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Guan Y, Li J, Wei Y, Shi PT, Yang C, Yun X, Quan Q, Wang WJ, Yu XG, Wei M. Brain functional connectivity alterations in patients with anterior cruciate ligament injury. Brain Res 2024; 1836:148956. [PMID: 38657888 DOI: 10.1016/j.brainres.2024.148956] [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: 01/19/2024] [Revised: 04/16/2024] [Accepted: 04/21/2024] [Indexed: 04/26/2024]
Abstract
Recent advancements in neuroimaging have illustrated that anterior cruciate ligament (ACL) injuries could impact the central nervous system (CNS), causing neuroplastic changes in the brain beyond the traditionally understood biomechanical consequences. While most of previous functional magnetic resonance imaging (fMRI) studies have focused on localized cortical activity changes post-injury, emerging research has suggested disruptions in functional connectivity across the brain. However, these prior investigations, albeit pioneering, have been constrained by two limitations: a reliance on small-sample participant cohorts, often limited to two to three patients, potentially limiting the generalizability of findings, and an adherence to region of interest based analysis, which may overlook broader network interactions. To address these limitations, our study employed resting-state fMRI to assess whole-brain functional connectivity in 15 ACL-injured patients, comparing them to matched controls using two distinct network analysis methods. Using Network-Based Statistics, we identified widespread reductions in connectivity that spanned across multiple brain regions. Further modular connectivity analysis showed significant decreases in inter-modular connectivity between the sensorimotor and cerebellar modules, and intra-modular connectivity within the default-mode network in ACL-injured patients. Our results thus highlight a shift from localized disruptions to network-wide dysfunctions, suggesting that ACL injuries induce widespread CNS changes. This enhanced understanding has the potential to stimulate the development of strategies aiming to restore functional connectivity and improve recovery outcomes.
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Affiliation(s)
- Yu Guan
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing 100142, China; Medical School of Chinese PLA, Beijing 100853, China
| | - Ji Li
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing 100142, China
| | - Yu Wei
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing 100142, China
| | - Peng-Tao Shi
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing 100142, China; Medical School of Chinese PLA, Beijing 100853, China
| | - Chen Yang
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing 100142, China; Medical School of Chinese PLA, Beijing 100853, China
| | - Xing Yun
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing 100142, China; Medical School of Chinese PLA, Beijing 100853, China
| | - Qi Quan
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing 100142, China; Department of Orthopedic Surgery, Key Laboratory of Musculoskeletal Trauma &War Injuries PLA, Beijing Key Lab of Regenerative Medicine in Orthopedics, Chinese PLA General Hospital, Beijing 100853, China
| | - Wen-Juan Wang
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing 100142, China
| | - Xin-Guang Yu
- Department of Neurosurgery, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Min Wei
- Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing 100142, China; Medical School of Chinese PLA, Beijing 100853, China.
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2
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Chicos LA, Rangaprakash D, Srinivasan SS, Gutierrez-Arango S, Song H, Barry RL, Herr HM. Resting state neurophysiology of agonist-antagonist myoneural interface in persons with transtibial amputation. Sci Rep 2024; 14:13456. [PMID: 38862558 PMCID: PMC11166995 DOI: 10.1038/s41598-024-63134-4] [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: 04/06/2024] [Accepted: 05/24/2024] [Indexed: 06/13/2024] Open
Abstract
The agonist-antagonist myoneural interface (AMI) is an amputation surgery that preserves sensorimotor signaling mechanisms of the central-peripheral nervous systems. Our first neuroimaging study investigating AMI subjects conducted by Srinivasan et al. (2020) focused on task-based neural signatures, and showed evidence of proprioceptive feedback to the central nervous system. The study of resting state neural activity helps non-invasively characterize the neural patterns that prime task response. In this study on resting state functional magnetic resonance imaging in AMI subjects, we compared functional connectivity in patients with transtibial AMI (n = 12) and traditional (n = 7) amputations (TA). To test our hypothesis that we would find significant neurophysiological differences between AMI and TA subjects, we performed a whole-brain exploratory analysis to identify a seed region; namely, we conducted ANOVA, followed by t-test statistics to locate a seed in the salience network. Then, we implemented a seed-based connectivity analysis to gather cluster-level inferences contrasting our subject groups. We show evidence supporting our hypothesis that the AMI surgery induces functional network reorganization resulting in a neural configuration that significantly differs from the neural configuration after TA surgery. AMI subjects show significantly less coupling with regions functionally dedicated to selecting where to focus attention when it comes to salient stimuli. Our findings provide researchers and clinicians with a critical mechanistic understanding of the effect of AMI amputation on brain networks at rest, which has promising implications for improved neurorehabilitation and prosthetic control.
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Affiliation(s)
- Laura A Chicos
- Biomechatronics Group, Massachusetts Institute of Technology, Media Lab, Cambridge, MA, 02139, USA.
- K. Lisa Yang Center for Bionics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - D Rangaprakash
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
| | - Shriya S Srinivasan
- Harvard-MA Institute of Technology Division of Health Sciences and Technology, Cambridge, MA, 02139, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Allston, MA, 02134, USA
| | - Samantha Gutierrez-Arango
- Biomechatronics Group, Massachusetts Institute of Technology, Media Lab, Cambridge, MA, 02139, USA
- K. Lisa Yang Center for Bionics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Hyungeun Song
- Biomechatronics Group, Massachusetts Institute of Technology, Media Lab, Cambridge, MA, 02139, USA
- K. Lisa Yang Center for Bionics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Harvard-MA Institute of Technology Division of Health Sciences and Technology, Cambridge, MA, 02139, USA
| | - Robert L Barry
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA, 02115, USA
- Harvard-MA Institute of Technology Division of Health Sciences and Technology, Cambridge, MA, 02139, USA
| | - Hugh M Herr
- Biomechatronics Group, Massachusetts Institute of Technology, Media Lab, Cambridge, MA, 02139, USA
- K. Lisa Yang Center for Bionics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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3
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Kosakowski HL, Saadon-Grosman N, Du J, Eldaief MC, Buckner RL. Human striatal association megaclusters. J Neurophysiol 2024; 131:1083-1100. [PMID: 38505898 DOI: 10.1152/jn.00387.2023] [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: 10/19/2023] [Revised: 03/13/2024] [Accepted: 03/13/2024] [Indexed: 03/21/2024] Open
Abstract
The striatum receives projections from multiple regions of the cerebral cortex consistent with the role of the basal ganglia in diverse motor, affective, and cognitive functions. Within the striatum, the caudate receives projections from association cortex, including multiple distinct regions of prefrontal cortex. Building on recent insights about the details of how juxtaposed cortical networks are specialized for distinct aspects of higher-order cognition, we revisited caudate organization using within-individual precision neuroimaging initially in two intensively scanned individuals (each scanned 31 times). Results revealed that the caudate has side-by-side regions that are coupled to at least five distinct distributed association networks, paralleling the organization observed in the cerebral cortex. We refer to these spatial groupings of regions as striatal association megaclusters. Correlation maps from closely juxtaposed seed regions placed within the megaclusters recapitulated the five distinct cortical networks, including their multiple spatially distributed regions. Striatal association megaclusters were explored in 15 additional participants (each scanned at least 8 times), finding that their presence generalizes to new participants. Analysis of the laterality of the regions within the megaclusters further revealed that they possess asymmetries paralleling their cortical counterparts. For example, caudate regions linked to the language network were left lateralized. These results extend the general notion of parallel specialized basal ganglia circuits with the additional discovery that, even within the caudate, there is fine-grained separation of multiple distinct higher-order networks that reflects the organization and lateralization found in the cerebral cortex.NEW & NOTEWORTHY An individualized precision neuroimaging approach reveals juxtaposed zones of the caudate that are coupled with five distinct networks in association cortex. The organization of these caudate zones recapitulates organization observed in the cerebral cortex and extends the notion of specialized basal ganglia circuits.
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Affiliation(s)
- Heather L Kosakowski
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Noam Saadon-Grosman
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Jingnan Du
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Mark C Eldaief
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Department of Neurology, Massachusetts General Hospital, Charlestown, Massachusetts, United States
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States
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4
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Du J, DiNicola LM, Angeli PA, Saadon-Grosman N, Sun W, Kaiser S, Ladopoulou J, Xue A, Yeo BTT, Eldaief MC, Buckner RL. Organization of the human cerebral cortex estimated within individuals: networks, global topography, and function. J Neurophysiol 2024; 131:1014-1082. [PMID: 38489238 DOI: 10.1152/jn.00308.2023] [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: 08/16/2023] [Revised: 01/18/2024] [Accepted: 02/16/2024] [Indexed: 03/17/2024] Open
Abstract
The cerebral cortex is populated by specialized regions that are organized into networks. Here we estimated networks from functional MRI (fMRI) data in intensively sampled participants. The procedure was developed in two participants (scanned 31 times) and then prospectively applied to 15 participants (scanned 8-11 times). Analysis of the networks revealed a global organization. Locally organized first-order sensory and motor networks were surrounded by spatially adjacent second-order networks that linked to distant regions. Third-order networks possessed regions distributed widely throughout association cortex. Regions of distinct third-order networks displayed side-by-side juxtapositions with a pattern that repeated across multiple cortical zones. We refer to these as supra-areal association megaclusters (SAAMs). Within each SAAM, two candidate control regions were adjacent to three separate domain-specialized regions. Response properties were explored with task data. The somatomotor and visual networks responded to body movements and visual stimulation, respectively. Second-order networks responded to transients in an oddball detection task, consistent with a role in orienting to salient events. The third-order networks, including distinct regions within each SAAM, showed two levels of functional specialization. Regions linked to candidate control networks responded to working memory load across multiple stimulus domains. The remaining regions dissociated across language, social, and spatial/episodic processing domains. These results suggest that progressively higher-order networks nest outward from primary sensory and motor cortices. Within the apex zones of association cortex, there is specialization that repeatedly divides domain-flexible from domain-specialized regions. We discuss implications of these findings, including how repeating organizational motifs may emerge during development.NEW & NOTEWORTHY The organization of cerebral networks was estimated within individuals with intensive, repeat sampling of fMRI data. A hierarchical organization emerged in each individual that delineated first-, second-, and third-order cortical networks. Regions of distinct third-order association networks consistently exhibited side-by-side juxtapositions that repeated across multiple cortical zones, with clear and robust functional specialization among the embedded regions.
