfordUnited Kingdom
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, DTICUniversitat Pompeu FabraBarcelonaSpain
- Institució Catalana de Recerca i Estudis Avançats (ICREA)BarcelonaSpain
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4
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Albrecht C, van de Vijver R, Bellebaum C. Learning new words via feedback-Association between feedback-locked ERPs and recall performance-An exploratory study. Psychophysiology 2023; 60:e14324. [PMID: 37144796 DOI: 10.1111/psyp.14324] [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: 05/18/2022] [Revised: 04/13/2023] [Accepted: 04/18/2023] [Indexed: 05/06/2023]
Abstract
Feedback learning is thought to involve the dopamine system and its projection sites in the basal ganglia and anterior cingulate cortex (ACC), regions associated with procedural learning. Under certain conditions, such as when feedback is delayed, feedback-locked activation is pronounced in the medial temporal lobe (MTL), which is associated with declarative learning. In event-related potential research, the feedback-related negativity (FRN) has been linked to immediate feedback processing, while the N170, possibly reflecting MTL activity, has been related to delayed feedback processing. In the current study, we performed an exploratory investigation on the relation between N170 and FRN amplitude and memory performance in a test for declarative memory (free recall), also exploring the role of feedback delay. To this end, we adapted a paradigm in which participants learned associations between non-objects and non-words with either immediate or delayed feedback, and added a subsequent free recall test. We indeed found that N170, but not FRN amplitudes, depended on later free recall performance, with smaller amplitudes for later remembered non-words. In an additional analysis with memory performance as dependent variable, the N170, but not the FRN amplitude predicted free recall, modulated by feedback timing and valence. This finding shows that the N170 reflects an important process during feedback processing, possibly related to expectations and their violation, but is distinct from the process reflected by the FRN.
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Affiliation(s)
- Christine Albrecht
- Institute of Experimental Psychology, Heinrich Heine University, Düsseldorf, Germany
| | - Ruben van de Vijver
- Institute of Linguistics and Information Science, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christian Bellebaum
- Institute of Experimental Psychology, Heinrich Heine University, Düsseldorf, Germany
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5
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Li Z, Athwal D, Lee HL, Sah P, Opazo P, Chuang KH. Locating causal hubs of memory consolidation in spontaneous brain network in male mice. Nat Commun 2023; 14:5399. [PMID: 37669938 PMCID: PMC10480429 DOI: 10.1038/s41467-023-41024-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 08/17/2023] [Indexed: 09/07/2023] Open
Abstract
Memory consolidation after learning involves spontaneous, brain-wide network reorganization during rest and sleep, but how this is achieved is still poorly understood. Current theory suggests that the hippocampus is pivotal for this reshaping of connectivity. Using fMRI in male mice, we identify that a different set of spontaneous networks and their hubs are instrumental in consolidating memory during post-learning rest. We found that two types of spatial memory training invoke distinct functional connections, but that a network of the sensory cortex and subcortical areas is common for both tasks. Furthermore, learning increased brain-wide network integration, with the prefrontal, striatal and thalamic areas being influential for this network-level reconfiguration. Chemogenetic suppression of each hub identified after learning resulted in retrograde amnesia, confirming the behavioral significance. These results demonstrate the causal and functional roles of resting-state network hubs in memory consolidation and suggest that a distributed network beyond the hippocampus subserves this process.
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Affiliation(s)
- Zengmin Li
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Dilsher Athwal
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Hsu-Lei Lee
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Pankaj Sah
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Joint Center for Neuroscience and Neural Engineering, and Department of Biology, Southern University of Science and Technology, Shenzhen, Guangdong, PR China
| | - Patricio Opazo
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Clem Jones Centre for Ageing Dementia Research, The University of Queensland, Brisbane, QLD, Australia
- UK Dementia Research Institute, Centre for Discovery Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Kai-Hsiang Chuang
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia.
- Centre of Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia.
- Australian Research Council Training Centre for Innovation in Biomedical Imaging Technology, Brisbane, QLD, Australia.
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6
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Panda R, Vanhaudenhuyse A, Piarulli A, Annen J, Demertzi A, Alnagger N, Chennu S, Laureys S, Faymonville ME, Gosseries O. Altered Brain Connectivity and Network Topological Organization in a Non-ordinary State of Consciousness Induced by Hypnosis. J Cogn Neurosci 2023; 35:1394-1409. [PMID: 37315333 DOI: 10.1162/jocn_a_02019] [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: 06/16/2023]
Abstract
Hypnosis has been shown to be of clinical utility; however, its underlying neural mechanisms remain unclear. This study aims to investigate altered brain dynamics during the non-ordinary state of consciousness induced by hypnosis. We studied high-density EEG in 9 healthy participants during eyes-closed wakefulness and during hypnosis, induced by a muscle relaxation and eyes fixation procedure. Using hypotheses based on internal and external awareness brain networks, we assessed region-wise brain connectivity between six ROIs (right and left frontal, right and left parietal, upper and lower midline regions) at the scalp level and compared across conditions. Data-driven, graph-theory analyses were also carried out to characterize brain network topology in terms of brain network segregation and integration. During hypnosis, we observed (1) increased delta connectivity between left and right frontal, as well as between right frontal and parietal regions; (2) decreased connectivity for alpha (between right frontal and parietal and between upper and lower midline regions) and beta-2 bands (between upper midline and right frontal, frontal and parietal, also between upper and lower midline regions); and (3) increased network segregation (short-range connections) in delta and alpha bands, and increased integration (long-range connections) in beta-2 band. This higher network integration and segregation was measured bilaterally in frontal and right parietal electrodes, which were identified as central hub regions during hypnosis. This modified connectivity and increased network integration-segregation properties suggest a modification of the internal and external awareness brain networks that may reflect efficient cognitive-processing and lower incidences of mind-wandering during hypnosis.
