1
|
Russo S, Claar LD, Furregoni G, Marks LC, Krishnan G, Zauli FM, Hassan G, Solbiati M, d'Orio P, Mikulan E, Sarasso S, Rosanova M, Sartori I, Bazhenov M, Pigorini A, Massimini M, Koch C, Rembado I. Thalamic feedback shapes brain responses evoked by cortical stimulation in mice and humans. Nat Commun 2025; 16:3627. [PMID: 40240330 PMCID: PMC12003640 DOI: 10.1038/s41467-025-58717-2] [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: 05/22/2024] [Accepted: 03/27/2025] [Indexed: 04/18/2025] Open
Abstract
Cortical stimulation with single pulses is a common technique in clinical practice and research. However, we still do not understand the extent to which it engages subcortical circuits that may contribute to the associated evoked potentials (EPs). Here we show that cortical stimulation generates remarkably similar EPs in humans and mice, with a late component similarly modulated by the state of the targeted cortico-thalamic network. We then optogenetically dissect the underlying circuit in mice, demonstrating that the EPs late component is caused by a thalamic hyperpolarization and rebound. The magnitude of this late component correlates with bursting frequency and synchronicity of thalamic neurons, modulated by the subject's behavioral state. A simulation of the thalamo-cortical circuit highlights that both intrinsic thalamic currents as well as cortical and thalamic GABAergic neurons contribute to this response profile. We conclude that single pulse cortical stimulation engages cortico-thalamo-cortical circuits largely preserved across different species and stimulation modalities.
Collapse
Affiliation(s)
- Simone Russo
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan, 20157, Italy
- Department of Philosophy 'Piero Martinetti', University of Milan, Milan, Italy
- Brain and Consciousness, Allen Institute, Seattle, USA
- Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | | | - Giulia Furregoni
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan, 20157, Italy
- School of Advanced Studies, Center of Neuroscience, University of Camerino, Camerino, Italy
| | - Lydia C Marks
- Brain and Consciousness, Allen Institute, Seattle, USA
| | - Giri Krishnan
- Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Flavia Maria Zauli
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan, 20157, Italy
- Department of Philosophy 'Piero Martinetti', University of Milan, Milan, Italy
- ASST Grande Ospedale Metropolitano Niguarda, "C. Munari" Epilepsy Surgery Centre, Milan, Italy
| | - Gabriel Hassan
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan, 20157, Italy
- Department of Philosophy 'Piero Martinetti', University of Milan, Milan, Italy
| | - Michela Solbiati
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan, 20157, Italy
- ASST Grande Ospedale Metropolitano Niguarda, "C. Munari" Epilepsy Surgery Centre, Milan, Italy
| | - Piergiorgio d'Orio
- ASST Grande Ospedale Metropolitano Niguarda, "C. Munari" Epilepsy Surgery Centre, Milan, Italy
- University of Parma, Parma, 43121, Italy
| | - Ezequiel Mikulan
- Department of Health Sciences, University of Milan, Milan, Italy
| | - Simone Sarasso
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan, 20157, Italy
| | - Mario Rosanova
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan, 20157, Italy
| | - Ivana Sartori
- ASST Grande Ospedale Metropolitano Niguarda, "C. Munari" Epilepsy Surgery Centre, Milan, Italy
| | - Maxim Bazhenov
- Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
- Neurosciences Graduate Program, University of California San Diego, La Jolla, CA, 92093, USA
| | - Andrea Pigorini
- Department of Biomedical, Surgical and Dental Sciences, Università degli Studi di Milano, Milan, 20122, Italy
- UOC Maxillo-facial Surgery and dentistry, Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Marcello Massimini
- Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan, 20157, Italy
- Istituto Di Ricovero e Cura a Carattere Scientifico, Fondazione Don Carlo Gnocchi, Milan, 20122, Italy
- Azrieli Program in Brain, Mind and Consciousness, Canadian Institute for Advanced Research (CIFAR), Toronto, Ontario, M5G 1M1, Canada
| | - Christof Koch
- Brain and Consciousness, Allen Institute, Seattle, USA
| | - Irene Rembado
- Brain and Consciousness, Allen Institute, Seattle, USA.
| |
Collapse
|
2
|
Opalka AN, Dougherty KJ, Wang DV. A Distinct Down-to-Up Transition Assembly in the Retrosplenial Cortex during Slow-Wave Sleep. J Neurosci 2025; 45:e1484242025. [PMID: 39952672 PMCID: PMC11968548 DOI: 10.1523/jneurosci.1484-24.2025] [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: 08/05/2024] [Revised: 01/24/2025] [Accepted: 02/11/2025] [Indexed: 02/17/2025] Open
Abstract
Understanding the intricate mechanisms underlying slow-wave sleep (SWS) is crucial for deciphering the brain's role in memory consolidation and cognitive functions. It is well established that cortical delta oscillations (0.5-4 Hz) coordinate communications among cortical, hippocampal, and thalamic regions during SWS. These delta oscillations feature periods of Up and Down states, with the latter previously thought to represent complete cortical silence; however, new evidence suggests that Down states serve important functions for information exchange during memory consolidation. The retrosplenial cortex (RSC) is pivotal for memory consolidation due to its extensive connectivity with memory-associated regions, although it remains unclear how RSC neurons engage in delta-associated consolidation processes. Here, we employed multichannel in vivo electrophysiology to study RSC neuronal activity in ad libitum behaving male mice during natural SWS. We discovered a discrete assembly of putative excitatory RSC neurons (∼20%) that initiated firing at SWS Down states and reached maximal firing at the Down-to-Up transitions. Therefore, we termed these RSC neurons the Down-to-Up transition assembly (DUA) and the remaining RSC excitatory neurons as non-DUA. Compared with non-DUA, DUA neurons appear to exhibit higher firing rates and larger cell body size and lack monosynaptic connectivity with nearby RSC neurons. Furthermore, optogenetics combined with electrophysiology revealed differential innervation of RSC excitatory neurons by memory-associated inputs. Collectively, these findings provide insight into the distinct activity patterns of RSC neuronal subpopulations during sleep and their potential role in memory processes.