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Affiliation(s)
- Jingnan Du
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Lauren M DiNicola
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Peter A Angeli
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Noam Saadon-Grosman
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Wendy Sun
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Stephanie Kaiser
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Joanna Ladopoulou
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
| | - Aihuiping Xue
- Centre for Sleep & Cognition and Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - B T Thomas Yeo
- Centre for Sleep & Cognition and Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - Mark C Eldaief
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts, United States
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States
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5
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Meng Z, Huang Y, Wang W, Zhou L, Zhou K. Orienting role of the putative human posterior infero-temporal area in visual attention. Cortex 2024; 175:54-65. [PMID: 38704919 DOI: 10.1016/j.cortex.2024.04.006] [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: 09/27/2023] [Revised: 02/27/2024] [Accepted: 04/17/2024] [Indexed: 05/07/2024]
Abstract
The dorsal attention network (DAN) is a network of brain regions essential for attentional orienting, which includes the lateral intraparietal area (LIP) and frontal eye field (FEF). Recently, the putative human dorsal posterior infero-temporal area (phPITd) has been identified as a new node of the DAN. However, its functional relationship with other areas of the DAN and its specific role in visual attention remained unclear. In this study, we analyzed a large publicly available neuroimaging dataset to investigate the intrinsic functional connectivities (FCs) of the phPITd with other brain areas. The results showed that the intrinsic FCs of the phPITd with the areas of the visual network and the DAN were significantly stronger than those with the ventral attention network (VAN) areas and areas of other networks. We further conducted individual difference analyses with a sample size of 295 participants and a series of attentional tasks to investigate which attentional components each phPITd-based DAN edge predicts. Our findings revealed that the intrinsic FC of the left phPITd with the LIPv could predict individual ability in attentional orienting, but not in alerting, executive control, and distractor suppression. Our results not only provide direct evidence of the phPITd's functional relationship with the LIPv, but also offer a comprehensive understanding of its specific role in visual attention.
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Affiliation(s)
- Zong Meng
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, 100875, China
| | - Yingjie Huang
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, 100875, China
| | - Wenbo Wang
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, 100875, China
| | - Liqin Zhou
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, 100875, China.
| | - Ke Zhou
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, 100875, China.
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Muta K, Haga Y, Hata J, Kaneko T, Hagiya K, Komaki Y, Seki F, Yoshimaru D, Nakae K, Woodward A, Gong R, Kishi N, Okano H. Commonality and variance of resting-state networks in common marmoset brains. Sci Rep 2024; 14:8316. [PMID: 38594386 PMCID: PMC11004137 DOI: 10.1038/s41598-024-58799-w] [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: 12/09/2023] [Accepted: 04/03/2024] [Indexed: 04/11/2024] Open
Abstract
Animal models of brain function are critical for the study of human diseases and development of effective interventions. Resting-state network (RSN) analysis is a powerful tool for evaluating brain function and performing comparisons across animal species. Several studies have reported RSNs in the common marmoset (Callithrix jacchus; marmoset), a non-human primate. However, it is necessary to identify RSNs and evaluate commonality and inter-individual variance through analyses using a larger amount of data. In this study, we present marmoset RSNs detected using > 100,000 time-course image volumes of resting-state functional magnetic resonance imaging data with careful preprocessing. In addition, we extracted brain regions involved in the composition of these RSNs to understand the differences between humans and marmosets. We detected 16 RSNs in major marmosets, three of which were novel networks that have not been previously reported in marmosets. Since these RSNs possess the potential for use in the functional evaluation of neurodegenerative diseases, the data in this study will significantly contribute to the understanding of the functional effects of neurodegenerative diseases.
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Affiliation(s)
- Kanako Muta
- Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Saitama, Japan
| | - Yawara Haga
- Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Saitama, Japan
- Live Animal Imaging Center, Central Institute for Experimental Animals, Kanagawa, Japan
| | - Junichi Hata
- Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Saitama, Japan
- Live Animal Imaging Center, Central Institute for Experimental Animals, Kanagawa, Japan
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan
- Division of Regenerative Medicine, The Jikei University School of Medicine, Tokyo, Japan
| | - Takaaki Kaneko
- Division of Behavioral Development, Department of System Neuroscience, National Institute for Physiological Science, Aichi, Japan
| | - Kei Hagiya
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Saitama, Japan
| | - Yuji Komaki
- Live Animal Imaging Center, Central Institute for Experimental Animals, Kanagawa, Japan
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan
| | - Fumiko Seki
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Saitama, Japan
- Live Animal Imaging Center, Central Institute for Experimental Animals, Kanagawa, Japan
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan
| | - Daisuke Yoshimaru
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Saitama, Japan
- Live Animal Imaging Center, Central Institute for Experimental Animals, Kanagawa, Japan
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan
- Division of Regenerative Medicine, The Jikei University School of Medicine, Tokyo, Japan
| | - Ken Nakae
- Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences, Aichi, Japan
| | - Alexander Woodward
- Connectome Analysis Unit, Center for Brain Science, RIKEN, Saitama, Japan
| | - Rui Gong
- Connectome Analysis Unit, Center for Brain Science, RIKEN, Saitama, Japan
| | - Noriyuki Kishi
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Saitama, Japan
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan
| | - Hideyuki Okano
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Saitama, Japan.
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan.
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7
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Wei W, Zhang K, Chang J, Zhang S, Ma L, Wang H, Zhang M, Zu Z, Yang L, Chen F, Fan C, Li X. Analyzing 20 years of Resting-State fMRI Research: Trends and collaborative networks revealed. Brain Res 2024; 1822:148634. [PMID: 37848120 DOI: 10.1016/j.brainres.2023.148634] [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: 06/21/2023] [Revised: 09/19/2023] [Accepted: 10/14/2023] [Indexed: 10/19/2023]
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI), initially proposed by Biswal et al. in 1995, has emerged as a pivotal facet of neuroimaging research. Its ability to examine brain activity during the resting state without the need for explicit tasks or stimuli has made it an integral component of brain imaging studies. In recent years, rs-fMRI has witnessed substantial growth and found widespread application in the investigation of functional connectivity within the brain. To delineate the developmental trajectory of rs-fMRI over the past two decades, we conducted a comprehensive analysis using bibliometric tool Citespace. Our analysis encompassed publication trends, authorship networks, institutional affiliations, international collaborations, as well as emergent themes in references and keywords. Our study reveals a remarkable increase in the volume of rs-fMRI publications over the past two decades, underscoring the burgeoning interest and potential within this field. Harvard University stands out as the institution with the highest number of research papers published in the realm of RS-fMRI, while the United States holds the highest overall influence in this domain. The recent emergence of keywords such as "machine learning" and "default mode," coupled with citation surges in reference to rs-fMRI, have paved new avenues for research within this field. Our study underscores the critical importance of integrating machine learning techniques into rs-fMRI investigations, offering valuable insights into brain function and disease diagnosis. These findings hold profound significance for the field of neuroscience and may furnish insights for future research employing rs-fMRI as a diagnostic tool for a wide array of neurological disorders, thus emphasizing its pivotal role and potential as a tool for investigating brain functionality.
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Affiliation(s)
- Wenzhuo Wei
- Research Centre for Translational Medicine, the Second Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China; Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Kaiyuan Zhang
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jin Chang
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Shuyu Zhang
- School of Psychology, the Australian National University, Australian
| | - Lijun Ma
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Huixue Wang
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Mi Zhang
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Zhenyue Zu
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Linxi Yang
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Fenglan Chen
- Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China
| | - Chuan Fan
- Department of Psychiatry, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.
| | - Xiaoming Li
- Research Centre for Translational Medicine, the Second Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China; Department of Medical Psychology, School of Mental Health and Psychological Science, Anhui Medical University, Hefei, Anhui, China.
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8
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Angeli PA, DiNicola LM, Saadon-Grosman N, Eldaief MC, Buckner RL. Specialization of the Human Hippocampal Long Axis Revisited. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.19.572264. [PMID: 38187548 PMCID: PMC10769203 DOI: 10.1101/2023.12.19.572264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
The hippocampus possesses anatomical differences along its long axis. Here the functional specialization of the human hippocampal long axis was explored using network-anchored precision functional MRI (N = 11) paired with behavioral analyses (N=266). Functional connectivity analyses demonstrated that the anterior hippocampus was preferentially correlated with a cerebral network associated with remembering, while the posterior hippocampus was correlated with a distinct network associated with behavioral salience. Seed regions placed within the hippocampus recapitulated the distinct cerebral networks. Functional characterization using task data within the same intensively sampled individuals discovered a functional double dissociation between the anterior and posterior hippocampal regions. The anterior hippocampal region was sensitive to remembering and imagining the future, specifically tracking the process of scene construction, while the posterior hippocampal region displayed transient responses to targets in an oddball detection task and to transitions between task blocks. These findings suggest specialization along the long axis of the hippocampus with differential responses reflecting the functional properties of the partner cerebral networks.
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Affiliation(s)
- Peter A Angeli
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Lauren M DiNicola
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Noam Saadon-Grosman
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Mark C Eldaief
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
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9
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Crestol A, Rajagopal S, Lissaman R, LaPlume AA, Pasvanis S, Olsen RK, Einstein G, Jacobs EG, Rajah MN. Menopause Status and Within-Group Differences in Chronological Age Affect the Functional Neural Correlates of Spatial Context Memory in Middle-Aged Females. J Neurosci 2023; 43:8756-8768. [PMID: 37903593 PMCID: PMC10727179 DOI: 10.1523/jneurosci.0663-23.2023] [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: 04/13/2023] [Revised: 10/10/2023] [Accepted: 10/11/2023] [Indexed: 11/01/2023] Open
Abstract
Reductions in the ability to encode and retrieve past experiences in rich spatial contextual detail (episodic memory) are apparent by midlife-a time when most females experience spontaneous menopause. Yet, little is known about how menopause status affects episodic memory-related brain activity at encoding and retrieval in middle-aged premenopausal and postmenopausal females, and whether any observed group differences in brain activity and memory performance correlate with chronological age within group. We conducted an event-related task fMRI study of episodic memory for spatial context to address this knowledge gap. Multivariate behavioral partial least squares was used to investigate how chronological age and retrieval accuracy correlated with brain activity in 31 premenopausal females (age range, 39.55-53.30 years; mean age, 44.28 years; SD age, 3.12 years) and 41 postmenopausal females (age range, 46.70-65.14 years; mean age, 57.56 years; SD age, 3.93 years). We found that postmenopausal status, and advanced age within postmenopause, was associated with lower spatial context memory. The fMRI analysis showed that only in postmenopausal females, advanced age was correlated with decreased activity in occipitotemporal, parahippocampal, and inferior parietal cortices during encoding and retrieval, and poorer spatial context memory performance. In contrast, only premenopausal females exhibited an overlap in encoding and retrieval activity in angular gyrus, midline cortical regions, and prefrontal cortex, which correlated with better spatial context retrieval accuracy. These results highlight how menopause status and chronological age, nested within menopause group, affect episodic memory and its neural correlates at midlife.SIGNIFICANCE STATEMENT This is the first fMRI study to examine how premenopause and postmenopause status affect the neural correlates of episodic memory encoding and retrieval, and how chronological age contributes to any observed group similarities and differences. We found that both menopause status (endocrine age) and chronological age affect spatial context memory and its neural correlates. Menopause status directly affected the direction of age-related and performance-related correlations with brain activity in inferior parietal, parahippocampal, and occipitotemporal cortices across encoding and retrieval. Moreover, we found that only premenopausal females exhibited cortical reinstatement of encoding-related activity in midline cortical, prefrontal, and angular gyrus, at retrieval. This suggests that spatial context memory abilities may rely on distinct brain systems at premenopause compared with postmenopause.