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Affiliation(s)
| | | | | | - Jitka Annen
- University of Liège, Belgium
- University Hospital of Liège, Belgium
| | | | - Naji Alnagger
- University of Liège, Belgium
- University Hospital of Liège, Belgium
| | | | - Steven Laureys
- University of Liège, Belgium
- University Hospital of Liège, Belgium
- Laval University, Québec, Canada
| | | | - Olivia Gosseries
- University of Liège, Belgium
- University Hospital of Liège, Belgium
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7
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Ezzyat Y, Kragel JE, Solomon EA, Lega BC, Aronson JP, Jobst BC, Gross RE, Sperling MR, Worrell GA, Sheth SA, Wanda PA, Rizzuto DS, Kahana MJ. Functional and anatomical connectivity predict brain stimulation's mnemonic effects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.27.550851. [PMID: 37609181 PMCID: PMC10441352 DOI: 10.1101/2023.07.27.550851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Closed-loop direct brain stimulation is a promising tool for modulating neural activity and behavior. However, it remains unclear how to optimally target stimulation to modulate brain activity in particular brain networks that underlie particular cognitive functions. Here, we test the hypothesis that stimulation's behavioral and physiological effects depend on the stimulation target's anatomical and functional network properties. We delivered closed-loop stimulation as 47 neurosurgical patients studied and recalled word lists. Multivariate classifiers, trained to predict momentary lapses in memory function, triggered stimulation of the lateral temporal cortex (LTC) during the study phase of the task. We found that LTC stimulation specifically improved memory when delivered to targets near white matter pathways. Memory improvement was largest for targets near white matter that also showed high functional connectivity to the brain's memory network. These targets also reduced low-frequency activity in this network, an established marker of successful memory encoding. These data reveal how anatomical and functional networks mediate stimulation's behavioral and physiological effects, provide further evidence that closed-loop LTC stimulation can improve episodic memory, and suggest a method for optimizing neuromodulation through improved stimulation targeting.
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Affiliation(s)
- Youssef Ezzyat
- Dept. of Psychology, Wesleyan University, Middletown CT 06459
| | | | - Ethan A. Solomon
- Perelman School of Medicine, University of Pennsylvania, Philadelphia PA 19104
| | - Bradley C. Lega
- Dept. of Neurosurgery, University of Texas Southwestern, Dallas TX 75390
| | - Joshua P. Aronson
- Dept. of Neurosurgery, Dartmouth-Hitchcock Medical Center, Lebanon NH 03756
| | - Barbara C. Jobst
- Dept. of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon NH 03756
| | - Robert E. Gross
- Dept. of Neurosurgery, Emory University Hospital, Atlanta GA 30322
| | - Michael R. Sperling
- Dept. of Neurology, Thomas Jefferson University Hospital, Philadelphia PA 19107
| | | | - Sameer A. Sheth
- Dept. of Neurosurgery, Columbia University Medical Center, New York, NY 10032
| | - Paul A. Wanda
- Dept. of Psychology, University of Pennsylvania, Philadelphia PA 19104
| | - Daniel S. Rizzuto
- Dept. of Psychology, University of Pennsylvania, Philadelphia PA 19104
| | - Michael J. Kahana
- Dept. of Psychology, University of Pennsylvania, Philadelphia PA 19104
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8
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Li Y, Wang Y, Chen A. Flexible integration and segregation of large-scale networks during adaptive control. Behav Brain Res 2023; 451:114521. [PMID: 37268251 DOI: 10.1016/j.bbr.2023.114521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 05/08/2023] [Accepted: 05/30/2023] [Indexed: 06/04/2023]
Abstract
Adaptive control characterizes the dynamic adjustment of cognitive control to changing environmental demand, and has obtained growing interests in its neural mechanism for the past two decades. Recent years, interpreting network reconfiguration in terms of integration and segregation has been proved to shed light on neural structure underlying various cognitive tasks. However, the relationship between network architecture and adaptive control remains unclear. Here, we quantified the network integration (global efficiency, participation coefficient, inter-subnetwork efficiency) and segregation (local efficiency, modularity) in the whole-brain and analyzed how these graph theory metrics were modulated by adaptive control. The results showed that the integration of the cognitive control network (the fronto-parietal network, FPN), the visual network (VIN) and the sensori-motor network (SMN) was significantly improved when conflict was rare, so as to cope with the incongruent trials of high cognitive control demands. Additionally, as the conflict proportion increased, the segregation of the cingulo-opercular network (CON) and the default mode network (DMN) significantly enhanced, which may contribute to specialized functioning or automatic processing, and help to solve conflict in a less resource-intensive mode. Finally, using graph metrics as features, the multivariate classifier reliably predicted the context condition. These results demonstrate how large-scale brain networks support adaptive control through flexible integration and segregation.
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Affiliation(s)
- Yilu Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yanqing Wang
- Institute of Psychology, Chinese Academy of Sciences and University of Chinese Academy of Sciences, Beijing 100101, China
| | - Antao Chen
- School of Psychology, Center for Exercise and Brain Science, Shanghai University of Sport, Shanghai 200438, China.
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9
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Watanabe N, Miyoshi K, Jimura K, Shimane D, Keerativittayayut R, Nakahara K, Takeda M. Multimodal deep neural decoding reveals highly resolved spatiotemporal profile of visual object representation in humans. Neuroimage 2023; 275:120164. [PMID: 37169115 DOI: 10.1016/j.neuroimage.2023.120164] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 05/02/2023] [Accepted: 05/09/2023] [Indexed: 05/13/2023] Open
Abstract
Perception and categorization of objects in a visual scene are essential to grasp the surrounding situation. Recently, neural decoding schemes, such as machine learning in functional magnetic resonance imaging (fMRI), has been employed to elucidate the underlying neural mechanisms. However, it remains unclear as to how spatially distributed brain regions temporally represent visual object categories and sub-categories. One promising strategy to address this issue is neural decoding with concurrently obtained neural response data of high spatial and temporal resolution. In this study, we explored the spatial and temporal organization of visual object representations using concurrent fMRI and electroencephalography (EEG), combined with neural decoding using deep neural networks (DNNs). We hypothesized that neural decoding by multimodal neural data with DNN would show high classification performance in visual object categorization (faces or non-face objects) and sub-categorization within faces and objects. Visualization of the fMRI DNN was more sensitive than that in the univariate approach and revealed that visual categorization occurred in brain-wide regions. Interestingly, the EEG DNN valued the earlier phase of neural responses for categorization and the later phase of neural responses for sub-categorization. Combination of the two DNNs improved the classification performance for both categorization and sub-categorization compared with fMRI DNN or EEG DNN alone. These deep learning-based results demonstrate a categorization principle in which visual objects are represented in a spatially organized and coarse-to-fine manner, and provide strong evidence of the ability of multimodal deep learning to uncover spatiotemporal neural machinery in sensory processing.