Collapse
Affiliation(s)
- Ashley N Opalka
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, Pennsylvania 19129
| | - Kimberly J Dougherty
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, Pennsylvania 19129
| | - Dong V Wang
- Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, Pennsylvania 19129
| |
Collapse
|
3
|
Lei Y, Liu Y, Wang M, Yuan N, Hou Y, Ding L, Zhu Z, Wu Z, Li C, Zheng M, Zhang R, Ribeiro Gomes AR, Xu Y, Luo Z, Liu Z, Chai Q, Misery P, Zhong Y, Song X, Lamy C, Cui W, Yu Q, Fang J, An Y, Tian Y, Liu Y, Sun X, Wang R, Li H, Song J, Tan X, Wang H, Wang S, Han L, Zhang Y, Li S, Wang K, Wang G, Zhou W, Liu J, Yu C, Zhang S, Chang L, Toplanaj D, Chen M, Liu J, Zhao Y, Ren B, Shi H, Zhang H, Yan H, Ma J, Wang L, Li Y, Zuo Y, Lu L, Gu L, Li S, Wang Y, He Y, Li S, Zhang Q, Lu Y, Dou Y, Liu Y, Zhao A, Zhang M, Zhang X, Xia Y, Zhang W, Cao H, Lu Z, Yu Z, Li X, Wang X, Liang Z, Xu S, Liu C, Zheng C, Xu C, Liu Z, Li C, Sun YG, Xu X, Dehay C, Vezoli J, Poo MM, Yao J, Liu L, Wei W, Kennedy H, Shen Z. Single-cell spatial transcriptome atlas and whole-brain connectivity of the macaque claustrum. Cell 2025:S0092-8674(25)00273-9. [PMID: 40185102 DOI: 10.1016/j.cell.2025.02.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Revised: 10/03/2024] [Accepted: 02/28/2025] [Indexed: 04/07/2025]
Abstract
Claustrum orchestrates brain functions via its connections with numerous brain regions, but its molecular and cellular organization remains unresolved. Single-nucleus RNA sequencing of 227,750 macaque claustral cells identified 48 transcriptome-defined cell types, with most glutamatergic neurons similar to deep-layer insular neurons. Comparison of macaque, marmoset, and mouse transcriptomes revealed macaque-specific cell types. Retrograde tracer injections at 67 cortical and 7 subcortical regions defined four distinct distribution zones of retrogradely labeled claustral neurons. Joint analysis of whole-brain connectivity and single-cell spatial transcriptome showed that these four zones containing distinct compositions of glutamatergic (but not GABAergic) cell types preferentially connected to specific brain regions with a strong ipsilateral bias. Several macaque-specific glutamatergic cell types in ventral vs. dorsal claustral zones selectively co-projected to two functionally related areas-entorhinal cortex and hippocampus vs. motor cortex and putamen, respectively. These data provide the basis for elucidating the neuronal organization underlying diverse claustral functions.
Collapse
Affiliation(s)
- Ying Lei
- BGI-Research, Hangzhou 310012, China; BGI-Research, Shenzhen 518103, China; Shanxi Medical University - BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan 030001, China
| | - Yuxuan Liu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Chinese Academy of Science, Beijing 100049, China
| | - Mingli Wang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; Lingang Laboratory, Shanghai 200031, China; University of Chinese Academy of Sciences, Chinese Academy of Science, Beijing 100049, China
| | - Nini Yuan
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yujie Hou
- Univ Lyon, Université Claude Bernard Lyon 1, Inserm U1208, Stem Cell and Brain Research Institute, Bron 69500, France
| | - Lingjun Ding
- BGI-Research, Hangzhou 310012, China; BGI-Research, Shenzhen 518103, China
| | - Zhiyong Zhu
- BGI-Research, Hangzhou 310012, China; BGI-Research, Shenzhen 518103, China
| | - Zihan Wu
- AI for Life Sciences Lab, Tencent, Shenzhen 518057, China
| | - Chao Li
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mingyuan Zheng
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ruiyi Zhang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ana Rita Ribeiro Gomes
- Univ Lyon, Université Claude Bernard Lyon 1, Inserm U1208, Stem Cell and Brain Research Institute, Bron 69500, France
| | - Yuanfang Xu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhaoke Luo
- Univ Lyon, Université Claude Bernard Lyon 1, Inserm U1208, Stem Cell and Brain Research Institute, Bron 69500, France
| | - Zhen Liu
- Lingang Laboratory, Shanghai 200031, China
| | - Qinwen Chai
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Chinese Academy of Science, Beijing 100049, China
| | - Pierre Misery
- Univ Lyon, Université Claude Bernard Lyon 1, Inserm U1208, Stem Cell and Brain Research Institute, Bron 69500, France
| | - Yanqing Zhong
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xinxiang Song
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Camille Lamy
- Univ Lyon, Université Claude Bernard Lyon 1, Inserm U1208, Stem Cell and Brain Research Institute, Bron 69500, France
| | - Wei Cui
- BGI-Research, Qingdao 266555, China
| | - Qian Yu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jiao Fang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Chinese Academy of Science, Beijing 100049, China
| | - Yingjie An
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ye Tian
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Chinese Academy of Science, Beijing 100049, China
| | - Yiwen Liu
- Lingang Laboratory, Shanghai 200031, China; University of Chinese Academy of Sciences, Chinese Academy of Science, Beijing 100049, China
| | - Xing Sun
- Lingang Laboratory, Shanghai 200031, China
| | - Ruiqi Wang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Huanhuan Li
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jingjing Song
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xing Tan
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - He Wang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shiwen Wang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ling Han
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Shenyu Li
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Kexin Wang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Chinese Academy of Science, Beijing 100049, China
| | - Guangling Wang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Wanqiu Zhou
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jianfeng Liu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Chinese Academy of Science, Beijing 100049, China
| | - Cong Yu
- BGI-Research, Qingdao 266555, China
| | - Shuzhen Zhang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Liangtang Chang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Dafina Toplanaj
- Univ Lyon, Université Claude Bernard Lyon 1, Inserm U1208, Stem Cell and Brain Research Institute, Bron 69500, France
| | - Mengni Chen
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jiabing Liu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yun Zhao
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Biyu Ren
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Hanyu Shi
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Hui Zhang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Haotian Yan
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jianyun Ma
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Chinese Academy of Science, Beijing 100049, China
| | - Lina Wang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yan Li
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yichen Zuo
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Chinese Academy of Science, Beijing 100049, China
| | - Linjie Lu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Liqin Gu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shuting Li
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Yinying He
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Qi Zhang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yanbing Lu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yannong Dou
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yuan Liu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Anqi Zhao
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China
| | - Minyuan Zhang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xinyan Zhang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ying Xia
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Wei Zhang
- Lingang Laboratory, Shanghai 200031, China
| | - Huateng Cao
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhiyue Lu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zixian Yu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xin Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Chinese Academy of Science, Beijing 100049, China
| | - Xiaofei Wang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhifeng Liang
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Chinese Academy of Science, Beijing 100049, China
| | - Shengjin Xu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Chinese Academy of Science, Beijing 100049, China
| | - Cirong Liu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Chinese Academy of Science, Beijing 100049, China
| | - Changhong Zheng
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China
| | - Chun Xu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Chinese Academy of Science, Beijing 100049, China
| | - Zhiyong Liu
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Chinese Academy of Science, Beijing 100049, China
| | - Chengyu Li
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; Lingang Laboratory, Shanghai 200031, China; University of Chinese Academy of Sciences, Chinese Academy of Science, Beijing 100049, China
| | - Yan-Gang Sun
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Chinese Academy of Science, Beijing 100049, China
| | - Xun Xu
- BGI-Research, Shenzhen 518103, China; Shanxi Medical University - BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan 030001, China
| | - Colette Dehay
- Univ Lyon, Université Claude Bernard Lyon 1, Inserm U1208, Stem Cell and Brain Research Institute, Bron 69500, France
| | - Julien Vezoli
- Univ Lyon, Université Claude Bernard Lyon 1, Inserm U1208, Stem Cell and Brain Research Institute, Bron 69500, France
| | - Mu-Ming Poo
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China; University of Chinese Academy of Sciences, Chinese Academy of Science, Beijing 100049, China
| | - Jianhua Yao
- AI for Life Sciences Lab, Tencent, Shenzhen 518057, China.
| | - Longqi Liu
- BGI-Research, Hangzhou 310012, China; BGI-Research, Shenzhen 518103, China; Shanxi Medical University - BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan 030001, China.