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Affiliation(s)
- Arielle Crestol
- Integrated Program in Neuroscience, McGill University, Montréal, Quebec H3A 1A1, Canada
| | | | - Rikki Lissaman
- Douglas Mental Health University Institute, Verdun, Quebec H4H 1R3, Canada
- Department of Psychiatry, McGill University, Montréal, Quebec H3A 1A1, Canada
| | - Annalise A LaPlume
- Douglas Mental Health University Institute, Verdun, Quebec H4H 1R3, Canada
- Department of Psychology, Toronto Metropolitan University, Toronto, Ontario M5B 2K3, Canada
| | | | - Rosanna K Olsen
- Rotman Research Institute, Baycrest Centre and University of Toronto, Toronto, Ontario M6A 2E1, Canada
- Department of Psychology, University of Toronto, Toronto, Ontario M5S 3G3, Canada
| | - Gillian Einstein
- Rotman Research Institute, Baycrest Centre and University of Toronto, Toronto, Ontario M6A 2E1, Canada
- Department of Psychology, University of Toronto, Toronto, Ontario M5S 3G3, Canada
| | - Emily G Jacobs
- Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, California 93106-9660
| | - M Natasha Rajah
- Douglas Mental Health University Institute, Verdun, Quebec H4H 1R3, Canada
- Department of Psychology, McGill University, Montréal, Quebec H3A 1G1, Canada
- Department of Psychiatry, McGill University, Montréal, Quebec H3A 1A1, Canada
- Department of Psychology, Toronto Metropolitan University, Toronto, Ontario M5B 2K3, Canada
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10
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Pagani M, Gutierrez-Barragan D, de Guzman AE, Xu T, Gozzi A. Mapping and comparing fMRI connectivity networks across species. Commun Biol 2023; 6:1238. [PMID: 38062107 PMCID: PMC10703935 DOI: 10.1038/s42003-023-05629-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 11/22/2023] [Indexed: 12/18/2023] Open
Abstract
Technical advances in neuroimaging, notably in fMRI, have allowed distributed patterns of functional connectivity to be mapped in the human brain with increasing spatiotemporal resolution. Recent years have seen a growing interest in extending this approach to rodents and non-human primates to understand the mechanism of fMRI connectivity and complement human investigations of the functional connectome. Here, we discuss current challenges and opportunities of fMRI connectivity mapping across species. We underscore the critical importance of physiologically decoding neuroimaging measures of brain (dys)connectivity via multiscale mechanistic investigations in animals. We next highlight a set of general principles governing the organization of mammalian connectivity networks across species. These include the presence of evolutionarily conserved network systems, a dominant cortical axis of functional connectivity, and a common repertoire of topographically conserved fMRI spatiotemporal modes. We finally describe emerging approaches allowing comparisons and extrapolations of fMRI connectivity findings across species. As neuroscientists gain access to increasingly sophisticated perturbational, computational and recording tools, cross-species fMRI offers novel opportunities to investigate the large-scale organization of the mammalian brain in health and disease.
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Affiliation(s)
- Marco Pagani
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
- Autism Center, Child Mind Institute, New York, NY, USA
- IMT School for Advanced Studies, Lucca, Italy
| | - Daniel Gutierrez-Barragan
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - A Elizabeth de Guzman
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Ting Xu
- Center for the Integrative Developmental Neuroscience, Child Mind Institute, New York, NY, USA
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy.
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11
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Hotta J, Saari J, Harno H, Kalso E, Forss N, Hari R. Somatotopic disruption of the functional connectivity of the primary sensorimotor cortex in complex regional pain syndrome type 1. Hum Brain Mapp 2023; 44:6258-6274. [PMID: 37837646 PMCID: PMC10619416 DOI: 10.1002/hbm.26513] [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: 01/15/2023] [Revised: 06/16/2023] [Accepted: 09/17/2023] [Indexed: 10/16/2023] Open
Abstract
In complex regional pain syndrome (CRPS), the representation area of the affected limb in the primary sensorimotor cortex (SM1) reacts abnormally during sensory stimulation and motor actions. We recorded 3T functional magnetic resonance imaging resting-state data from 17 upper-limb CRPS type 1 patients and 19 healthy control subjects to identify alterations of patients' SM1 function during spontaneous pain and to find out how the spatial distribution of these alterations were related to peripheral symptoms. Seed-based correlations and independent component analyses indicated that patients' upper-limb SM1 representation areas display (i) reduced interhemispheric connectivity, associated with the combined effect of intensity and spatial extent of limb pain, (ii) increased connectivity with the right anterior insula that positively correlated with the duration of CRPS, (iii) increased connectivity with periaqueductal gray matter, and (iv) disengagement from the other parts of the SM1 network. These findings, now reported for the first time in CRPS, parallel the alterations found in patients suffering from other chronic pain conditions or from limb denervation; they also agree with findings in healthy persons who are exposed to experimental pain or have used their limbs asymmetrically. Our results suggest that CRPS is associated with a sustained and somatotopically specific alteration of SM1 function, that has correspondence to the spatial distribution of the peripheral manifestations and to the duration of the syndrome.
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Affiliation(s)
- Jaakko Hotta
- Department of Neuroscience and Biomedical EngineeringAalto University School of ScienceEspooFinland
- Aalto NeuroImagingAalto UniversityEspooFinland
- Department of NeurologyHelsinki University Hospital and Clinical Neurosciences, Neurology, University of HelsinkiHelsinkiFinland
| | - Jukka Saari
- Department of Neuroscience and Biomedical EngineeringAalto University School of ScienceEspooFinland
- Aalto NeuroImagingAalto UniversityEspooFinland
- Department of Applied PhysicsUniversity of Eastern FinlandKuopioFinland
| | - Hanna Harno
- Department of NeurologyHelsinki University Hospital and Clinical Neurosciences, Neurology, University of HelsinkiHelsinkiFinland
- Department of Anaesthesiology, Intensive Care and Pain MedicineUniversity of Helsinki and Helsinki University HospitalHelsinkiFinland
| | - Eija Kalso
- Department of Anaesthesiology, Intensive Care and Pain MedicineUniversity of Helsinki and Helsinki University HospitalHelsinkiFinland
| | - Nina Forss
- Department of Neuroscience and Biomedical EngineeringAalto University School of ScienceEspooFinland
- Department of NeurologyHelsinki University Hospital and Clinical Neurosciences, Neurology, University of HelsinkiHelsinkiFinland
| | - Riitta Hari
- Department of Neuroscience and Biomedical EngineeringAalto University School of ScienceEspooFinland
- Department of Art and MediaAalto University School of Arts, Design and ArchitectureHelsinkiFinland
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12
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Luckett PH, Park KY, Lee JJ, Lenze EJ, Wetherell JL, Eyler L, Snyder AZ, Ances BM, Shimony JS, Leuthardt EC. Data-efficient resting-state functional magnetic resonance imaging brain mapping with deep learning. J Neurosurg 2023; 139:1258-1269. [PMID: 37060318 PMCID: PMC10576012 DOI: 10.3171/2023.3.jns2314] [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: 01/04/2023] [Accepted: 03/01/2023] [Indexed: 04/16/2023]
Abstract
OBJECTIVE Resting-state functional MRI (RS-fMRI) enables the mapping of function within the brain and is emerging as an efficient tool for the presurgical evaluation of eloquent cortex. Models capable of reliable and precise mapping of resting-state networks (RSNs) with a reduced scanning time would lead to improved patient comfort while reducing the cost per scan. The aims of the present study were to develop a deep 3D convolutional neural network (3DCNN) capable of voxel-wise mapping of language (LAN) and motor (MOT) RSNs with minimal quantities of RS-fMRI data. METHODS Imaging data were gathered from multiple ongoing studies at Washington University School of Medicine and other thoroughly characterized, publicly available data sets. All study participants (n = 2252 healthy adults) were cognitively screened and completed structural neuroimaging and RS-fMRI. Random permutations of RS-fMRI regions of interest were used to train a 3DCNN. After training, model inferences were compared using varying amounts of RS-fMRI data from the control data set as well as 5 patients with glioblastoma multiforme. RESULTS The trained model achieved 96% out-of-sample validation accuracy on data encompassing a large age range collected on multiple scanner types and varying sequence parameters. Testing on out-of-sample control data showed 97.9% similarity between results generated using either 50 or 200 RS-fMRI time points, corresponding to approximately 2.5 and 10 minutes, respectively (96.9% LAN, 96.3% MOT true-positive rate). In evaluating data from patients with brain tumors, the 3DCNN was able to accurately map LAN and MOT networks despite structural and functional alterations. CONCLUSIONS Functional maps produced by the 3DCNN can inform surgical planning in patients with brain tumors in a time-efficient manner. The authors present a highly efficient method for presurgical functional mapping and thus improved functional preservation in patients with brain tumors.