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Affiliation(s)
- Noriya Watanabe
- Research Center for Brain Communication, Kochi University of Technology, Kami, Kochi, 782-8502, Japan
| | - Kosuke Miyoshi
- Narrative Nights, Inc., Yokohama, Kanagawa, 236-0011, Japan
| | - Koji Jimura
- Research Center for Brain Communication, Kochi University of Technology, Kami, Kochi, 782-8502, Japan; Department of Informatics, Gunma University, Maebashi, Gunma, 371-8510, Japan
| | - Daisuke Shimane
- Research Center for Brain Communication, Kochi University of Technology, Kami, Kochi, 782-8502, Japan
| | - Ruedeerat Keerativittayayut
- Research Center for Brain Communication, Kochi University of Technology, Kami, Kochi, 782-8502, Japan; Chulabhorn Royal Academy, Bangkok, 10210, Thailand
| | - Kiyoshi Nakahara
- Research Center for Brain Communication, Kochi University of Technology, Kami, Kochi, 782-8502, Japan
| | - Masaki Takeda
- Research Center for Brain Communication, Kochi University of Technology, Kami, Kochi, 782-8502, Japan.
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10
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Amer T, Davachi L. Extra-hippocampal contributions to pattern separation. eLife 2023; 12:82250. [PMID: 36972123 PMCID: PMC10042541 DOI: 10.7554/elife.82250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 03/21/2023] [Indexed: 03/29/2023] Open
Abstract
Pattern separation, or the process by which highly similar stimuli or experiences in memory are represented by non-overlapping neural ensembles, has typically been ascribed to processes supported by the hippocampus. Converging evidence from a wide range of studies, however, suggests that pattern separation is a multistage process supported by a network of brain regions. Based on this evidence, considered together with related findings from the interference resolution literature, we propose the 'cortico-hippocampal pattern separation' (CHiPS) framework, which asserts that brain regions involved in cognitive control play a significant role in pattern separation. Particularly, these regions may contribute to pattern separation by (1) resolving interference in sensory regions that project to the hippocampus, thus regulating its cortical input, or (2) directly modulating hippocampal processes in accordance with task demands. Considering recent interest in how hippocampal operations are modulated by goal states likely represented and regulated by extra-hippocampal regions, we argue that pattern separation is similarly supported by neocortical-hippocampal interactions.
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Affiliation(s)
- Tarek Amer
- Department of Psychology, University of Victoria, Victoria, Canada
| | - Lila Davachi
- Department of Psychology, Columbia University, New York, United States
- Nathan Kline Research Institute, Orangeburg, United States
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11
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Noro Y, Li R, Matsui T, Jimura K. A method for reconstruction of interpretable brain networks from transient synchronization in resting-state BOLD fluctuations. Front Neuroinform 2023; 16:960607. [PMID: 36713290 PMCID: PMC9878402 DOI: 10.3389/fninf.2022.960607] [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: 06/03/2022] [Accepted: 12/22/2022] [Indexed: 01/13/2023] Open
Abstract
Resting-state (rs) fMRI has been widely used to examine brain-wide large-scale spatiotemporal architectures, known as resting-state networks (RSNs). Recent studies have focused on the temporally evolving characteristics of RSNs, but it is unclear what temporal characteristics are reflected in the networks. To address this issue, we devised a novel method for voxel-based visualization of spatiotemporal characteristics of rs-fMRI with a time scale of tens of seconds. We first extracted clusters of dominant activity-patterns using a region-of-interest approach and then used these temporal patterns of the clusters to obtain voxel-based activation patterns related to the clusters. We found that activation patterns related to the clusters temporally evolved with a characteristic temporal structure and showed mutual temporal alternations over minutes. The voxel-based representation allowed the decoding of activation patterns of the clusters in rs-fMRI using a meta-analysis of functional activations. The activation patterns of the clusters were correlated with behavioral measures. Taken together, our analysis highlights a novel approach to examine brain activity dynamics during rest.
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Affiliation(s)
- Yusuke Noro
- Department of Biosciences and Informatics, Keio University, Yokohama, Japan
| | - Ruixiang Li
- Department of Physiology, The University of Tokyo School of Medicine, Tokyo, Japan
| | - Teppei Matsui
- Department of Biology, Okayama University, Okayama, Japan,PRESTO, Japan Science and Technology Agency, Tokyo, Japan,Teppei Matsui ✉
| | - Koji Jimura
- Department of Informatics, Gunma University, Maebashi, Japan,*Correspondence: Koji Jimura ✉
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12
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The Stroop effect involves an excitatory-inhibitory fronto-cerebellar loop. Nat Commun 2023; 14:27. [PMID: 36631460 PMCID: PMC9834394 DOI: 10.1038/s41467-022-35397-w] [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: 02/20/2022] [Accepted: 11/30/2022] [Indexed: 01/13/2023] Open
Abstract
The Stroop effect is a classical, well-known behavioral phenomenon in humans that refers to robust interference between language and color information. It remains unclear, however, when the interference occurs and how it is resolved in the brain. Here we show that the Stroop effect occurs during perception of color-word stimuli and involves a cross-hemispheric, excitatory-inhibitory loop functionally connecting the lateral prefrontal cortex and cerebellum. Participants performed a Stroop task and a non-verbal control task (which we term the Swimmy task), and made a response vocally or manually. The Stroop effect involved the lateral prefrontal cortex in the left hemisphere and the cerebellum in the right hemisphere, independently of the response type; such lateralization was absent during the Swimmy task, however. Moreover, the prefrontal cortex amplified cerebellar activity, whereas the cerebellum suppressed prefrontal activity. This fronto-cerebellar loop may implement language and cognitive systems that enable goal-directed behavior during perceptual conflicts.