| | - Wu Wei
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China; Lingang Laboratory, Shanghai 200031, China; University of Chinese Academy of Sciences, Chinese Academy of Science, Beijing 100049, China.
| | - Henry Kennedy
- Univ Lyon, Université Claude Bernard Lyon 1, Inserm U1208, Stem Cell and Brain Research Institute, Bron 69500, France.
| | - Zhiming Shen
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China.
| |
Collapse
|
4
|
Park S, Sohn K, Yoon D, Lee J, Choi S. Single-unit activity in the anterior claustrum during memory retrieval after trace fear conditioning. PLoS One 2025; 20:e0318307. [PMID: 39932965 PMCID: PMC11813112 DOI: 10.1371/journal.pone.0318307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2024] [Accepted: 01/13/2025] [Indexed: 02/13/2025] Open
Abstract
We have recently identified a group of claustral neurons that continuously maintain information associated with a fear-conditioned stimulus (CS) for at least tens of seconds, even after the CS has ceased. This "online state" refers to the persistent maintenance of threat-associated information, enabling it to be actively processed even after the threat has terminated. This state may involve reciprocal interactions of the claustral neurons with brain regions involved in decision-making, motor preparation, and adaptive behavioral responses. If these claustral neurons truly encode the online state, their function should remain independent of the modality of the threat stimulus or the specific defensive behavior exhibited. In this study, we used a tone cue and monitored freezing behavior in trace conditioning, in contrast to the light cue and escape behavior used in our recent study. During the retrieval test of trace conditioning, a subset of rostral-to-striatum claustrum (rsCla) neurons exhibited sustained activity in response to the CS, particularly during the trace interval. Importantly, we found a positive correlation between the activity of rsCla neurons and the magnitude of freezing during the trace interval, when intervals without freezing were excluded. Thus, this subset of rsCla neurons appears to exhibit the characteristics of 'online neurons' during memory retrieval following trace conditioning.
Collapse
Affiliation(s)
- Sewon Park
- Department of Neurobiology, University of Utah, Salt Lake City, Utah, United States of America
| | - Kuenbae Sohn
- School of Biological Sciences, College of Natural Sciences, Seoul National University, Seoul, Korea
| | - Donghyeon Yoon
- School of Biological Sciences, College of Natural Sciences, Seoul National University, Seoul, Korea
| | - Junghwa Lee
- School of Biological Sciences, College of Natural Sciences, Seoul National University, Seoul, Korea
| | - Sukwoo Choi
- School of Biological Sciences, College of Natural Sciences, Seoul National University, Seoul, Korea
| |
Collapse
|
5
|
Fan P, Zhang R, Xiao G, Song Y, Zhuang C, Yuan L, Mo F, Lu B, Xu Z, Wang Y, Luo J, Wang M, Mi W, Cao J, Dai Q, Cai X. Simultaneous recording of neuronal discharge and calcium activity reveals claustrum-cortex neurosynchrony under anesthesia. FUNDAMENTAL RESEARCH 2025; 5:93-102. [PMID: 40166107 PMCID: PMC11955029 DOI: 10.1016/j.fmre.2023.12.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 12/25/2023] [Accepted: 12/27/2023] [Indexed: 04/02/2025] Open
Abstract
Neural information transmission between deep brain nuclei and the cortex is essential for brain function. Currently, high-resolution simultaneous detection of neural information between the deep brain nuclei and the large-scale cortex still poses challenges. We have developed the microelectrode arrays based on the Micro-Electro-Mechanical System technology, and modified the electrode surface with nanomaterials to improve the electrode performance. This study combined microelectrode arrays and extended-field-of-view microscopy to achieve simultaneous recording of claustrum (CLA) electrophysiology and wide-field cortical calcium imaging at single-cell resolution. This work investigated the synchronous changes of neural information in CLA and cortex of mice during the whole process from wakefulness to anesthesia and then to wakefulness, and summarized the characteristics of the CLA electrophysiology and cortical calcium signaling under different inhalation anesthesia concentrations. We found the synergy between microscopic spike and local field potential of CLA neurons under deep anesthesia, and the law that high inhalation anesthesia concentration enhanced the synchronization between neurons in CLA and cortex. The combination of microelectrode arrays and extended-field-of-view microscopy also gives a new method for synchronous detection of multimodal and multi-brain region neural information.
Collapse
Affiliation(s)
- Penghui Fan
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Rujin Zhang
- Department of Anesthesiology, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Guihua Xiao
- Department of Automation, Tsinghua University, Beijing 100084, China
| | - Yilin Song
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chaowei Zhuang
- Department of Automation, Tsinghua University, Beijing 100084, China
| | - Lekang Yuan
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China
| | - Fan Mo
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Botao Lu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhaojie Xu
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiding Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jinping Luo
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mixia Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weidong Mi
- Department of Anesthesiology, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Jiangbei Cao
- Department of Anesthesiology, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Qionghai Dai
- Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xinxia Cai
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| |
Collapse
|
6
|
Do AD, Portet C, Goutagny R, Jackson J. The claustrum and synchronized brain states. Trends Neurosci 2024; 47:1028-1040. [PMID: 39488479 DOI: 10.1016/j.tins.2024.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Revised: 09/25/2024] [Accepted: 10/09/2024] [Indexed: 11/04/2024]
Abstract
Cortical activity is constantly fluctuating between distinct spatiotemporal activity patterns denoted by changes in brain state. States of cortical desynchronization arise during motor generation, increased attention, and high cognitive load. Synchronized brain states comprise spatially widespread, coordinated low-frequency neural activity during rest and sleep when disengaged from the external environment or 'offline'. The claustrum is a small subcortical structure with dense reciprocal connections with the cortex suggesting modulation by, or participation in, brain state regulation. Here, we highlight recent work suggesting that neural activity in the claustrum supports cognitive processes associated with synchronized brain states characterized by increased low-frequency network activity. As an example, we outline how claustrum activity could support episodic memory consolidation during sleep.
Collapse
Affiliation(s)
- Alison D Do
- Department of Physiology, University of Alberta, Edmonton, AB, Canada
| | - Coline Portet
- University of Strasbourg, Strasbourg, France; Laboratoire de Neurosciences Cognitives et Adaptatives, CNRS UMR7364, Strasbourg, France
| | - Romain Goutagny
- University of Strasbourg, Strasbourg, France; Laboratoire de Neurosciences Cognitives et Adaptatives, CNRS UMR7364, Strasbourg, France
| | - Jesse Jackson
- Department of Physiology, University of Alberta, Edmonton, AB, Canada; Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada.