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Affiliation(s)
- Patrick H. Luckett
- Division of Neurotechnology, Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri
| | - Ki Yun Park
- Division of Neurotechnology, Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri
- Department of Neuroscience, Washington University School of Medicine, St. Louis, Missouri
| | - John J. Lee
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Eric J Lenze
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Julie L Wetherell
- Mental Health Impact Unit 3, VA San Diego Healthcare System, San Diego, California
- Department of Psychiatry, University of California, San Diego, California
| | - Lisa Eyler
- Department of Psychiatry, University of California, San Diego, California
| | - Abraham Z. Snyder
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri
| | - Beau M. Ances
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri
| | - Joshua S. Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Eric C. Leuthardt
- Division of Neurotechnology, Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri
- Department of Neuroscience, Washington University School of Medicine, St. Louis, Missouri
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO
- Center for Innovation in Neuroscience and Technology, Division of Neurotechnology, Washington University School of Medicine, St. Louis, MO
- Brain Laser Center, Washington University School of Medicine, St. Louis, Missouri
- National Center for Adaptive Neurotechnologies
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13
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Nakajima R, Shirakami A, Tsumura H, Matsuda K, Nakamura E, Shimono M. Mutual generation in neuronal activity across the brain via deep neural approach, and its network interpretation. Commun Biol 2023; 6:1105. [PMID: 37907640 PMCID: PMC10618281 DOI: 10.1038/s42003-023-05453-2] [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: 04/26/2023] [Accepted: 10/11/2023] [Indexed: 11/02/2023] Open
Abstract
In the brain, many regions work in a network-like association, yet it is not known how durable these associations are in terms of activity and could survive without structural connections. To assess the association or similarity between brain regions with a generating approach, this study evaluated the similarity of activities of neurons within each region after disconnecting between regions. The "generation" approach here refers to using a multi-layer LSTM (Long Short-Term Memory) model to learn the rules of activity generation in one region and then apply that knowledge to generate activity in other regions. Surprisingly, the results revealed that activity generation from one region to disconnected regions was possible with similar accuracy to generation between the same regions in many cases. Notably, firing rates and synchronization of firing between neuron pairs, often used as neuronal representations, could be reproduced with precision. Additionally, accuracies were associated with the relative angle between brain regions and the strength of the structural connections that initially connected them. This outcome enables us to look into trends governing non-uniformity of the cortex based on the potential to generate informative data and reduces the need for animal experiments.
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Affiliation(s)
- Ryota Nakajima
- Kyoto University, Graduate School of Medicine, Kyoto, Japan
| | | | - Hayato Tsumura
- Kyoto University, Graduate School of Medicine, Kyoto, Japan
| | - Kouki Matsuda
- Kyoto University, Graduate School of Medicine, Kyoto, Japan
| | - Eita Nakamura
- Kyoto University, Graduate School of Informatics, Kyoto, Japan
- Kyoto University, The Hakubi Center for Advanced Research, Kyoto, Japan
| | - Masanori Shimono
- Kyoto University, Graduate School of Medicine, Kyoto, Japan.
- Kyoto University, The Hakubi Center for Advanced Research, Kyoto, Japan.
- Osaka University, Graduate School of Informatics, Kyoto, Japan.
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14
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Kosakowski HL, Saadon-Grosman N, Du J, Eldaief ME, Buckner RL. Human Striatal Association Megaclusters. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.03.560666. [PMID: 37873093 PMCID: PMC10592903 DOI: 10.1101/2023.10.03.560666] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
The striatum receives projections from multiple regions of the cerebral cortex consistent with its role in diverse motor, affective, and cognitive functions. Supporting cognitive functions, the caudate receives projections from cortical association regions. Building on recent insights about the details of how multiple cortical networks are specialized for distinct aspects of higher-order cognition, we revisited caudate organization using within-individual precision neuroimaging (n=2, each participant scanned 31 times). Detailed analysis revealed that the caudate has side-by-side zones that are coupled to at least Give distinct distributed association networks, paralleling the specialization observed in the cerebral cortex. Examining correlation maps from closely juxtaposed seed regions in the caudate recapitulated the Give distinct cerebral networks including their multiple spatially distributed regions. These results extend the general notion of parallel specialized basal ganglia circuits, with the additional discovery that even within the caudate, there is Gine-grained separation of multiple distinct higher-order networks.
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Affiliation(s)
- Heather L Kosakowski
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Noam Saadon-Grosman
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Jingnan Du
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Mark E Eldaief
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Neurology, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
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15
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Xing XX, Gao X, Jiang C. Individual Variability of Human Cortical Spontaneous Activity by 3T/7T fMRI. Neuroscience 2023; 528:117-128. [PMID: 37544577 DOI: 10.1016/j.neuroscience.2023.07.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 07/29/2023] [Accepted: 07/31/2023] [Indexed: 08/08/2023]
Abstract
Mapping variability in cortical spontaneous activity (CSA) is an essential goal of understanding various sources of dark brain energy in human neuroscience. CSA was traditionally characterized using resting-state functional MRI (rfMRI) at 1.5T or 3T magnets while recently with 7T-rfMRI. However, the utility and interpretability of 7T-rfMRI must first be established for its variability. By leveraging rfMRI data from the Human Connectome Project (HCP), we derived CSA metrics with 3T-rfMRI and 7T-rfMRI for the same 84 healthy participants (52 females). The 7T-rfMRI produces different CSA metrics at multiple spatial-scales and their variability from the 3T-rfMRI. These differences were spatially dependent and varied according to specific cortical organization. For the amplitude metric, 7T-rfMRI enhanced its spatial contrasts in the anterior cortex but weakened it in the posterior cortex. An opposite pattern was observed for the connectivity metrics. The reliability changes of these metrics were scale dependent, indicating enhanced reliability for connectivity but weakened reliability for amplitude by 7T-rfMRI. These effects were primarily located in the high-order associate cortex, parsing the corresponding changes in individual differences with respect to 7T-rfMRI: (1) higher connectivity variability between participants and the lower connectivity variability within individual participants, and (2) lower amplitude variability between participants and higher amplitude variability within participants. Our work, for the first time, demonstrated the variability of the human CSA across space, rfMRI settings/platforms, and individuals. We discussed the statistical implications of our findings on CSA-based experimental designs and reproducible neuroscience as well as their translational value for personalized applications.
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Affiliation(s)
- Xiu-Xia Xing
- Department of Applied Mathematics, College of Mathematics, Faculty of Science, Beijing University of Technology, Beijing 100124, China.
| | - Xiao Gao
- School of Psychology, Capital Normal University, Beijing 100048, China
| | - Chao Jiang
- Faculty of Psychology, Southwest University, Chongqing 400715, China
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16
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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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17
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Dadario NB, Sughrue ME. The functional role of the precuneus. Brain 2023; 146:3598-3607. [PMID: 37254740 DOI: 10.1093/brain/awad181] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 05/08/2023] [Accepted: 05/10/2023] [Indexed: 06/01/2023] Open
Abstract
Recent advancements in computational approaches and neuroimaging techniques have refined our understanding of the precuneus. While previously believed to be largely a visual processing region, the importance of the precuneus in complex cognitive functions has been previously less familiar due to a lack of focal lesions in this deeply seated region, but also a poor understanding of its true underlying anatomy. Fortunately, recent studies have revealed significant information on the structural and functional connectivity of this region, and this data has provided a more detailed mechanistic understanding of the importance of the precuneus in healthy and pathologic states. Through improved resting-state functional MRI analyses, it has become clear that the function of the precuneus can be better understood based on its functional association with large scale brain networks. Dual default mode network systems have been well explained in recent years in supporting episodic memory and theory of mind; however, a novel 'para-cingulate' network, which is a subnetwork of the larger central executive network, with likely significant roles in self-referential processes and related psychiatric symptoms is introduced here and requires further clarification. Importantly, detailed anatomic studies on the precuneus structural connectivity inside and beyond the cingulate cortex has demonstrated the presence of large structural white matter connections, which provide an additional layer of meaning to the structural-functional significance of this region and its association with large scale brain networks. Together, the structural-functional connectivity of the precuneus has provided central elements which can model various neurodegenerative diseases and psychiatric disorders, such as Alzheimer's disease and depression.
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Affiliation(s)
- Nicholas B Dadario
- Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ 07102, USA
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18
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Kwon Y, Salvo JJ, Anderson N, Holubecki AM, Lakshman M, Yoo K, Kay K, Gratton C, Braga RM. Situating the parietal memory network in the context of multiple parallel distributed networks using high-resolution functional connectivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.16.553585. [PMID: 37645962 PMCID: PMC10462098 DOI: 10.1101/2023.08.16.553585] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
A principle of brain organization is that networks serving higher cognitive functions are widely distributed across the brain. One exception has been the parietal memory network (PMN), which plays a role in recognition memory but is often defined as being restricted to posteromedial association cortex. We hypothesized that high-resolution estimates of the PMN would reveal small regions that had been missed by prior approaches. High-field 7T functional magnetic resonance imaging (fMRI) data from extensively sampled participants was used to define the PMN within individuals. The PMN consistently extended beyond the core posteromedial set to include regions in the inferior parietal lobule; rostral, dorsal, medial, and ventromedial prefrontal cortex; the anterior insula; and ramus marginalis of the cingulate sulcus. The results suggest that, when fine-scale anatomy is considered, the PMN matches the expected distributed architecture of other association networks, reinforcing that parallel distributed networks are an organizing principle of association cortex.
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Affiliation(s)
- Y Kwon
- Northwestern University Department of Neurology
| | - J J Salvo
- Northwestern University Department of Neurology
| | - N Anderson
- Northwestern University Department of Neurology
| | | | - M Lakshman
- Northwestern University Department of Neurology
| | - K Yoo
- Yale University Department of Psychology
| | - K Kay
- University of Minnesota Department of Radiology
| | - C Gratton
- Florida State University Department of Psychology
| | - R M Braga
- Northwestern University Department of Neurology
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19
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Du J, DiNicola LM, Angeli PA, Saadon-Grosman N, Sun W, Kaiser S, Ladopoulou J, Xue A, Yeo BTT, Eldaief MC, Buckner RL. Within-Individual Organization of the Human Cerebral Cortex: Networks, Global Topography, and Function. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.08.552437. [PMID: 37609246 PMCID: PMC10441314 DOI: 10.1101/2023.08.08.552437] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
The human cerebral cortex is populated by specialized regions that are organized into networks. Here we estimated networks using a Multi-Session Hierarchical Bayesian Model (MS-HBM) applied to intensively sampled within-individual functional MRI (fMRI) data. The network estimation procedure was initially developed and tested in two participants (each scanned 31 times) and then prospectively applied to 15 new participants (each scanned 8 to 11 times). Detailed analysis of the networks revealed a global organization. Locally organized first-order sensory and motor networks were surrounded by spatially adjacent second-order networks that also linked to distant regions. Third-order networks each possessed regions distributed widely throughout association cortex. Moreover, regions of distinct third-order networks displayed side-by-side juxtapositions with a pattern that repeated similarly across multiple cortical zones. We refer to these as Supra-Areal Association Megaclusters (SAAMs). Within each SAAM, two candidate control regions were typically adjacent to three separate domain-specialized regions. Independent task data were analyzed to explore functional response properties. The somatomotor and visual first-order networks responded to body movements and visual stimulation, respectively. A subset of the second-order networks responded to transients in an oddball detection task, consistent with a role in orienting to salient or novel events. The third-order networks, including distinct regions within each SAAM, showed two levels of functional specialization. Regions linked to candidate control networks responded to working memory load across multiple stimulus domains. The remaining regions within each SAAM did not track working memory load but rather dissociated across language, social, and spatial / episodic processing domains. These results support a model of the cerebral cortex in which progressively higher-order networks nest outwards from primary sensory and motor cortices. Within the apex zones of association cortex there is specialization of large-scale networks that divides domain-flexible from domain-specialized regions repeatedly across parietal, temporal, and prefrontal cortices. We discuss implications of these findings including how repeating organizational motifs may emerge during development.