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13
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Adaptive changes in sensorimotor processing in patients with acute low back pain. Sci Rep 2022; 12:21741. [PMID: 36526879 PMCID: PMC9758154 DOI: 10.1038/s41598-022-26174-2] [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: 09/15/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
In low back pain (LBP), primary care and secondary prevention of recurrent and persistent LBP are not always successful. Enhanced understanding of neural mechanisms of sensorimotor processing and pain modulation in patients with acute LBP is mandatory. This explorative fMRI study investigated sensorimotor processing due to mechanosensory stimulation of the lumbar spine. We studied 19 adult patients with acute LBP (< 4 weeks of an acute episode) and 23 healthy controls. On a numeric rating scale, patients reported moderate mean pain intensity of 4.5 out of 10, while LBP-associated disability indicated mild mean disability. The event-related fMRI analysis yielded no between-group differences. However, the computation of functional connectivity resulted in adaptive changes in networks involved in sensorimotor processing in the patient group: Connectivity strength was decreased in the salience and cerebellar networks but increased in the limbic and parahippocampal networks. Timewise, these results indicate that early connectivity changes might reflect adaptive physiological processes in an episode of acute LBP. These findings raise intriguing questions regarding their role in pain persistence and recurrences of LBP, particularly concerning the multiple consequences of acute LBP pain. Advanced understanding of neural mechanisms of processing non-painful mechanosensations in LBP may also improve therapeutic approaches.
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14
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Poh JH, Vu MAT, Stanek JK, Hsiung A, Egner T, Adcock RA. Hippocampal convergence during anticipatory midbrain activation promotes subsequent memory formation. Nat Commun 2022; 13:6729. [PMID: 36344524 PMCID: PMC9640528 DOI: 10.1038/s41467-022-34459-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 10/25/2022] [Indexed: 11/09/2022] Open
Abstract
The hippocampus has been a focus of memory research since H.M's surgery abolished his ability to form new memories, yet its mechanistic role in memory remains debated. Here, we identify a candidate memory mechanism: an anticipatory hippocampal "convergence state", observed while awaiting valuable information, and which predicts subsequent learning. During fMRI, participants viewed trivia questions eliciting high or low curiosity, followed seconds later by its answer. We reasoned that encoding success requires a confluence of conditions, so that hippocampal states more conducive to memory formation should converge in state space. To operationalize convergence of neural states, we quantified the typicality of multivoxel patterns in the medial temporal lobes during anticipation and encoding of trivia answers. We found that the typicality of anticipatory hippocampal patterns increased during high curiosity. Crucially, anticipatory hippocampal pattern typicality increased with dopaminergic midbrain activation and uniquely accounted for the association between midbrain activation and subsequent recall. We propose that hippocampal convergence states may complete a cascade from motivation and midbrain activation to memory enhancement, and may be a general predictor of memory formation.
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Affiliation(s)
- Jia-Hou Poh
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA.
| | - Mai-Anh T Vu
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
- Department of Neurobiology, Duke University, Durham, NC, USA
- Department of Psychological & Brain Sciences, Boston University, Boston, MA, USA
| | - Jessica K Stanek
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
- Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
| | - Abigail Hsiung
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
- Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
| | - Tobias Egner
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
- Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
| | - R Alison Adcock
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA.
- Department of Neurobiology, Duke University, Durham, NC, USA.
- Department of Psychology & Neuroscience, Duke University, Durham, NC, USA.
- Department of Psychiatry & Behavioral Sciences, Duke University, Durham, NC, USA.
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15
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Yin S, Li Y, Chen A. Functional coupling between frontoparietal control subnetworks bridges the default and dorsal attention networks. Brain Struct Funct 2022; 227:2243-2260. [PMID: 35751677 DOI: 10.1007/s00429-022-02517-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 05/23/2022] [Indexed: 12/25/2022]
Abstract
The frontoparietal control network (FPCN) plays a central role in tuning connectivity between brain networks to achieve integrated cognitive processes. It has been proposed that two subnetworks within the FPCN separately regulate two antagonistic networks: the FPCNa is connected to the default network (DN) that deals with internally oriented introspective processes, whereas the FPCNb is connected to the dorsal attention network (DAN) that deals with externally oriented perceptual attention. However, cooperation between the DN and DAN induced by distinct task demands has not been well-studied. Here, we characterized the dynamic cooperation among the DN, DAN, and two FPCN subnetworks in a task in which internally oriented self-referential processing could facilitate externally oriented visual working memory. Functional connectivity analysis showed enhanced coupling of a circuit from the DN to the FPCNa, then to the FPCNb, and finally to the DAN when the self-referential processing improved memory recognition in high self-referential conditions. The direct connection between the DN and DAN was not enhanced. This circuit could be reflected by an increased chain-mediating effect of the FPCNa and the FPCNb between the DN and DAN in high self-referential conditions. Graph analysis revealed that high self-referential conditions were accompanied by increased global and local efficiencies, and the increases were mainly driven by the increased efficiency of FPCN nodes. Together, our findings extend prior observations and indicate that the coupling between the two FPCN subnetworks serves as a bridge between the DN and DAN, supporting the interaction between internally oriented and externally oriented processes.