| |
Collapse
|
7
|
Schinz D, Neubauer A, Hippen R, Schulz J, Li HB, Thalhammer M, Schmitz-Koep B, Menegaux A, Wendt J, Ayyildiz S, Brandl F, Priller J, Uder M, Zimmer C, Hedderich DM, Sorg C. Claustrum Volumes Are Lower in Schizophrenia and Mediate Patients' Attentional Deficits. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00350-1. [PMID: 39608754 DOI: 10.1016/j.bpsc.2024.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2024] [Revised: 11/15/2024] [Accepted: 11/15/2024] [Indexed: 11/30/2024]
Abstract
BACKGROUND While the last decade of extensive research revealed the prominent role of the claustrum for mammalian forebrain organization (i.e., widely distributed claustral-cortical circuits coordinate basic cognitive functions such as attention), it is poorly understood whether the claustrum is relevant for schizophrenia and related cognitive symptoms. We hypothesized that claustrum volumes are lower in schizophrenia and also that potentially lower volumes mediate patients' attention deficits. METHODS Based on T1-weighted magnetic resonance imaging, advanced automated claustrum segmentation, and attention symbol coding task in 90 patients with schizophrenia and 96 healthy control participants from 2 independent sites, the COBRE open-source database and Munich dataset, we compared total intracranial volume-normalized claustrum volumes and symbol coding task scores across groups via analysis of covariance and related variables via correlation and mediation analysis. RESULTS Patients had lower claustrum volumes of about 13% (p < .001, Hedges' g = 0.63), which not only correlated with (r = 0.24, p = .014) but also mediated lower symbol coding task scores (indirect effect ab = -1.30 ± 0.69; 95% CI, -3.73 to -1.04). Results were not confounded by age, sex, global and claustrum-adjacent gray matter changes, scanner site, smoking, and medication. CONCLUSIONS Results demonstrate lower claustrum volumes that mediate patients' attention deficits in schizophrenia. Data indicate the claustrum as being relevant for schizophrenia pathophysiology and cognitive functioning.
Collapse
Affiliation(s)
- David Schinz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany; Technische Universität München Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany; Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen, Nürnberg, Germany.
| | - Antonia Neubauer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany; Technische Universität München Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany; Center for Neuropathology and Prion Research, University Hospital Munich, Ludwig Maximilians University of Munich, Munich, Germany
| | - Rebecca Hippen
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany; Technische Universität München Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Julia Schulz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany; Technische Universität München Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Hongwei Bran Li
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Melissa Thalhammer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany; Technische Universität München Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Benita Schmitz-Koep
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany; Technische Universität München Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Aurore Menegaux
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany; Technische Universität München Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Jil Wendt
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany; Technische Universität München Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Sevilay Ayyildiz
- Anatomy Ph.D. Program, Graduate School of Health Sciences, Kocaeli University, Istanbul, Turkey
| | - Felix Brandl
- Department of Psychiatry, School of Medicine, Technical University of Munich, Munich, Germany
| | - Josef Priller
- Department of Psychiatry, School of Medicine, Technical University of Munich, Munich, Germany
| | - Michael Uder
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen, Nürnberg, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany; Technische Universität München Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Dennis M Hedderich
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany; Technische Universität München Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Christian Sorg
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany; Technische Universität München Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany; Department of Psychiatry, School of Medicine, Technical University of Munich, Munich, Germany
| |
Collapse
|
8
|
Zahacy R, Ma Y, Winship IR, Jackson J, Chan AW. Claustrum modulation drives altered prefrontal cortex dynamics and connectivity. Commun Biol 2024; 7:1556. [PMID: 39578634 PMCID: PMC11584859 DOI: 10.1038/s42003-024-07256-5] [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: 08/21/2024] [Accepted: 11/12/2024] [Indexed: 11/24/2024] Open
Abstract
This study delves into the claustrum's role in modulating spontaneous and sensory-evoked network activity across cortical regions. Using mesoscale calcium imaging and Gi and Gq DREADDs in anesthetized mice, we show that decreasing claustral activity enhances prefrontal cortical activity, while activation reduces prefrontal cortical activity. This claustrum modulation also caused changes to the brain's large-scale functional networks, emphasizing the claustrum's ability to influence long-range functional connectivity in the cortex. Claustrum inhibition increased the local coupling between frontal cortex areas, but reduced the correlation between anterior medial regions and lateral/posterior regions, while also enhancing sensory-evoked responses in the visual cortex. These findings indicate the claustrum can participate in orchestrating neural communication across cortical regions through modulation of prefrontal cortical activity. These insights deepen our understanding of the claustrum's impact on prefrontal connectivity, large-scale network dynamics, and sensory processing, positioning the claustrum as a key node modulating large-scale cortical dynamics.
Collapse
Affiliation(s)
- Ryan Zahacy
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
| | - Yonglie Ma
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada
| | - Ian R Winship
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada
| | - Jesse Jackson
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada.
- Department of Physiology, University of Alberta, Edmonton, AB, Canada.
| | - Allen W Chan
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada.
- Department of Psychiatry, University of Alberta, Edmonton, AB, Canada.
| |
Collapse
|
9
|
Lamsam L, Gu B, Liang M, Sun G, Khan KJ, Sheth KN, Hirsch LJ, Pittenger C, Kaye AP, Krystal JH, Damisah EC. The human claustrum tracks slow waves during sleep. Nat Commun 2024; 15:8964. [PMID: 39419999 PMCID: PMC11487173 DOI: 10.1038/s41467-024-53477-x] [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: 01/19/2024] [Accepted: 10/11/2024] [Indexed: 10/19/2024] Open
Abstract
Slow waves are a distinguishing feature of non-rapid-eye-movement (NREM) sleep, an evolutionarily conserved process critical for brain function. Non-human studies suggest that the claustrum, a small subcortical nucleus, coordinates slow waves. We show that, in contrast to neurons from other brain regions, claustrum neurons in the human brain increase their spiking activity and track slow waves during NREM sleep, suggesting that the claustrum plays a role in coordinating human sleep architecture.
Collapse
Affiliation(s)
- Layton Lamsam
- Department of Neurosurgery, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Brett Gu
- Department of Neurosurgery, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Mingli Liang
- Department of Neurosurgery, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - George Sun
- Department of Neurosurgery, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Kamren J Khan
- Department of Neurosurgery, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Kevin N Sheth
- Department of Neurosurgery, Yale School of Medicine, Yale University, New Haven, CT, USA
- Department of Neurology, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Lawrence J Hirsch
- Department of Neurology, Yale School of Medicine, Yale University, New Haven, CT, USA
- Department of Neurology, Yale School of Medicine, Comprehensive Epilepsy Center, Yale University, New Haven, CT, USA
| | - Christopher Pittenger
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
- Department of Psychology, Yale University, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, Yale University, New Haven, CT, USA
- Center for Brain and Mind Health, Yale University, New Haven, CT, USA
| | - Alfred P Kaye
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
- Clinical Neurosciences Division, VA National Center for PTSD, West Haven, CT, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
- Department of Psychology, Yale University, New Haven, CT, USA
- Clinical Neurosciences Division, VA National Center for PTSD, West Haven, CT, USA
- Department of Neuroscience, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Eyiyemisi C Damisah
- Department of Neurosurgery, Yale School of Medicine, Yale University, New Haven, CT, USA.
- Wu Tsai Institute, Yale University, New Haven, CT, USA.
- Center for Brain and Mind Health, Yale University, New Haven, CT, USA.