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Affiliation(s)
- Jingnan Du
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Lauren M DiNicola
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Peter A Angeli
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Noam Saadon-Grosman
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Wendy Sun
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Stephanie Kaiser
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Joanna Ladopoulou
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Aihuiping Xue
- Centre for Sleep & Cognition & Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - B T Thomas Yeo
- Centre for Sleep & Cognition & Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - Mark C Eldaief
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Psychiatry, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
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20
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Wang J, Li H, Qu G, Cecil KM, Dillman JR, Parikh NA, He L. Dynamic weighted hypergraph convolutional network for brain functional connectome analysis. Med Image Anal 2023; 87:102828. [PMID: 37130507 PMCID: PMC10247416 DOI: 10.1016/j.media.2023.102828] [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: 10/14/2022] [Revised: 04/14/2023] [Accepted: 04/18/2023] [Indexed: 05/04/2023]
Abstract
The hypergraph structure has been utilized to characterize the brain functional connectome (FC) by capturing the high order relationships among multiple brain regions of interest (ROIs) compared with a simple graph. Accordingly, hypergraph neural network (HGNN) models have emerged and provided efficient tools for hypergraph embedding learning. However, most existing HGNN models can only be applied to pre-constructed hypergraphs with a static structure during model training, which might not be a sufficient representation of the complex brain networks. In this study, we propose a dynamic weighted hypergraph convolutional network (dwHGCN) framework to consider a dynamic hypergraph with learnable hyperedge weights. Specifically, we generate hyperedges based on sparse representation and calculate the hyper similarity as node features. The hypergraph and node features are fed into a neural network model, where the hyperedge weights are updated adaptively during training. The dwHGCN facilitates the learning of brain FC features by assigning larger weights to hyperedges with higher discriminative power. The weighting strategy also improves the interpretability of the model by identifying the highly active interactions among ROIs shared by a common hyperedge. We validate the performance of the proposed model on two classification tasks with three paradigms functional magnetic resonance imaging (fMRI) data from Philadelphia Neurodevelopmental Cohort. Experimental results demonstrate the superiority of our proposed method over existing hypergraph neural networks. We believe our model can be applied to other applications in neuroimaging for its strength in representation learning and interpretation.
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Affiliation(s)
- Junqi Wang
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Hailong Li
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Gang Qu
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
| | - Kim M Cecil
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Jonathan R Dillman
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Nehal A Parikh
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Lili He
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Artificial Intelligence Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
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21
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Shahsavarani S, Thibodeaux DN, Xu W, Kim SH, Lodgher F, Nwokeabia C, Cambareri M, Yagielski AJ, Zhao HT, Handwerker DA, Gonzalez-Castillo J, Bandettini PA, Hillman EMC. Cortex-wide neural dynamics predict behavioral states and provide a neural basis for resting-state dynamic functional connectivity. Cell Rep 2023; 42:112527. [PMID: 37243588 PMCID: PMC10592480 DOI: 10.1016/j.celrep.2023.112527] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 03/14/2023] [Accepted: 05/01/2023] [Indexed: 05/29/2023] Open
Abstract
Although resting-state functional magnetic resonance imaging (fMRI) studies have observed dynamically changing brain-wide networks of correlated activity, fMRI's dependence on hemodynamic signals makes results challenging to interpret. Meanwhile, emerging techniques for real-time recording of large populations of neurons have revealed compelling fluctuations in neuronal activity across the brain that are obscured by traditional trial averaging. To reconcile these observations, we use wide-field optical mapping to simultaneously record pan-cortical neuronal and hemodynamic activity in awake, spontaneously behaving mice. Some components of observed neuronal activity clearly represent sensory and motor function. However, particularly during quiet rest, strongly fluctuating patterns of activity across diverse brain regions contribute greatly to interregional correlations. Dynamic changes in these correlations coincide with changes in arousal state. Simultaneously acquired hemodynamics depict similar brain-state-dependent correlation shifts. These results support a neural basis for dynamic resting-state fMRI, while highlighting the importance of brain-wide neuronal fluctuations in the study of brain state.
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Affiliation(s)
- Somayeh Shahsavarani
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA; Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - David N Thibodeaux
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Weihao Xu
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Sharon H Kim
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Fatema Lodgher
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Chinwendu Nwokeabia
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Morgan Cambareri
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Alexis J Yagielski
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Hanzhi T Zhao
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Daniel A Handwerker
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Peter A Bandettini
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA; Functional MRI Core Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Elizabeth M C Hillman
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA; Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA.
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22
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Wei Y, Womer FY, Sun K, Zhu Y, Sun D, Duan J, Zhang R, Wei S, Jiang X, Zhang Y, Tang Y, Zhang X, Wang F. Applying dimensional psychopathology: transdiagnostic prediction of executive cognition using brain connectivity and inflammatory biomarkers. Psychol Med 2023; 53:3557-3567. [PMID: 35536000 DOI: 10.1017/s0033291722000174] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
BACKGROUND The association between executive dysfunction, brain dysconnectivity, and inflammation is a prominent feature across major psychiatric disorders (MPDs), schizophrenia, bipolar disorder, and major depressive disorder. A dimensional approach is warranted to delineate their mechanistic interplay across MPDs. METHODS This single site study included a total of 1543 participants (1058 patients and 485 controls). In total, 1169 participants underwent diffusion tensor and resting-state functional magnetic resonance imaging (745 patients and 379 controls completed the Wisconsin Card Sorting Test). Fractional anisotropy (FA) and regional homogeneity (ReHo) assessed structural and functional connectivity, respectively. Pro-inflammatory cytokine levels [interleukin (IL)-1β, IL-6, and tumor necrosis factor-α] were obtained in 325 participants using blood samples collected with 24 h of scanning. Group differences were determined for main measures, and correlation and mediation analyses and machine learning prediction modeling were performed. RESULTS Executive deficits were associated with decreased FA, increased ReHo, and elevated IL-1β and IL-6 levels across MPDs, compared to controls. FA and ReHo alterations in fronto-limbic-striatal regions contributed to executive deficits. IL-1β mediated the association between FA and cognition, and IL-6 mediated the relationship between ReHo and cognition. Executive cognition was better predicted by both brain connectivity and cytokine measures than either one alone for FA-IL-1β and ReHo-IL-6. CONCLUSIONS Transdiagnostic associations among brain connectivity, inflammation, and executive cognition exist across MPDs, implicating common neurobiological substrates and mechanisms for executive deficits in MPDs. Further, inflammation-related brain dysconnectivity within fronto-limbic-striatal regions may represent a transdiagnostic dimension underlying executive dysfunction that could be leveraged to advance treatment.
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Affiliation(s)
- Yange Wei
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Fay Y Womer
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63130, USA
| | - Kaijin Sun
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Yue Zhu
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Dandan Sun
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Jia Duan
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Ran Zhang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Shengnan Wei
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Xiaowei Jiang
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Yanbo Zhang
- Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, T6G 2B7, Canada
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Xizhe Zhang
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 210001, China
| | - Fei Wang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China
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23
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Soyuhos O, Baldauf D. Functional connectivity fingerprints of the frontal eye field and inferior frontal junction suggest spatial versus nonspatial processing in the prefrontal cortex. Eur J Neurosci 2023; 57:1114-1140. [PMID: 36789470 DOI: 10.1111/ejn.15936] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 01/28/2023] [Accepted: 02/08/2023] [Indexed: 02/16/2023]
Abstract
Neuroimaging evidence suggests that the frontal eye field (FEF) and inferior frontal junction (IFJ) govern the encoding of spatial and nonspatial (such as feature- or object-based) representations, respectively, both during visual attention and working memory tasks. However, it is still unclear whether such contrasting functional segregation is also reflected in their underlying functional connectivity patterns. Here, we hypothesized that FEF has predominant functional coupling with spatiotopically organized regions in the dorsal ('where') visual stream whereas IFJ has predominant functional connectivity with the ventral ('what') visual stream. We applied seed-based functional connectivity analyses to temporally high-resolving resting-state magnetoencephalography (MEG) recordings. We parcellated the brain according to the multimodal Glasser atlas and tested, for various frequency bands, whether the spontaneous activity of each parcel in the ventral and dorsal visual pathway has predominant functional connectivity with FEF or IFJ. The results show that FEF has a robust power correlation with the dorsal visual pathway in beta and gamma bands. In contrast, anterior IFJ (IFJa) has a strong power coupling with the ventral visual stream in delta, beta and gamma oscillations. Moreover, while FEF is phase-coupled with the superior parietal lobe in the beta band, IFJa is phase-coupled with the middle and inferior temporal cortex in delta and gamma oscillations. We argue that these intrinsic connectivity fingerprints are congruent with each brain region's function. Therefore, we conclude that FEF and IFJ have dissociable connectivity patterns that fit their respective functional roles in spatial versus nonspatial top-down attention and working memory control.