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Affiliation(s)
- Shouhang Yin
- School of Mathematics and Statistics, Southwest University, Chongqing, 400715, China
| | - Yilu Li
- Faculty of Psychology, Southwest University, Chongqing, 400715, China
| | - Antao Chen
- School of Psychology, Shanghai University of Sport, Shanghai, China.
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16
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Capogna E, Sneve MH, Raud L, Folvik L, Ness HT, Walhovd KB, Fjell AM, Vidal-Piñeiro D. Whole-brain connectivity during encoding: age-related differences and associations with cognitive and brain structural decline. Cereb Cortex 2022; 33:68-82. [PMID: 35193146 PMCID: PMC9758575 DOI: 10.1093/cercor/bhac053] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 01/25/2022] [Accepted: 01/26/2022] [Indexed: 11/14/2022] Open
Abstract
There is a limited understanding of age differences in functional connectivity during memory encoding. In the present study, a sample of cognitively healthy adult participants (n = 488, 18-81 years), a subsample of whom had longitudinal cognitive and brain structural data spanning on average 8 years back, underwent functional magnetic resonance imaging while performing an associative memory encoding task. We investigated (1) age-related differences in whole-brain connectivity during memory encoding; (2) whether encoding connectivity patterns overlapped with the activity signatures of specific cognitive processes, and (3) whether connectivity associated with memory encoding related to longitudinal brain structural and cognitive changes. Age was associated with lower intranetwork connectivity among cortical networks and higher internetwork connectivity between networks supporting higher level cognitive functions and unimodal and attentional areas during encoding. Task-connectivity between mediotemporal and posterior parietal regions-which overlapped with areas involved in mental imagery-was related to better memory performance only in older age. The connectivity patterns supporting memory performance in older age reflected preservation of thickness of the medial temporal cortex. The results are more in accordance with a maintenance rather than a compensation account.
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Affiliation(s)
- Elettra Capogna
- Corresponding author: Department of Psychology, University of Oslo, 0317 Oslo, Norway.
| | - Markus H Sneve
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Liisa Raud
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Line Folvik
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Hedda T Ness
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
| | - Kristine B Walhovd
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway,Department of Radiology and Nuclear Medicine, Oslo University Hospital, 0424 Oslo, Norway
| | - Anders M Fjell
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway,Department of Radiology and Nuclear Medicine, Oslo University Hospital, 0424 Oslo, Norway
| | - Didac Vidal-Piñeiro
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373 Oslo, Norway
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17
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Wang Y, Hu X, Li Y. Investigating cognitive flexibility deficit in schizophrenia using task-based whole-brain functional connectivity. Front Psychiatry 2022; 13:1069036. [PMID: 36479558 PMCID: PMC9719952 DOI: 10.3389/fpsyt.2022.1069036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 11/07/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Cognitive flexibility is a core cognitive control function supported by the brain networks of the whole-brain. Schizophrenic patients show deficits in cognitive flexibility in conditions such as task-switching. A large number of neuroimaging studies have revealed abnormalities in local brain activations associated with deficits in cognitive flexibility in schizophrenia, but the relationship between impaired cognitive flexibility and the whole-brain functional connectivity (FC) pattern is unclear. METHOD We investigated the task-based functional connectivity of the whole-brain in patients with schizophrenia and healthy controls during task-switching. Multivariate pattern analysis (MVPA) was utilized to investigate whether the FC pattern can be used as a feature to discriminate schizophrenia patients from healthy controls. Graph theory analysis was further used to quantify the degrees of integration and segregation in the whole-brain networks to interpret the different reconfiguration patterns of brain networks in schizophrenia patients and healthy controls. RESULTS The results showed that the FC pattern classified schizophrenia patients and healthy controls with significant accuracy. Moreover, the altered whole-brain functional connectivity pattern was driven by a lower degree of network integration and segregation in schizophrenia, indicating that both global and local information transfers at the entire-network level were less efficient in schizophrenia patients than in healthy controls during task-switching processing. CONCLUSION These results investigated the group differences in FC profiles during task-switching and not only elucidated that FC patterns are changed in schizophrenic patients, suggesting that task-based FC could be used as a potential neuromarker to discriminate schizophrenia patients from healthy controls in cognitive flexibility but also provide increased insight into the brain network organization that may contribute to impaired cognitive flexibility.
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Affiliation(s)
- Yanqing Wang
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Xueping Hu
- School of Linguistic Science and Art, Jiangsu Normal University, Xuzhou, China.,Key Laboratory of Language and Cognitive Neuroscience of Jiangsu Province, Collaborative Innovation Center for Language Ability, Xuzhou, China
| | - Yilu Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
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18
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Lin Q, Yoo K, Shen X, Constable TR, Chun MM. Functional Connectivity during Encoding Predicts Individual Differences in Long-Term Memory. J Cogn Neurosci 2021; 33:2279-2296. [PMID: 34272957 PMCID: PMC8497062 DOI: 10.1162/jocn_a_01759] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
What is the neural basis of individual differences in the ability to hold information in long-term memory (LTM)? Here, we first characterize two whole-brain functional connectivity networks based on fMRI data acquired during an n-back task that robustly predict individual differences in two important forms of LTM, recognition and recollection. We then focus on the recognition memory model and contrast it with a working memory model. Although functional connectivity during the n-back task also predicts working memory performance and the two networks have some shared components, they are also largely distinct from each other: The recognition memory model performance remains robust when we control for working memory, and vice versa. Functional connectivity only within regions traditionally associated with LTM formation, such as the medial temporal lobe and those that show univariate subsequent memory effect, have little predictive power for both forms of LTM. Interestingly, the interactions between these regions and other brain regions play a more substantial role in predicting recollection memory than recognition memory. These results demonstrate that individual differences in LTM are dependent on the configuration of a whole-brain functional network including but not limited to regions associated with LTM during encoding and that such a network is separable from what supports the retention of information in working memory.