- Department of Neuroscience, Yale School of Medicine, Yale University, New Haven, CT, USA.
| |
Collapse
|
10
|
Mantas I, Flais I, Masarapu Y, Ionescu T, Frapard S, Jung F, Le Merre P, Saarinen M, Tiklova K, Salmani BY, Gillberg L, Zhang X, Chergui K, Carlén M, Giacomello S, Hengerer B, Perlmann T, Svenningsson P. Claustrum and dorsal endopiriform cortex complex cell-identity is determined by Nurr1 and regulates hallucinogenic-like states in mice. Nat Commun 2024; 15:8176. [PMID: 39289358 PMCID: PMC11408527 DOI: 10.1038/s41467-024-52429-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 09/04/2024] [Indexed: 09/19/2024] Open
Abstract
The Claustrum/dorsal endopiriform cortex complex (CLA) is an enigmatic brain region with extensive glutamatergic projections to multiple cortical areas. The transcription factor Nurr1 is highly expressed in the CLA, but its role in this region is not understood. By using conditional gene-targeted mice, we show that Nurr1 is a crucial regulator of CLA neuron identity. Although CLA neurons remain intact in the absence of Nurr1, the distinctive gene expression pattern in the CLA is abolished. CLA has been hypothesized to control hallucinations, but little is known of how the CLA responds to hallucinogens. After the deletion of Nurr1 in the CLA, both hallucinogen receptor expression and signaling are lost. Furthermore, functional ultrasound and Neuropixel electrophysiological recordings revealed that the hallucinogenic-receptor agonists' effects on functional connectivity between prefrontal and sensorimotor cortices are altered in Nurr1-ablated mice. Our findings suggest that Nurr1-targeted strategies provide additional avenues for functional studies of the CLA.
Collapse
Affiliation(s)
- Ioannis Mantas
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden.
| | - Ivana Flais
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- CNSDR, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
- Department of Neuroimaging King's College London, London, UK
| | - Yuvarani Masarapu
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Tudor Ionescu
- CNSDR, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Solène Frapard
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Felix Jung
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Pierre Le Merre
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Marcus Saarinen
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Katarina Tiklova
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | | | - Linda Gillberg
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Xiaoqun Zhang
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Karima Chergui
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Marie Carlén
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Stefania Giacomello
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Bastian Hengerer
- CNSDR, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Thomas Perlmann
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Per Svenningsson
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| |
Collapse
|
11
|
Bennett C, Ouellette B, Ramirez TK, Cahoon A, Cabasco H, Browning Y, Lakunina A, Lynch GF, McBride EG, Belski H, Gillis R, Grasso C, Howard R, Johnson T, Loeffler H, Smith H, Sullivan D, Williford A, Caldejon S, Durand S, Gale S, Guthrie A, Ha V, Han W, Hardcastle B, Mochizuki C, Sridhar A, Suarez L, Swapp J, Wilkes J, Siegle JH, Farrell C, Groblewski PA, Olsen SR. SHIELD: Skull-shaped hemispheric implants enabling large-scale electrophysiology datasets in the mouse brain. Neuron 2024; 112:2869-2885.e8. [PMID: 38996587 DOI: 10.1016/j.neuron.2024.06.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 05/02/2024] [Accepted: 06/18/2024] [Indexed: 07/14/2024]
Abstract
To understand the neural basis of behavior, it is essential to measure spiking dynamics across many interacting brain regions. Although new technologies, such as Neuropixels probes, facilitate multi-regional recordings, significant surgical and procedural hurdles remain for these experiments to achieve their full potential. Here, we describe skull-shaped hemispheric implants enabling large-scale electrophysiology datasets (SHIELD). These 3D-printed skull-replacement implants feature customizable insertion holes, allowing dozens of cortical and subcortical structures to be recorded in a single mouse using repeated multi-probe insertions over many days. We demonstrate the procedure's high success rate, biocompatibility, lack of adverse effects on behavior, and compatibility with imaging and optogenetics. To showcase SHIELD's scientific utility, we use multi-probe recordings to reveal novel insights into how alpha rhythms organize spiking activity across visual and sensorimotor networks. Overall, this method enables powerful, large-scale electrophysiological experiments for the study of distributed neural computation.
Collapse
Affiliation(s)
- Corbett Bennett
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA.
| | - Ben Ouellette
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | | | | | - Hannah Cabasco
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Yoni Browning
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Anna Lakunina
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Galen F Lynch
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | | | - Hannah Belski
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Ryan Gillis
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Conor Grasso
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Robert Howard
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Tye Johnson
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Henry Loeffler
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Heston Smith
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | | | | | | | | | - Samuel Gale
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Alan Guthrie
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Vivian Ha
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Warren Han
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Ben Hardcastle
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | | | - Arjun Sridhar
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Lucas Suarez
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Jackie Swapp
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Joshua Wilkes
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | | | | | | | - Shawn R Olsen
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA.
| |
Collapse
|
12
|
Anderson TL, Keady JV, Songrady J, Tavakoli NS, Asadipooya A, Neeley RE, Turner JR, Ortinski PI. Distinct 5-HT receptor subtypes regulate claustrum excitability by serotonin and the psychedelic, DOI. Prog Neurobiol 2024; 240:102660. [PMID: 39218140 PMCID: PMC11444019 DOI: 10.1016/j.pneurobio.2024.102660] [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: 02/06/2024] [Revised: 07/03/2024] [Accepted: 08/25/2024] [Indexed: 09/04/2024]
Abstract
Recent evidence indicates that neuronal activity within the claustrum (CLA) may be central to cellular and behavioral responses to psychedelic hallucinogens. The CLA prominently innervates many cortical targets and displays exceptionally high levels of serotonin (5-HT) binding. However, the influence of serotonin receptors, prime targets of psychedelic drug action, on CLA activity remains unexplored. We characterize the CLA expression of all known 5-HT subtypes and contrast the effects of 5-HT and the psychedelic hallucinogen, 2,5-dimethoxy-4-iodoamphetamine (DOI), on excitability of cortical-projecting CLA neurons. We find that the CLA is particularly enriched with 5-HT2C receptors, expressed predominantly on glutamatergic neurons. Electrophysiological recordings from CLA neurons that project to the anterior cingulate cortex (ACC) indicate that application of 5-HT inhibits glutamate receptor-mediated excitatory postsynaptic currents (EPSCs). In contrast, application of DOI stimulates EPSCs. We find that the opposite effects of 5-HT and DOI on synaptic signaling can both be reversed by inhibition of the 5-HT2C, but not 5-HT2A, receptors. We identify specific 5-HT receptor subtypes as serotonergic regulators of the CLA excitability and argue against the canonical role of 5-HT2A in glutamatergic synapse response to psychedelics within the CLA-ACC circuit.
Collapse
Affiliation(s)
- Tanner L Anderson
- University of Kentucky, College of Medicine, Department of Neuroscience, Lexington, KY 40536, United States
| | - Jack V Keady
- University of Kentucky, College of Pharmacy, Department of Pharmaceutical Sciences, Lexington, KY 40536, United States
| | - Judy Songrady
- University of Kentucky, College of Pharmacy, Department of Pharmaceutical Sciences, Lexington, KY 40536, United States
| | - Navid S Tavakoli
- University of Kentucky, College of Medicine, Department of Neuroscience, Lexington, KY 40536, United States
| | - Artin Asadipooya
- University of Kentucky, College of Medicine, Department of Neuroscience, Lexington, KY 40536, United States
| | - Ryson E Neeley
- University of Kentucky, College of Medicine, Department of Neuroscience, Lexington, KY 40536, United States
| | - Jill R Turner
- University of Kentucky, College of Pharmacy, Department of Pharmaceutical Sciences, Lexington, KY 40536, United States
| | - Pavel I Ortinski
- University of Kentucky, College of Medicine, Department of Neuroscience, Lexington, KY 40536, United States.