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Affiliation(s)
- Orhan Soyuhos
- Centre for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy.,Center for Neuroscience, University of California, Davis, California, USA
| | - Daniel Baldauf
- Centre for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy
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24
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Chicos L, Rangaprakash D, Barry R, Herr H. Resting state neurophysiology of agonist-antagonist myoneural interface in persons with transtibial amputation. RESEARCH SQUARE 2023:rs.3.rs-2362961. [PMID: 36798194 PMCID: PMC9934762 DOI: 10.21203/rs.3.rs-2362961/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
The agonist-antagonist myoneural interface (AMI) is a novel amputation surgery that preserves sensorimotor signaling mechanisms of the central-peripheral nervous systems. Our first neuroimaging study investigating AMI subjects (Srinivasan et al., Sci. Transl. Med. 2020) focused on task-based neural signatures, and showed evidence of proprioceptive feedback to the central nervous system. The study of resting state neural activity helps non-invasively characterize the neural patterns that prime task response. In this first study on resting state fMRI in AMI subjects, we compared resting state functional connectivity in patients with transtibial AMI (n=12) and traditional (n=7) amputations, as well as biologically intact control subjects (n=10). We hypothesized that the AMI surgery will induce functional network reorganization that significantly differs from the traditional amputation surgery and also more closely resembles the neural configuration of controls. We found AMI subjects to have lower connectivity with salience and motor seed regions compared to traditional amputees. Additionally, with connections affected in traditional amputees, AMI subjects exhibited a connectivity pattern more closely resembling controls. Lastly, sensorimotor connectivity in amputee cohorts was significantly associated with phantom sensation (R2=0.7, p=0.0008). These findings provide researchers and clinicians with a critical mechanistic understanding of the effects of the AMI surgery on the brain at rest, spearheading future research towards improved prosthetic control and embodiment.
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Affiliation(s)
| | | | - Robert Barry
- Massachusetts General Hospital & Harvard Medical School
| | - Hugh Herr
- Massachusetts Institute of Technology
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25
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Krohn S, von Schwanenflug N, Waschke L, Romanello A, Gell M, Garrett DD, Finke C. A spatiotemporal complexity architecture of human brain activity. SCIENCE ADVANCES 2023; 9:eabq3851. [PMID: 36724223 PMCID: PMC9891702 DOI: 10.1126/sciadv.abq3851] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
The human brain operates in large-scale functional networks. These networks are an expression of temporally correlated activity across brain regions, but how global network properties relate to the neural dynamics of individual regions remains incompletely understood. Here, we show that the brain's network architecture is tightly linked to critical episodes of neural regularity, visible as spontaneous "complexity drops" in functional magnetic resonance imaging signals. These episodes closely explain functional connectivity strength between regions, subserve the propagation of neural activity patterns, and reflect interindividual differences in age and behavior. Furthermore, complexity drops define neural activity states that dynamically shape the connectivity strength, topological configuration, and hierarchy of brain networks and comprehensively explain known structure-function relationships within the brain. These findings delineate a principled complexity architecture of neural activity-a human "complexome" that underpins the brain's functional network organization.
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Affiliation(s)
- Stephan Krohn
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- Corresponding author. (S.K.); (C.F.)
| | - Nina von Schwanenflug
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Leonhard Waschke
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
| | - Amy Romanello
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Martin Gell
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, RWTH Aachen University, Aachen, Germany
| | - Douglas D. Garrett
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
| | - Carsten Finke
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Berlin, Germany
- Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- Corresponding author. (S.K.); (C.F.)
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26
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Zhu H, Huang Z, Yang Y, Su K, Fan M, Zou Y, Li T, Yin D. Activity flow mapping over probabilistic functional connectivity. Hum Brain Mapp 2023; 44:341-361. [PMID: 36647263 PMCID: PMC9842909 DOI: 10.1002/hbm.26044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 07/01/2022] [Accepted: 07/28/2022] [Indexed: 01/25/2023] Open
Abstract
Emerging evidence indicates that activity flow over resting-state network topology allows the prediction of task activations. However, previous studies have mainly adopted static, linear functional connectivity (FC) estimates as activity flow routes. It is unclear whether an intrinsic network topology that captures the dynamic nature of FC can be a better representation of activity flow routes. Moreover, the effects of between- versus within-network connections and tight versus loose (using rest baseline) task contrasts on the prediction of task-evoked activity across brain systems remain largely unknown. In this study, we first propose a probabilistic FC estimation derived from a dynamic framework as a new activity flow route. Subsequently, activity flow mapping was tested using between- and within-network connections separately for each region as well as using a set of tight task contrasts. Our results showed that probabilistic FC routes substantially improved individual-level activity flow prediction. Although it provided better group-level prediction, the multiple regression approach was more dependent on the length of data points at the individual-level prediction. Regardless of FC type, we consistently observed that between-network connections showed a relatively higher prediction performance in higher-order cognitive control than in primary sensorimotor systems. Furthermore, cognitive control systems exhibit a remarkable increase in prediction accuracy with tight task contrasts and a decrease in sensorimotor systems. This work demonstrates that probabilistic FC estimates are promising routes for activity flow mapping and also uncovers divergent influences of connectional topology and task contrasts on activity flow prediction across brain systems with different functional hierarchies.
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Affiliation(s)
- Hengcheng Zhu
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive ScienceEast China Normal UniversityShanghaiChina
| | - Ziyi Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive ScienceEast China Normal UniversityShanghaiChina
| | - Yifeixue Yang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive ScienceEast China Normal UniversityShanghaiChina
| | - Kaiqiang Su
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive ScienceEast China Normal UniversityShanghaiChina
| | - Mingxia Fan
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic ScienceEast China Normal UniversityShanghaiChina
| | - Yong Zou
- Institute of Theoretical Physics, School of Physics and Electronic ScienceEast China Normal UniversityShanghaiChina
| | - Ting Li
- Shanghai Changning Mental Health CenterShanghaiChina
| | - Dazhi Yin
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive ScienceEast China Normal UniversityShanghaiChina
- Shanghai Changning Mental Health CenterShanghaiChina
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27
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Li HX, Lu B, Wang YW, Li XY, Chen X, Yan CG. Neural representations of self-generated thought during think-aloud fMRI. Neuroimage 2023; 265:119775. [PMID: 36455761 DOI: 10.1016/j.neuroimage.2022.119775] [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: 09/10/2022] [Revised: 11/24/2022] [Accepted: 11/27/2022] [Indexed: 11/29/2022] Open
Abstract
Is the brain at rest during the so-called resting state? Ongoing experiences in the resting state vary in unobserved and uncontrolled ways across time, individuals, and populations. However, the role of self-generated thoughts in resting-state fMRI remains largely unexplored. In this study, we collected real-time self-generated thoughts during "resting-state" fMRI scans via the think-aloud method (i.e., think-aloud fMRI), which required participants to report whatever they were currently thinking. We first investigated brain activation patterns during a think-aloud condition and found that significantly activated brain areas included all brain regions required for speech. We then calculated the relationship between divergence in thought content and brain activation during think-aloud and found that divergence in thought content was associated with many brain regions. Finally, we explored the neural representation of self-generated thoughts by performing representational similarity analysis (RSA) at three neural scales: a voxel-wise whole-brain searchlight level, a region-level whole-brain analysis using the Schaefer 400-parcels, and at the systems level using the Yeo seven-networks. We found that "resting-state" self-generated thoughts were distributed across a wide range of brain regions involving all seven Yeo networks. This study highlights the value of considering ongoing experiences during resting-state fMRI and providing preliminary methodological support for think-aloud fMRI.
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Affiliation(s)
- Hui-Xian Li
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China; International Big-Data Center for Depression Research, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Bin Lu
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China; International Big-Data Center for Depression Research, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Yu-Wei Wang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China; International Big-Data Center for Depression Research, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Xue-Ying Li
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China; International Big-Data Center for Depression Research, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Xiao Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China; International Big-Data Center for Depression Research, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China; International Big-Data Center for Depression Research, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
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28
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Zhang K, Du X, Liu X, Su W, Sun Z, Wang M, Du X. Gender differences in brain response to infant emotional faces. BMC Neurosci 2022; 23:79. [PMID: 36575370 PMCID: PMC9793562 DOI: 10.1186/s12868-022-00761-5] [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: 09/16/2022] [Accepted: 11/30/2022] [Indexed: 12/28/2022] Open
Abstract
Infant emotional stimuli can preferentially engage adults' attention and provide valuable information essential for successful interaction between adults and infants. Exploring the neural processes of recognizing infant stimuli promotes better understandings of the mother-infant attachment mechanisms. Here, combining task-functional magnetic resonance imaging (Task-fMRI) and resting-state fMRI (rs-fMRI), we investigated the effects of infants' faces on the brain activity of adults. Two groups including 26 women and 25 men were recruited to participate in the current study. During the task-fMRI, subjects were exposed to images of infant emotional faces (including happy, neutral, and sad) randomly. We found that the brains of women and men reacted differently to infants' faces, and these differential areas are in facial processing, attention, and empathetic networks. The rs-fMRI further showed that the connectivity of the default-mode network-related regions increased in women than in men. Additionally, brain activations in regions related to emotional networks were associated with the empathetic abilities of women. These differences in women might facilitate them to more effective and quick adjustments in behaviors and emotions during the nurturing infant period. The findings provide special implications and insights for understanding the neural processing of reacting to infant cues in adults.
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Affiliation(s)
- Kaihua Zhang
- grid.410585.d0000 0001 0495 1805School of Psychology, Shandong Normal University, Jinan, 250358 Shandong China
| | - Xiaoyu Du
- grid.1008.90000 0001 2179 088XFaculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Victoria, 3010 Australia
| | - Xianling Liu
- grid.411634.50000 0004 0632 4559Department of Medicine Imaging, The People’s Hospital of Jinan Central District, Jinan, 250014 Shandong China
| | - Wei Su
- grid.410585.d0000 0001 0495 1805School of Psychology, Shandong Normal University, Jinan, 250358 Shandong China
| | - Zhenhua Sun
- grid.410747.10000 0004 1763 3680School of Information Science and Engineering, Linyi University, Linyi, 276000 Shandong China
| | - Mengxing Wang
- grid.507037.60000 0004 1764 1277College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, 201318 China
| | - Xiaoxia Du
- grid.412543.50000 0001 0033 4148Department of Psychology, Shanghai University of Sport, No.399 Shanghai Road, Yangpu District, Shanghai, 200438 China
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Adegoke MA, Teter O, Meaney DF. Flexibility of in vitro cortical circuits influences resilience from microtrauma. Front Cell Neurosci 2022; 16:991740. [PMID: 36589287 PMCID: PMC9803265 DOI: 10.3389/fncel.2022.991740] [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: 07/11/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Background Small clusters comprising hundreds to thousands of neurons are an important level of brain architecture that correlates single neuronal properties to fulfill brain function, but the specific mechanisms through which this scaling occurs are not well understood. In this study, we developed an in vitro experimental platform of small neuronal circuits (islands) to probe the importance of structural properties for their development, physiology, and response to microtrauma. Methods Primary cortical neurons were plated on a substrate patterned to promote attachment in clusters of hundreds of cells (islands), transduced with GCaMP6f, allowed to mature until 10-13 days in vitro (DIV), and monitored with Ca2+ as a non-invasive proxy for electrical activity. We adjusted two structural factors-island size and cellular density-to evaluate their role in guiding spontaneous activity and network formation in neuronal islands. Results We found cellular density, but not island size, regulates of circuit activity and network function in this system. Low cellular density islands can achieve many states of activity, while high cellular density biases islands towards a limited regime characterized by low rates of activity and high synchronization, a property we summarized as "flexibility." The injury severity required for an island to lose activity in 50% of its population was significantly higher in low-density, high flexibility islands. Conclusion Together, these studies demonstrate flexible living cortical circuits are more resilient to microtrauma, providing the first evidence that initial circuit state may be a key factor to consider when evaluating the consequences of trauma to the cortex.