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19
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Song H, Rosenberg MD. Predicting attention across time and contexts with functional brain connectivity. Curr Opin Behav Sci 2021. [DOI: 10.1016/j.cobeha.2020.12.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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20
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Banjac S, Roger E, Pichat C, Cousin E, Mosca C, Lamalle L, Krainik A, Kahane P, Baciu M. Reconfiguration dynamics of a language-and-memory network in healthy participants and patients with temporal lobe epilepsy. Neuroimage Clin 2021; 31:102702. [PMID: 34090125 PMCID: PMC8186554 DOI: 10.1016/j.nicl.2021.102702] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 04/21/2021] [Accepted: 05/14/2021] [Indexed: 12/03/2022]
Abstract
Current theoretical frameworks suggest that human behaviors are based on strong and complex interactions between cognitive processes such as those underlying language and memory functions in normal and neurological populations. We were interested in assessing the dynamic cerebral substrate of such interaction between language and declarative memory, as the composite function, in healthy controls (HC, N = 19) and patients with temporal lobe epilepsy (TLE, N = 16). Our assumption was that the language and declarative memory integration is based on a language-and-memory network (LMN) that is dynamic and reconfigures according to task demands and brain status. Therefore, we explored two types of LMN dynamics, a state reconfiguration (intrinsic resting-state compared to extrinsic state assessed with a sentence recall task) and a reorganization of state reconfiguration (TLE compared to HC). The dynamics was evaluated in terms of segregation (community or module detection) and integration (connector hubs). In HC, the level of segregation was the same in both states and the mechanism of LMN state reconfiguration was shown through module change of key language and declarative memory regions with integrative roles. In TLE patients, the reorganization of LMN state reconfiguration was reflected in segregation increase and extrinsic modules that were based on shorter-distance connections. While lateral and mesial temporal regions enabled state reconfiguration in HC, these regions showed reduced flexibility in TLE. We discuss our results in a connectomic perspective and propose a dynamic model of language and declarative memory functioning. We claim that complex and interactive cognitive functions, such as language and declarative memory, should be investigated dynamically, considering the interaction between cognitive networks.
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Affiliation(s)
- Sonja Banjac
- Univ. Grenoble Alpes, CNRS LPNC UMR 5105, 38000 Grenoble, France
| | - Elise Roger
- Univ. Grenoble Alpes, CNRS LPNC UMR 5105, 38000 Grenoble, France
| | - Cédric Pichat
- Univ. Grenoble Alpes, CNRS LPNC UMR 5105, 38000 Grenoble, France
| | - Emilie Cousin
- Univ. Grenoble Alpes, CNRS LPNC UMR 5105, 38000 Grenoble, France; Univ. Grenoble Alpes, UMS IRMaGe CHU Grenoble, 38000 Grenoble, France
| | - Chrystèle Mosca
- Neurology Department, Grenoble Hospital, Univ. Grenoble Alpes, 38000 Grenoble, France
| | - Laurent Lamalle
- Univ. Grenoble Alpes, UMS IRMaGe CHU Grenoble, 38000 Grenoble, France
| | - Alexandre Krainik
- Univ. Grenoble Alpes, UMS IRMaGe CHU Grenoble, 38000 Grenoble, France
| | - Philippe Kahane
- Neurology Department, Grenoble Hospital, Univ. Grenoble Alpes, 38000 Grenoble, France
| | - Monica Baciu
- Univ. Grenoble Alpes, CNRS LPNC UMR 5105, 38000 Grenoble, France.
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21
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Gurler D, White DM, Kraguljac NV, Ver Hoef L, Martin C, Tennant B, Lahti AC. Neural Signatures of Memory Encoding in Schizophrenia Are Modulated by Antipsychotic Treatment. Neuropsychobiology 2021; 80:12-24. [PMID: 32316023 PMCID: PMC7874518 DOI: 10.1159/000506402] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 02/07/2020] [Indexed: 12/17/2022]
Abstract
There is no pharmacological treatment to remediate cognitive impairment in schizophrenia (SZ). It is imperative to characterize underlying pathologies of memory processing in order to effectively develop new treatments. In this longitudinal study, we combined functional magnetic resonance imaging during a memory encoding task with proton MR spectroscopy to measure hippocampal glutamate + glutamine (Glx). Seventeen SZ were scanned while unmedicated and after 6 weeks of treatment with risperidone and compared to a group of matched healthy controls (HC) scanned 6 weeks apart. Unmedicated patients showed reduced blood oxygen level dependent (BOLD) response in several regions, including the hippocampus, and greater BOLD response in regions of the default mode network (DMN) during correct memory encoding. Post hoc contrasts from significant group by time interactions indicated reduced hippocampal BOLD response at baseline with subsequent increase following treatment. Hippocampal Glx was not different between groups at baseline, but at week 6, hippocampal Glx was significantly lower in SZ compared to HC. Finally, in unmedicated SZ, higher hippocampal Glx predicted less deactivation of the BOLD response in regions of the DMN. Using 2 brain imaging modalities allowed us to concurrently investigate different mechanisms involved in memory encoding dysfunction in SZ. Hippocampal pathology during memory encoding stems from decreased hippocampal recruitment and faulty deactivation of the DMN, and hippocampal recruitment during encoding can be modulated by antipsychotic treatment. High Glx in unmedicated patients predicted less deactivation of the DMN; these results suggest a mechanism by which faulty DMN deactivation, a hallmark of pathological findings in SZ, is achieved.