| |
Collapse
|
13
|
Yamakawa H, Fukawa A, Yairi IE, Matsuo Y. Brain-consistent architecture for imagination. Front Syst Neurosci 2024; 18:1302429. [PMID: 39229305 PMCID: PMC11368743 DOI: 10.3389/fnsys.2024.1302429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 07/29/2024] [Indexed: 09/05/2024] Open
Abstract
Background Imagination represents a pivotal capability of human intelligence. To develop human-like artificial intelligence, uncovering the computational architecture pertinent to imaginative capabilities through reverse engineering the brain's computational functions is essential. The existing Structure-Constrained Interface Decomposition (SCID) method, leverages the anatomical structure of the brain to extract computational architecture. However, its efficacy is limited to narrow brain regions, making it unsuitable for realizing the function of imagination, which involves diverse brain areas such as the neocortex, basal ganglia, thalamus, and hippocampus. Objective In this study, we proposed the Function-Oriented SCID method, an advancement over the existing SCID method, comprising four steps designed for reverse engineering broader brain areas. This method was applied to the brain's imaginative capabilities to design a hypothetical computational architecture. The implementation began with defining the human imaginative ability that we aspire to simulate. Subsequently, six critical requirements necessary for actualizing the defined imagination were identified. Constraints were established considering the unique representational capacity and the singularity of the neocortex's modes, a distributed memory structure responsible for executing imaginative functions. In line with these constraints, we developed five distinct functions to fulfill the requirements. We allocated specific components for each function, followed by an architectural proposal aligning each component with a corresponding brain organ. Results In the proposed architecture, the distributed memory component, associated with the neocortex, realizes the representation and execution function; the imaginary zone maker component, associated with the claustrum, accomplishes the dynamic-zone partitioning function; the routing conductor component, linked with the complex of thalamus and basal ganglia, performs the manipulation function; the mode memory component, related to the specific agranular neocortical area executes the mode maintenance function; and the recorder component, affiliated with the hippocampal formation, handles the history management function. Thus, we have provided a fundamental cognitive architecture of the brain that comprehensively covers the brain's imaginative capacities.
Collapse
Affiliation(s)
- Hiroshi Yamakawa
- School of Engineering, The University of Tokyo, Tokyo, Japan
- The Whole Brain Architecture Initiative, Tokyo, Japan
| | - Ayako Fukawa
- The Whole Brain Architecture Initiative, Tokyo, Japan
- Graduate School of Science and Technology, Sophia University, Tokyo, Japan
| | - Ikuko Eguchi Yairi
- Graduate School of Science and Technology, Sophia University, Tokyo, Japan
| | - Yutaka Matsuo
- School of Engineering, The University of Tokyo, Tokyo, Japan
| |
Collapse
|
14
|
Atlan G, Matosevich N, Peretz-Rivlin N, Marsh-Yvgi I, Zelinger N, Chen E, Kleinman T, Bleistein N, Sheinbach E, Groysman M, Nir Y, Citri A. Claustrum neurons projecting to the anterior cingulate restrict engagement during sleep and behavior. Nat Commun 2024; 15:5415. [PMID: 38926345 PMCID: PMC11208603 DOI: 10.1038/s41467-024-48829-6] [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: 10/08/2022] [Accepted: 05/14/2024] [Indexed: 06/28/2024] Open
Abstract
The claustrum has been linked to attention and sleep. We hypothesized that this reflects a shared function, determining responsiveness to stimuli, which spans the axis of engagement. To test this hypothesis, we recorded claustrum population dynamics from male mice during both sleep and an attentional task ('ENGAGE'). Heightened activity in claustrum neurons projecting to the anterior cingulate cortex (ACCp) corresponded to reduced sensory responsiveness during sleep. Similarly, in the ENGAGE task, heightened ACCp activity correlated with disengagement and behavioral lapses, while low ACCp activity correlated with hyper-engagement and impulsive errors. Chemogenetic elevation of ACCp activity reduced both awakenings during sleep and impulsive errors in the ENGAGE task. Furthermore, mice employing an exploration strategy in the task showed a stronger correlation between ACCp activity and performance compared to mice employing an exploitation strategy which reduced task complexity. Our results implicate ACCp claustrum neurons in restricting engagement during sleep and goal-directed behavior.
Collapse
Affiliation(s)
- Gal Atlan
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem; Edmond J. Safra Campus, Givat Ram, Jerusalem, Israel
| | - Noa Matosevich
- Department of Physiology & Pharmacology, Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Noa Peretz-Rivlin
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem; Edmond J. Safra Campus, Givat Ram, Jerusalem, Israel
| | - Idit Marsh-Yvgi
- The Alexander Silberman Institute of Life Science, Faculty of Science, The Hebrew University of Jerusalem; Edmond J. Safra Campus, Givat Ram, Jerusalem, Israel
| | - Noam Zelinger
- Department of Physiology & Pharmacology, Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Eden Chen
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem; Edmond J. Safra Campus, Givat Ram, Jerusalem, Israel
| | - Timna Kleinman
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem; Edmond J. Safra Campus, Givat Ram, Jerusalem, Israel
| | - Noa Bleistein
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem; Edmond J. Safra Campus, Givat Ram, Jerusalem, Israel
- The Alexander Silberman Institute of Life Science, Faculty of Science, The Hebrew University of Jerusalem; Edmond J. Safra Campus, Givat Ram, Jerusalem, Israel
| | - Efrat Sheinbach
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem; Edmond J. Safra Campus, Givat Ram, Jerusalem, Israel
- The Alexander Silberman Institute of Life Science, Faculty of Science, The Hebrew University of Jerusalem; Edmond J. Safra Campus, Givat Ram, Jerusalem, Israel
| | - Maya Groysman
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem; Edmond J. Safra Campus, Givat Ram, Jerusalem, Israel
| | - Yuval Nir
- Department of Physiology & Pharmacology, Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
- The Sieratzki-Sagol Center for Sleep Medicine, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Ami Citri
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem; Edmond J. Safra Campus, Givat Ram, Jerusalem, Israel.
- The Alexander Silberman Institute of Life Science, Faculty of Science, The Hebrew University of Jerusalem; Edmond J. Safra Campus, Givat Ram, Jerusalem, Israel.
- Program in Child and Brain Development, Canadian Institute for Advanced Research; MaRS Centre, Toronto, ON, Canada.
| |
Collapse
|
15
|
Stewart BW, Keaser ML, Lee H, Margerison SM, Cormie MA, Moayedi M, Lindquist MA, Chen S, Mathur BN, Seminowicz DA. Pathological claustrum activity drives aberrant cognitive network processing in human chronic pain. Curr Biol 2024; 34:1953-1966.e6. [PMID: 38614082 DOI: 10.1016/j.cub.2024.03.021] [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: 01/17/2024] [Revised: 02/08/2024] [Accepted: 03/13/2024] [Indexed: 04/15/2024]
Abstract
Aberrant cognitive network activity and cognitive deficits are established features of chronic pain. However, the nature of cognitive network alterations associated with chronic pain and their underlying mechanisms require elucidation. Here, we report that the claustrum, a subcortical nucleus implicated in cognitive network modulation, is activated by acute painful stimulation and pain-predictive cues in healthy participants. Moreover, we discover pathological activity of the claustrum and a region near the posterior inferior frontal sulcus of the right dorsolateral prefrontal cortex (piDLPFC) in migraine patients during acute pain and cognitive task performance. Dynamic causal modeling suggests a directional influence of the claustrum on activity in this piDLPFC region, and diffusion weighted imaging verifies their structural connectivity. These findings advance understanding of claustrum function during acute pain and provide evidence of a possible circuit mechanism driving cognitive impairments in chronic pain.