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Affiliation(s)
- Modupe A. Adegoke
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, United States
| | - Olivia Teter
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, United States
| | - David F. Meaney
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, United States,Department of Neurosurgery, Penn Center for Brain Injury and Repair, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States,*Correspondence: David F. Meaney,
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30
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Madden MB, Stewart BW, White MG, Krimmel SR, Qadir H, Barrett FS, Seminowicz DA, Mathur BN. A role for the claustrum in cognitive control. Trends Cogn Sci 2022; 26:1133-1152. [PMID: 36192309 PMCID: PMC9669149 DOI: 10.1016/j.tics.2022.09.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 09/02/2022] [Accepted: 09/07/2022] [Indexed: 01/12/2023]
Abstract
Early hypotheses of claustrum function were fueled by neuroanatomical data and yielded suggestions that the claustrum is involved in processes ranging from salience detection to multisensory integration for perceptual binding. While these hypotheses spurred useful investigations, incompatibilities inherent in these views must be reconciled to further conceptualize claustrum function amid a wealth of new data. Here, we review the varied models of claustrum function and synthesize them with developments in the field to produce a novel functional model: network instantiation in cognitive control (NICC). This model proposes that frontal cortices direct the claustrum to flexibly instantiate cortical networks to subserve cognitive control. We present literature support for this model and provide testable predictions arising from this conceptual framework.
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Affiliation(s)
- Maxwell B Madden
- Department of Pharmacology, School of Medicine, University of Maryland, Baltimore, MD 21201, USA
| | - Brent W Stewart
- Department of Pharmacology, School of Medicine, University of Maryland, Baltimore, MD 21201, USA; Department of Neural and Pain Sciences, School of Dentistry, University of Maryland, Baltimore, MD 21201, USA; Center to Advance Chronic Pain Research, University of Maryland, Baltimore, MD 21201, USA
| | - Michael G White
- Department of Pharmacology, School of Medicine, University of Maryland, Baltimore, MD 21201, USA
| | - Samuel R Krimmel
- Department of Neural and Pain Sciences, School of Dentistry, University of Maryland, Baltimore, MD 21201, USA; Center to Advance Chronic Pain Research, University of Maryland, Baltimore, MD 21201, USA
| | - Houman Qadir
- Department of Pharmacology, School of Medicine, University of Maryland, Baltimore, MD 21201, USA
| | - Frederick S Barrett
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21224, USA; Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21224, USA; Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD 21224, USA
| | - David A Seminowicz
- Department of Neural and Pain Sciences, School of Dentistry, University of Maryland, Baltimore, MD 21201, USA; Center to Advance Chronic Pain Research, University of Maryland, Baltimore, MD 21201, USA; Department of Medical Biophysics, Schulich School of Medicine & Dentistry, University of Western Ontario, London, Ontario, Canada
| | - Brian N Mathur
- Department of Pharmacology, School of Medicine, University of Maryland, Baltimore, MD 21201, USA; Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD 21201, USA.
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31
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Argilés M, Sunyer-Grau B, Arteche-Fernandez S, Peña-Gómez C. Functional connectivity of brain networks with three monochromatic wavelengths: a pilot study using resting-state functional magnetic resonance imaging. Sci Rep 2022; 12:16197. [PMID: 36171254 PMCID: PMC9519584 DOI: 10.1038/s41598-022-20668-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 09/16/2022] [Indexed: 11/28/2022] Open
Abstract
Exposure to certain monochromatic wavelengths can affect non-visual brain regions. Growing research indicates that exposure to light can have a positive impact on health-related problems such as spring asthenia, circadian rhythm disruption, and even bipolar disorders and Alzheimer’s. However, the extent and location of changes in brain areas caused by exposure to monochromatic light remain largely unknown. This pilot study (N = 7) using resting-state functional magnetic resonance shows light-dependent functional connectivity patterns on brain networks. We demonstrated that 1 min of blue, green, or red light exposure modifies the functional connectivity (FC) of a broad range of visual and non-visual brain regions. Largely, we observed: (i) a global decrease in FC in all the networks but the salience network after blue light exposure, (ii) a global increase in FC after green light exposure, particularly noticeable in the left hemisphere, and (iii) a decrease in FC on attentional networks coupled with a FC increase in the default mode network after red light exposure. Each one of the FC patterns appears to be best arranged to perform better on tasks associated with specific cognitive domains. Results can be relevant for future research on the impact of light stimulation on brain function and in a variety of health disciplines.
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Affiliation(s)
- Marc Argilés
- School of Optics and Optometry, Universitat Politècnica de Catalunya, Terrassa, Catalonia, Spain.
| | - Bernat Sunyer-Grau
- School of Optics and Optometry, Universitat Politècnica de Catalunya, Terrassa, Catalonia, Spain
| | - Sílvia Arteche-Fernandez
- School of Optics and Optometry, Universitat Politècnica de Catalunya, Terrassa, Catalonia, Spain
| | - Cleofé Peña-Gómez
- BarcelonaBeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Catalonia, Spain
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32
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Kobayashi S, O'Hashi K, Kobayashi M. Repetitive nociceptive stimulation increases spontaneous neural activation similar to nociception-induced activity in mouse insular cortex. Sci Rep 2022; 12:15190. [PMID: 36071208 PMCID: PMC9452502 DOI: 10.1038/s41598-022-19562-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 08/31/2022] [Indexed: 11/09/2022] Open
Abstract
Recent noninvasive neuroimaging technology has revealed that spatiotemporal patterns of cortical spontaneous activity observed in chronic pain patients are different from those in healthy subjects, suggesting that the spontaneous cortical activity plays a key role in the induction and/or maintenance of chronic pain. However, the mechanisms of the spontaneously emerging activities supposed to be induced by nociceptive inputs remain to be established. In the present study, we investigated spontaneous cortical activities in sessions before and after electrical stimulation of the periodontal ligament (PDL) by applying wide-field and two-photon calcium imaging to anesthetized GCaMP6s transgenic mice. First, we identified the sequential cortical activation patterns from the primary somatosensory and secondary somatosensory cortices to the insular cortex (IC) by PDL stimulation. We, then found that spontaneous IC activities that exhibited a similar spatiotemporal cortical pattern to evoked activities by PDL stimulation increased in the session after repetitive PDL stimulation. At the single-cell level, repetitive PDL stimulation augmented the synchronous neuronal activity. These results suggest that cortical plasticity induced by the repetitive stimulation leads to the frequent PDL stimulation-evoked-like spontaneous IC activation. This nociception-induced spontaneous activity in IC may be a part of mechanisms that induces chronic pain.
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Affiliation(s)
- Shutaro Kobayashi
- Department of Pharmacology, Nihon University School of Dentistry, 1-8-13 Kanda-Surugadai, Chiyoda-ku, Tokyo, 101-8310, Japan.,Department of Oral Surgery, Nihon University School of Dentistry, 1-8-13 Kanda-Surugadai, Chiyoda-ku, Tokyo, 101-8310, Japan
| | - Kazunori O'Hashi
- Department of Pharmacology, Nihon University School of Dentistry, 1-8-13 Kanda-Surugadai, Chiyoda-ku, Tokyo, 101-8310, Japan. .,Division of Oral and Craniomaxillofacial Research, Dental Research Center, Nihon University School of Dentistry, 1-8-13 Kanda-Surugadai, Chiyoda-ku, Tokyo, 101-8310, Japan. .,Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry (NCNP), 4-1-1 Ogawa-Higashi, Kodaira, Tokyo, 187-8502, Japan.
| | - Masayuki Kobayashi
- Department of Pharmacology, Nihon University School of Dentistry, 1-8-13 Kanda-Surugadai, Chiyoda-ku, Tokyo, 101-8310, Japan. .,Division of Oral and Craniomaxillofacial Research, Dental Research Center, Nihon University School of Dentistry, 1-8-13 Kanda-Surugadai, Chiyoda-ku, Tokyo, 101-8310, Japan. .,Molecular Imaging Research Center, RIKEN, 6-7-3 Minatojima-minamimachi, Chuo-ku, Kobe, 650-0047, Japan.
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Jack Rejeski W, Laurienti PJ, Bahrami M, Fanning J, Simpson SL, Burdette JH. Aging and Neural Vulnerabilities in Overeating: A Conceptual Overview and Model to Guide Treatment. PCN REPORTS : PSYCHIATRY AND CLINICAL NEUROSCIENCES 2022; 1:e39. [PMID: 36589860 PMCID: PMC9797202 DOI: 10.1002/pcn5.39] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 07/08/2022] [Accepted: 07/25/2022] [Indexed: 01/05/2023]
Abstract
Given the vulnerability of older adults to chronic disease and physical disability, coupled with the threat that obesity poses to healthy aging, there is an urgent need to understand the causes of positive energy balance and the struggle that many older adults face with intentional weight loss. This paper focuses on neural vulnerabilities related to overeating in older adults, and moderating variables that can have either favorable or unfavorable effect these vulnerabilities. Research from our laboratory on older adults with obesity suggests that they are prone to similar neural vulnerabilities for overeating that have been observed in younger and middle-aged populations. In addition, following brief postabsorptive states, functional brain networks both in the resting state and in response to active imagery of desired food are associated with 6-month weight loss. Data reviewed suggest that the sensorimotor network is a central hub in the process of valuation and underscores the central role played by habits in overeating. Finally, we demonstrate how research on the neural vulnerabilities for overeating offers a useful framework for guiding clinical decision-making in weight management.