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Affiliation(s)
- Demet Gurler
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham
| | - David Matthew White
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham
| | - Nina Vanessa Kraguljac
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham
| | | | - Clinton Martin
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham
| | - Blake Tennant
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham
| | - Adrienne Carol Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama, USA,
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22
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Self-Controlled Choice Arises from Dynamic Prefrontal Signals That Enable Future Anticipation. J Neurosci 2020; 40:9736-9750. [PMID: 33188069 DOI: 10.1523/jneurosci.1702-20.2020] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 11/02/2020] [Accepted: 11/04/2020] [Indexed: 11/21/2022] Open
Abstract
Self-control allows humans the patience necessary to maximize reward attainment in the future. Yet it remains elusive when and how the preference to self-controlled choice is formed. We measured brain activity while female and male humans performed an intertemporal choice task in which they first received delayed real liquid rewards (forced-choice trial), and then made a choice between the reward options based on the experiences (free-choice trial). We found that, while subjects were awaiting an upcoming reward in the forced-choice trial, the anterior prefrontal cortex (aPFC) tracked a dynamic signal reflecting the pleasure of anticipating the future reward. Importantly, this prefrontal signal was specifically observed in self-controlled individuals, and moreover, interregional negative coupling between the prefrontal region and the ventral striatum (VS) became stronger in those individuals. During consumption of the liquid rewards, reduced ventral striatal activity predicted self-controlled choices in the subsequent free-choice trials. These results suggest that a well-coordinated prefrontal-striatal mechanism during the reward experience shapes preferences regarding the future self-controlled choice.SIGNIFICANCE STATEMENT Anticipating future desirable events is a critical mental function that guides self-controlled behavior in humans. When and how are the self-controlled choices formed in the brain? We monitored brain activity while humans awaited a real liquid reward that became available in tens of seconds. We found that the frontal polar cortex tracked temporally evolving signals reflecting the pleasure of anticipating the future reward, which was enhanced in self-controlled individuals. Our results highlight the contribution of the fronto-polar cortex to the formation of self-controlled preferences, and further suggest that future prospect in the prefrontal cortex (PFC) plays an important role in shaping future choice behavior.
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23
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Frölich MA, White DM, Kraguljac NV, Lahti AC. Baseline Functional Connectivity Predicts Connectivity Changes Due to a Small Dose of Midazolam in Older Adults. Anesth Analg 2020; 130:224-232. [PMID: 31498189 DOI: 10.1213/ane.0000000000004385] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
BACKGROUND In the perioperative context, benzodiazepines are widely used as anxiolytics. They affect cognition in general, but it is unclear whether the effects of a small dose of the short-acting benzodiazepine midazolam can be assessed objectively. To address this scientific question, we conducted a prospective observational study in adults 55-73 years of age. Using both validated psychometric and functional imaging techniques, we determined whether a 2-mg intravenous (IV) dose of midazolam affects cognitive function. METHODS We measured the effect of 2 mg IV of midazolam with both the well-established Repeatable Battery for the Assessment of Neuropsychological Status test and resting-state functional magnetic imaging (rs-fMRI) in older adults. RESULTS Midazolam reduces immediate and delayed memory and has a profound and robust effect on rs-fMRI. Baseline resting-state connectivity predicts memory decline after midazolam administration. CONCLUSIONS Observed effects of midazolam on brain networks were statistically significant even in a small group of volunteers. If validated by other investigators, resting-state brain connectivity may have utility as a measure to predict sensitivity to midazolam in older adults.
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Affiliation(s)
| | - David M White
- Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Nina V Kraguljac
- Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Adrienne C Lahti
- Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama
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24
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Zhang W, Llera A, Hashemi MM, Kaldewaij R, Koch SBJ, Beckmann CF, Klumpers F, Roelofs K. Discriminating stress from rest based on resting-state connectivity of the human brain: A supervised machine learning study. Hum Brain Mapp 2020; 41:3089-3099. [PMID: 32293072 PMCID: PMC7336146 DOI: 10.1002/hbm.25000] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 03/11/2020] [Accepted: 03/19/2020] [Indexed: 01/25/2023] Open
Abstract
Acute stress induces large-scale neural reorganization with relevance to stress-related psychopathology. Here, we applied a novel supervised machine learning method, combining the strengths of a priori theoretical insights with a data-driven approach, to identify which connectivity changes are most prominently associated with a state of acute stress and individual differences therein. Resting-state functional magnetic resonance imaging scans were taken from 334 healthy participants (79 females) before and after a formal stress induction. For each individual scan, mean time-series were extracted from 46 functional parcels of three major brain networks previously shown to be potentially sensitive to stress effects (default mode network (DMN), salience network (SN), and executive control networks). A data-driven approach was then used to obtain discriminative spatial linear filters that classified the pre- and post-stress scans. To assess potential relevance for understanding individual differences, probability of classification using the most discriminative filters was linked to individual cortisol stress responses. Our model correctly classified pre- versus post-stress states with highly significant accuracy (above 75%; leave-one-out validation relative to chance performance). Discrimination between pre- and post-stress states was mainly based on connectivity changes in regions from the SN and DMN, including the dorsal anterior cingulate cortex, amygdala, posterior cingulate cortex, and precuneus. Interestingly, the probability of classification using these connectivity changes were associated with individual cortisol increases. Our results confirm the involvement of DMN and SN using a data-driven approach, and specifically single out key regions that might receive additional attention in future studies for their relevance also for individual differences.