Collapse
Affiliation(s)
- Brent W Stewart
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, W Baltimore Street, Baltimore, MD 21201, USA
| | - Michael L Keaser
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, W Baltimore Street, Baltimore, MD 21201, USA
| | - Hwiyoung Lee
- Department of Epidemiology & Public Health, Maryland Psychiatric Research Center, Wade Avenue, Catonsville, MD 21228, USA
| | - Sarah M Margerison
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, W Baltimore Street, Baltimore, MD 21201, USA; Physical Therapy and Rehabilitation Science, University of Maryland School of Medicine, Penn Street, Baltimore, MD 21201, USA
| | - Matthew A Cormie
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, Edward Street, Toronto, ON M5G 1E2, Canada
| | - Massieh Moayedi
- Centre for Multimodal Sensorimotor and Pain Research, Faculty of Dentistry, University of Toronto, Edward Street, Toronto, ON M5G 1E2, Canada; Department of Dentistry, Mount Sinai Hospital, University Avenue, Toronto, ON M5G 1X5, Canada; Division of Clinical & Computational Neuroscience, Krembil Brain Institute, University Health Network, Nassau Street, Toronto, ON M5T 1M8, Canada
| | - Martin A Lindquist
- Department of Biostatistics, Johns Hopkins University, N Wolfe Street, Baltimore, MD 21205, USA
| | - Shuo Chen
- Department of Epidemiology & Public Health, Maryland Psychiatric Research Center, Wade Avenue, Catonsville, MD 21228, USA
| | - Brian N Mathur
- Department of Pharmacology, University of Maryland School of Medicine, W Baltimore Street, Baltimore, MD 21201, USA; Department of Psychiatry, University of Maryland School of Medicine, W Baltimore Street, Baltimore, MD 21201, USA.
| | - David A Seminowicz
- Department of Neural and Pain Sciences, University of Maryland School of Dentistry, W Baltimore Street, Baltimore, MD 21201, USA; Department of Medical Biophysics, Schulich School of Medicine & Dentistry, University of Western Ontario, Richmond Street, London, ON N6A 5C1, Canada.
| |
Collapse
|
16
|
Chen FD, Sharma A, Roszko DA, Xue T, Mu X, Luo X, Chua H, Lo PGQ, Sacher WD, Poon JKS. Development of wafer-scale multifunctional nanophotonic neural probes for brain activity mapping. LAB ON A CHIP 2024; 24:2397-2417. [PMID: 38623840 DOI: 10.1039/d3lc00931a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
Optical techniques, such as optogenetic stimulation and functional fluorescence imaging, have been revolutionary for neuroscience by enabling neural circuit analysis with cell-type specificity. To probe deep brain regions, implantable light sources are crucial. Silicon photonics, commonly used for data communications, shows great promise in creating implantable devices with complex optical systems in a compact form factor compatible with high volume manufacturing practices. This article reviews recent developments of wafer-scale multifunctional nanophotonic neural probes. The probes can be realized on 200 or 300 mm wafers in commercial foundries and integrate light emitters for photostimulation, microelectrodes for electrophysiological recording, and microfluidic channels for chemical delivery and sampling. By integrating active optical devices to the probes, denser emitter arrays, enhanced on-chip biosensing, and increased ease of use may be realized. Silicon photonics technology makes possible highly versatile implantable neural probes that can transform neuroscience experiments.
Collapse
Affiliation(s)
- Fu Der Chen
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany.
- Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada
| | - Ankita Sharma
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany.
- Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada
| | - David A Roszko
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany.
- Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada
| | - Tianyuan Xue
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany.
- Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada
| | - Xin Mu
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany.
- Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada
| | - Xianshu Luo
- Advanced Micro Foundry Pte Ltd, 11 Science Park Road, Singapore Science Park II, 117685, Singapore
| | - Hongyao Chua
- Advanced Micro Foundry Pte Ltd, 11 Science Park Road, Singapore Science Park II, 117685, Singapore
| | - Patrick Guo-Qiang Lo
- Advanced Micro Foundry Pte Ltd, 11 Science Park Road, Singapore Science Park II, 117685, Singapore
| | - Wesley D Sacher
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany.
| | - Joyce K S Poon
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany.
- Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada
| |
Collapse
|
17
|
Lamsam L, Liang M, Gu B, Sun G, Hirsch LJ, Pittenger C, Kaye AP, Krystal JH, Damisah EC. The human claustrum tracks slow waves during sleep. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.29.577851. [PMID: 38352615 PMCID: PMC10862750 DOI: 10.1101/2024.01.29.577851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2024]
Abstract
Slow waves are a distinguishing feature of non-rapid-eye-movement (NREM) sleep, an evolutionarily conserved process critical for brain function. Non-human studies posit that the claustrum, a small subcortical nucleus, coordinates slow waves. We recorded claustrum neurons in humans during sleep. In contrast to neurons from other brain regions, claustrum neurons increased their activity and tracked slow waves during NREM sleep suggesting that the claustrum plays a role in human sleep architecture.
Collapse
|
18
|
Marriott BA, Do AD, Portet C, Thellier F, Goutagny R, Jackson J. Brain-state-dependent constraints on claustrocortical communication and function. Cell Rep 2024; 43:113620. [PMID: 38159273 DOI: 10.1016/j.celrep.2023.113620] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 11/20/2023] [Accepted: 12/11/2023] [Indexed: 01/03/2024] Open
Abstract
Neural activity in the claustrum has been associated with a range of vigilance states, yet the activity patterns and efficacy of synaptic communication of identified claustrum neurons have not been thoroughly determined. Here, we show that claustrum neurons projecting to the retrosplenial cortex are most active during synchronized cortical states such as non-rapid eye movement (NREM) sleep and are suppressed during increased cortical desynchronization associated with arousal, movement, and REM sleep. The efficacy of claustrocortical signaling is increased during NREM and diminished during movement due in part to increased cholinergic tone. Finally, claustrum activation during NREM sleep enhances memory consolidation through the phase resetting of cortical delta waves. Therefore, claustrocortical communication is constrained to function most effectively during cognitive processes associated with synchronized cortical states, such as memory consolidation.