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Affiliation(s)
- Wei Zhang
- Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands.,Behavioural Science Institute, Radboud University Nijmegen, The Netherlands
| | - Alberto Llera
- Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands.,Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands.,Karakter Child and Adolescent Psychiatry, Nijmegen, The Netherlands
| | - Mahur M Hashemi
- Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands.,Behavioural Science Institute, Radboud University Nijmegen, The Netherlands
| | - Reinoud Kaldewaij
- Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands.,Behavioural Science Institute, Radboud University Nijmegen, The Netherlands
| | - Saskia B J Koch
- Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands.,Behavioural Science Institute, Radboud University Nijmegen, The Netherlands
| | - Christian F Beckmann
- Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands.,Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands.,Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Floris Klumpers
- Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands.,Behavioural Science Institute, Radboud University Nijmegen, The Netherlands
| | - Karin Roelofs
- Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands.,Behavioural Science Institute, Radboud University Nijmegen, The Netherlands
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25
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Schedlbauer AM, Ekstrom AD. Flexible network community organization during the encoding and retrieval of spatiotemporal episodic memories. Netw Neurosci 2019; 3:1070-1093. [PMID: 31637339 PMCID: PMC6777981 DOI: 10.1162/netn_a_00102] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Accepted: 06/24/2019] [Indexed: 01/22/2023] Open
Abstract
Memory encoding and retrieval involve distinct interactions between multiple brain areas, yet the flexible structure of corresponding large-scale networks during such memory processing remains unclear. Using functional magnetic resonance imaging, we employed a spatiotemporal encoding and retrieval task, detecting functional community structure across the multiple components of our task. Consistent with past work, we identified a set of stable subnetworks, mostly belonging to primary motor and sensory cortices but also identified a subset of flexible hubs, mostly belonging to higher association areas. These “mover” hubs changed connectivity patterns across spatial and temporal memory encoding and retrieval, engaging in an integrative role within the network. Global encoding network and subnetwork dissimilarity predicted retrieval performance. Together, our findings emphasize the importance of flexible network allegiance among some hubs and the importance of network reconfiguration to human episodic memory. The degree to which task-related functional connectivity patterns remain stable or are dynamic when people learn and remember information remains largely untested. We investigated this issue by collecting fMRI while participants performed a memory encoding and retrieval task. Our results suggested that subnetworks are dynamic and tend to fragment relative to a resting-state network partition. From these changes in connectivity, we identified a subset of “movers,” or in other words, nodes that changed their allegiance to subnetworks across all aspects of the task. These findings emphasize that memory is a dynamic process involving changes in task-related functional connectivity across the brain.
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Affiliation(s)
| | - Arne D Ekstrom
- Neuroscience Graduate Group, University of California, Davis, CA, USA
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26
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Li C, Xia L, Ma J, Li S, Liang S, Ma X, Wang T, Li M, Wen H, Jiang G. Dynamic functional abnormalities in generalized anxiety disorders and their increased network segregation of a hyperarousal brain state modulated by insomnia. J Affect Disord 2019; 246:338-345. [PMID: 30597294 DOI: 10.1016/j.jad.2018.12.079] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 11/26/2018] [Accepted: 12/24/2018] [Indexed: 10/27/2022]
Abstract
BACKGROUND Insomnia is frequently accompanied by the generalized anxiety disorder (GAD) but mostly fMRI studies investigated their aberrant functional connectivity (FC) without this issue. Recently, dynamic FC approach is prevailing to capture the time-varying fluctuations of spontaneous brain activities. Nevertheless, it is unclear how the dynamic FC characteristics are altered by insomnia in GAD. METHODS We acquired resting state fMRI and neuropsychological tests for the 17 comorbid GAD with insomnia (GAD/IS), 15 GAD and 24 healthy controls (HC). Then, based on the sliding window correlations, we estimated distinct brain states and statistically compared their dynamic properties. Further combining with graph theory, their network properties of each state among groups were accessed. Lastly, we examined associations between abnormal parameters and neuropsychological tests. RESULTS We identified four brain states but did not observe significance on the state transitions. The mean dwell time and fraction of one globally hypoactive state accounted for high proportion of brain activities were significantly different (GAD > HC > GAD/IS). Meanwhile, we found gradual decreases in a brain state representing slight sleep/drowsiness (HC > GAD/IS > GAD). Additionally, we observed the GAD/IS patients had significantly increased network segregation and posterior cingulate cortex in a hyperarousal state, as well as significant associations with anxiety and insomnia severity. LIMITATIONS The influences of depression on dynamic FC properties in GAD are unclear yet and more subjects should be recruited. CONCLUSIONS These results provide new insights about the temporal features in GAD and offer potential biomarkers to evaluate the impacts of insomnia.
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Affiliation(s)
- Changhong Li
- Department of Medical Imaging, Guangdong No. 2 Provincial People's Hospital, Guangzhou, PR China
| | - Likun Xia
- Department of Magnetic Resonance Imaging, Yuxi People's Hospital, Yuxi, PR China
| | - Jian Ma
- Department of Magnetic Resonance Imaging, Yuxi People's Hospital, Yuxi, PR China
| | - Shumei Li
- Department of Medical Imaging, Guangdong No. 2 Provincial People's Hospital, Guangzhou, PR China
| | - Sayuan Liang
- Clinical Solution, Philips Innovation Hub, Shanghai, PR China
| | - Xiaofen Ma
- Department of Medical Imaging, Guangdong No. 2 Provincial People's Hospital, Guangzhou, PR China
| | - Tianyue Wang
- Department of Medical Imaging, Guangdong No. 2 Provincial People's Hospital, Guangzhou, PR China
| | - Meng Li
- Department of Medical Imaging, Guangdong No. 2 Provincial People's Hospital, Guangzhou, PR China
| | - Hua Wen
- Department of Medical Imaging, Guangdong No. 2 Provincial People's Hospital, Guangzhou, PR China
| | - Guihua Jiang
- Department of Medical Imaging, Guangdong No. 2 Provincial People's Hospital, Guangzhou, PR China.
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27
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Keerativittayayut R, Aoki R, Sarabi MT, Jimura K, Nakahara K. Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance. eLife 2018; 7:32696. [PMID: 29911970 PMCID: PMC6039182 DOI: 10.7554/elife.32696] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 06/16/2018] [Indexed: 12/19/2022] Open
Abstract
Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.
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Affiliation(s)
| | - Ryuta Aoki
- Research Center for Brain Communication, Kochi University of Technology, Kochi, Japan
| | | | - Koji Jimura
- Research Center for Brain Communication, Kochi University of Technology, Kochi, Japan.,Department of Biosciences and Informatics, Keio University, Yokohama, Japan
| | - Kiyoshi Nakahara
- School of Information, Kochi University of Technology, Kochi, Japan.,Research Center for Brain Communication, Kochi University of Technology, Kochi, Japan
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