Collapse
Affiliation(s)
- Brian A Marriott
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB T6G2H7, Canada
| | - Alison D Do
- Department of Physiology, University of Alberta, Edmonton, AB T6G2H7, Canada
| | - Coline Portet
- University of Strasbourg, Strasbourg, France; Laboratoire de Neurosciences Cognitives et Adaptatives, CNRS UMR7364, Strasbourg, France
| | - Flora Thellier
- University of Strasbourg, Strasbourg, France; Laboratoire de Neurosciences Cognitives et Adaptatives, CNRS UMR7364, Strasbourg, France
| | - Romain Goutagny
- University of Strasbourg, Strasbourg, France; Laboratoire de Neurosciences Cognitives et Adaptatives, CNRS UMR7364, Strasbourg, France.
| | - Jesse Jackson
- Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB T6G2H7, Canada; Department of Physiology, University of Alberta, Edmonton, AB T6G2H7, Canada.
| |
Collapse
|
19
|
Suzuki M, Pennartz CMA, Aru J. How deep is the brain? The shallow brain hypothesis. Nat Rev Neurosci 2023; 24:778-791. [PMID: 37891398 DOI: 10.1038/s41583-023-00756-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/25/2023] [Indexed: 10/29/2023]
Abstract
Deep learning and predictive coding architectures commonly assume that inference in neural networks is hierarchical. However, largely neglected in deep learning and predictive coding architectures is the neurobiological evidence that all hierarchical cortical areas, higher or lower, project to and receive signals directly from subcortical areas. Given these neuroanatomical facts, today's dominance of cortico-centric, hierarchical architectures in deep learning and predictive coding networks is highly questionable; such architectures are likely to be missing essential computational principles the brain uses. In this Perspective, we present the shallow brain hypothesis: hierarchical cortical processing is integrated with a massively parallel process to which subcortical areas substantially contribute. This shallow architecture exploits the computational capacity of cortical microcircuits and thalamo-cortical loops that are not included in typical hierarchical deep learning and predictive coding networks. We argue that the shallow brain architecture provides several critical benefits over deep hierarchical structures and a more complete depiction of how mammalian brains achieve fast and flexible computational capabilities.
Collapse
Affiliation(s)
- Mototaka Suzuki
- Department of Cognitive and Systems Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.
| | - Cyriel M A Pennartz
- Department of Cognitive and Systems Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - Jaan Aru
- Institute of Computer Science, University of Tartu, Tartu, Estonia.
| |
Collapse
|
20
|
Kou ZQ, Chen CY, Abdurahman M, Weng XC, Hu C, Geng HY. The Claustrum Controls Motor Activity Through Anterior Cingulate Cortex Input and Local Circuit Synchronization in a Preparatory Manner. Neurosci Bull 2023; 39:1591-1594. [PMID: 37310577 PMCID: PMC10533431 DOI: 10.1007/s12264-023-01079-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 04/19/2023] [Indexed: 06/14/2023] Open
Affiliation(s)
- Zi-Qi Kou
- Key Laboratory of Brain, Cognition and Education Science, Ministry of Education, South China Normal University, Guangzhou, 510631, China
- Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China
| | - Chun-Yan Chen
- Key Laboratory of Brain, Cognition and Education Science, Ministry of Education, South China Normal University, Guangzhou, 510631, China
- Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China
| | - Mamatsali Abdurahman
- Key Laboratory of Brain, Cognition and Education Science, Ministry of Education, South China Normal University, Guangzhou, 510631, China
- Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China
| | - Xu-Chu Weng
- Key Laboratory of Brain, Cognition and Education Science, Ministry of Education, South China Normal University, Guangzhou, 510631, China
- Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China
| | - Chun Hu
- Key Laboratory of Brain, Cognition and Education Science, Ministry of Education, South China Normal University, Guangzhou, 510631, China
- Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China
| | - Hong-Yan Geng
- Key Laboratory of Brain, Cognition and Education Science, Ministry of Education, South China Normal University, Guangzhou, 510631, China.
- Institute for Brain Research and Rehabilitation, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China.
| |
Collapse
|
21
|
Claar LD, Rembado I, Kuyat JR, Russo S, Marks LC, Olsen SR, Koch C. Cortico-thalamo-cortical interactions modulate electrically evoked EEG responses in mice. eLife 2023; 12:RP84630. [PMID: 37358562 DOI: 10.7554/elife.84630] [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] [Indexed: 06/27/2023] Open
Abstract
Perturbational complexity analysis predicts the presence of consciousness in volunteers and patients by stimulating the brain with brief pulses, recording EEG responses, and computing their spatiotemporal complexity. We examined the underlying neural circuits in mice by directly stimulating cortex while recording with EEG and Neuropixels probes during wakefulness and isoflurane anesthesia. When mice are awake, stimulation of deep cortical layers reliably evokes locally a brief pulse of excitation, followed by a biphasic sequence of 120 ms profound off period and a rebound excitation. A similar pattern, partially attributed to burst spiking, is seen in thalamic nuclei and is associated with a pronounced late component in the evoked EEG. We infer that cortico-thalamo-cortical interactions drive the long-lasting evoked EEG signals elicited by deep cortical stimulation during the awake state. The cortical and thalamic off period and rebound excitation, and the late component in the EEG, are reduced during running and absent during anesthesia.
Collapse
Affiliation(s)
- Leslie D Claar
- MindScope Program, Allen Institute, Seattle, United States
| | - Irene Rembado
- MindScope Program, Allen Institute, Seattle, United States
| | | | - Simone Russo
- MindScope Program, Allen Institute, Seattle, United States
- Department of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy
| | - Lydia C Marks
- MindScope Program, Allen Institute, Seattle, United States
| | - Shawn R Olsen
- MindScope Program, Allen Institute, Seattle, United States
| | - Christof Koch
- MindScope Program, Allen Institute, Seattle, United States
| |
Collapse
|
22
|
Wang Q, Wang Y, Kuo HC, Xie P, Kuang X, Hirokawa KE, Naeemi M, Yao S, Mallory M, Ouellette B, Lesnar P, Li Y, Ye M, Chen C, Xiong W, Ahmadinia L, El-Hifnawi L, Cetin A, Sorensen SA, Harris JA, Zeng H, Koch C. Regional and cell-type-specific afferent and efferent projections of the mouse claustrum. Cell Rep 2023; 42:112118. [PMID: 36774552 PMCID: PMC10415534 DOI: 10.1016/j.celrep.2023.112118] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 12/17/2022] [Accepted: 01/30/2023] [Indexed: 02/13/2023] Open
Abstract
The claustrum (CLA) is a conspicuous subcortical structure interconnected with cortical and subcortical regions. Its regional anatomy and cell-type-specific connections in the mouse remain not fully determined. Using multimodal reference datasets, we confirmed the delineation of the mouse CLA as a single group of neurons embedded in the agranular insular cortex. We quantitatively investigated brain-wide inputs and outputs of CLA using bulk anterograde and retrograde viral tracing data and single neuron tracing data. We found that the prefrontal module has more cell types projecting to the CLA than other cortical modules, with layer 5 IT neurons predominating. We found nine morphological types of CLA principal neurons that topographically innervate functionally linked cortical targets, preferentially the midline cortical areas, secondary motor area, and entorhinal area. Together, this study provides a detailed wiring diagram of the cell-type-specific connections of the mouse CLA, laying a foundation for studying its functions at the cellular level.
Collapse
Affiliation(s)
- Quanxin Wang
- Allen Institute for Brain Science, Seattle, WA 98109, USA.
| | - Yun Wang
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Hsien-Chi Kuo
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Peng Xie
- Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu, China
| | - Xiuli Kuang
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | | | - Maitham Naeemi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Shenqin Yao
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Matt Mallory
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Ben Ouellette
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Phil Lesnar
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Yaoyao Li
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | - Min Ye
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | - Chao Chen
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | - Wei Xiong
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | | | | | - Ali Cetin
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Julie A Harris
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Christof Koch
- Allen Institute for Brain Science, Seattle, WA 98109, USA.
| |
Collapse
|