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Affiliation(s)
- W. Jack Rejeski
- Department of Health and Exercise ScienceWake Forest UniversityWinston‐SalemNorth CarolinaUSA
- Department of Internal Medicine, Section on Geriatric MedicineWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
- Department of Radiology, Laboratory for Complex Brain NetworksWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Paul J. Laurienti
- Department of Radiology, Laboratory for Complex Brain NetworksWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
- Department of RadiologyWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Mohsen Bahrami
- Department of Radiology, Laboratory for Complex Brain NetworksWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
- Department of RadiologyWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Jason Fanning
- Department of Health and Exercise ScienceWake Forest UniversityWinston‐SalemNorth CarolinaUSA
| | - Sean L. Simpson
- Department of Radiology, Laboratory for Complex Brain NetworksWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
- Department of Biostatistics and Data ScienceWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Jonathan H. Burdette
- Department of Radiology, Laboratory for Complex Brain NetworksWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
- Department of RadiologyWake Forest University School of MedicineWinston‐SalemNorth CarolinaUSA
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34
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White BR, Ko TS, Morgan RW, Baker WB, Benson EJ, Lafontant A, Starr JP, Landis WP, Andersen K, Jahnavi J, Breimann J, Delso N, Morton S, Roberts AL, Lin Y, Graham K, Berg RA, Yodh AG, Licht DJ, Kilbaugh TJ. Low frequency power in cerebral blood flow is a biomarker of neurologic injury in the acute period after cardiac arrest. Resuscitation 2022; 178:12-18. [PMID: 35817269 PMCID: PMC9580006 DOI: 10.1016/j.resuscitation.2022.07.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 06/29/2022] [Accepted: 07/04/2022] [Indexed: 11/22/2022]
Abstract
AIM Cardiac arrest often results in severe neurologic injury. Improving care for these patients is difficult as few noninvasive biomarkers exist that allow physicians to monitor neurologic health. The amount of low-frequency power (LFP, 0.01-0.1 Hz) in cerebral haemodynamics has been used in functional magnetic resonance imaging as a marker of neuronal activity. Our hypothesis was that increased LFP in cerebral blood flow (CBF) would be correlated with improvements in invasive measures of neurologic health. METHODS We adapted the use of LFP for to monitoring of CBF with diffuse correlation spectroscopy. We asked whether LFP (or other optical biomarkers) correlated with invasive microdialysis biomarkers (lactate-pyruvate ratio - LPR - and glycerol concentration) of neuronal injury in the 4 h after return of spontaneous circulation in a swine model of paediatric cardiac arrest (Sus scrofa domestica, 8-11 kg, 51% female). Associations were tested using a mixed linear effects model. RESULTS We found that higher LFP was associated with higher LPR and higher glycerol concentration. No other biomarkers were associated with LPR; cerebral haemoglobin concentration, oxygen extraction fraction, and one EEG metric were associated with glycerol concentration. CONCLUSION Contrary to expectations, higher LFP in CBF was correlated with worse invasive biomarkers. Higher LFP may represent higher neurologic activity, or disruptions in neurovascular coupling. Either effect may be harmful in the acute period after cardiac arrest. Thus, these results suggest our methodology holds promise for development of new, clinically relevant biomarkers than can guide resuscitation and post-resuscitation care. Institutional protocol number: 19-001327.
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Affiliation(s)
- Brian R White
- Division of Pediatric Cardiology, Department of Pediatrics, The Children's Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, United States.
| | - Tiffany S Ko
- Division of Neurology, Department of Pediatrics, The Children's Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, United States
| | - Ryan W Morgan
- Department of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, United States
| | - Wesley B Baker
- Division of Neurology, Department of Pediatrics, The Children's Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, United States
| | - Emilie J Benson
- Department of Physics and Astronomy, University of Pennsylvania, United States
| | - Alec Lafontant
- Division of Neurology, Department of Pediatrics, The Children's Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, United States
| | - Jonathan P Starr
- Department of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, United States
| | - William P Landis
- Department of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, United States
| | - Kristen Andersen
- Division of Neurology, Department of Pediatrics, The Children's Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, United States
| | - Jharna Jahnavi
- Division of Neurology, Department of Pediatrics, The Children's Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, United States
| | - Jake Breimann
- Division of Neurology, Department of Pediatrics, The Children's Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, United States
| | - Nile Delso
- Department of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, United States
| | - Sarah Morton
- Department of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, United States
| | - Anna L Roberts
- Department of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, United States
| | - Yuxi Lin
- Department of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, United States
| | - Kathryn Graham
- Department of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, United States
| | - Robert A Berg
- Department of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, United States
| | - Arjun G Yodh
- Department of Physics and Astronomy, University of Pennsylvania, United States
| | - Daniel J Licht
- Division of Neurology, Department of Pediatrics, The Children's Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, United States
| | - Todd J Kilbaugh
- Department of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, United States
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35
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Li G, Yap PT. From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis. Front Hum Neurosci 2022; 16:940842. [PMID: 36061504 PMCID: PMC9428697 DOI: 10.3389/fnhum.2022.940842] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 07/28/2022] [Indexed: 01/28/2023] Open
Abstract
As a newly emerging field, connectomics has greatly advanced our understanding of the wiring diagram and organizational features of the human brain. Generative modeling-based connectome analysis, in particular, plays a vital role in deciphering the neural mechanisms of cognitive functions in health and dysfunction in diseases. Here we review the foundation and development of major generative modeling approaches for functional magnetic resonance imaging (fMRI) and survey their applications to cognitive or clinical neuroscience problems. We argue that conventional structural and functional connectivity (FC) analysis alone is not sufficient to reveal the complex circuit interactions underlying observed neuroimaging data and should be supplemented with generative modeling-based effective connectivity and simulation, a fruitful practice that we term "mechanistic connectome." The transformation from descriptive connectome to mechanistic connectome will open up promising avenues to gain mechanistic insights into the delicate operating principles of the human brain and their potential impairments in diseases, which facilitates the development of effective personalized treatments to curb neurological and psychiatric disorders.
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Affiliation(s)
- Guoshi Li
- Department of Radiology, University of North Carolina, Chapel Hill, NC, United States,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, United States,*Correspondence: Guoshi Li,
| | - Pew-Thian Yap
- Department of Radiology, University of North Carolina, Chapel Hill, NC, United States,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, United States
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36
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Kropotov JD. The enigma of infra-slow fluctuations in the human EEG. Front Hum Neurosci 2022; 16:928410. [PMID: 35982689 PMCID: PMC9378968 DOI: 10.3389/fnhum.2022.928410] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 07/12/2022] [Indexed: 11/13/2022] Open
Abstract
Spontaneous Infra-Slow Fluctuations (ISFs) of the human EEG (EEG-ISFs) were discovered 60 years ago when appropriate amplifiers for their recordings were designed. To avoid skin-related artifacts the recording of EEG-ISFs required puncturing the skin under the electrode. In the beginning of the 21st century the interest in EEG-ISFs was renewed with the appearance of commercially available DC-coupled amplified and by observation of ISFs of the blood oxygen level–dependent (BOLD) functional magnetic resonance imaging signal at a similar frequency. The independent components of irregular EEG-ISFs were shown to correlate with BOLD signals which in turn were driven by changes in arousal level measured by galvanic skin response (GSR), pupil size and HRV. There is no consensus regarding the temporal organization of EEG-ISFs: some studies emphasize the absence of peaks on EEG-ISFs spectra, some studies report prominent oscillations with frequency around 0.1 or 0.02 Hz, while some emphasize multiple discrete infraslow oscillations. No studies used parameters of EEG-ISFs as neuromarkers to discriminate psychiatric patients from healthy controls. Finally, a set of working hypotheses is suggested that must be tested in future research to solve the enigma of EEG-ISFs.
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37
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Gordon EM, Laumann TO, Marek S, Newbold DJ, Hampton JM, Seider NA, Montez DF, Nielsen AM, Van AN, Zheng A, Miller R, Siegel JS, Kay BP, Snyder AZ, Greene DJ, Schlaggar BL, Petersen SE, Nelson SM, Dosenbach NUF. Individualized Functional Subnetworks Connect Human Striatum and Frontal Cortex. Cereb Cortex 2022; 32:2868-2884. [PMID: 34718460 PMCID: PMC9247416 DOI: 10.1093/cercor/bhab387] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 11/14/2022] Open
Abstract
The striatum and cerebral cortex are interconnected via multiple recurrent loops that play a major role in many neuropsychiatric conditions. Primate corticostriatal connections can be precisely mapped using invasive tract-tracing. However, noninvasive human research has not mapped these connections with anatomical precision, limited in part by the practice of averaging neuroimaging data across individuals. Here we utilized highly sampled resting-state functional connectivity MRI for individual-specific precision functional mapping (PFM) of corticostriatal connections. We identified ten individual-specific subnetworks linking cortex-predominately frontal cortex-to striatum, most of which converged with nonhuman primate tract-tracing work. These included separable connections between nucleus accumbens core/shell and orbitofrontal/medial frontal gyrus; between anterior striatum and dorsomedial prefrontal cortex; between dorsal caudate and lateral prefrontal cortex; and between middle/posterior putamen and supplementary motor/primary motor cortex. Two subnetworks that did not converge with nonhuman primates were connected to cortical regions associated with human language function. Thus, precision subnetworks identify detailed, individual-specific, neurobiologically plausible corticostriatal connectivity that includes human-specific language networks.
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Affiliation(s)
- Evan M Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Scott Marek
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Dillan J Newbold
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Jacqueline M Hampton
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Nicole A Seider
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - David F Montez
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Ashley M Nielsen
- Institute for Innovations in Developmental Sciences, Northwestern University, Chicago, IL 60611, USA
| | - Andrew N Van
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Annie Zheng
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Ryland Miller
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Joshua S Siegel
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Benjamin P Kay
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Abraham Z Snyder
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Deanna J Greene
- Department of Cognitive Science, University of California San Diego, La Jolla, CA 92093, USA
| | - Bradley L Schlaggar
- Kennedy Krieger Institute, Baltimore, MD 21205, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Steven E Petersen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Psychological & Brain Sciences, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Steven M Nelson
- Department of Pediatrics, University of Minnesota, Minneapolis, MN 55454, USA
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55454, USA
| | - Nico U F Dosenbach
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Kennedy Krieger Institute, Baltimore, MD 21205, USA
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
- Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63110, USA
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38
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Csumitta KD, Gotts SJ, Clasen LS, Martin A, Raitano Lee N. Youth with Down syndrome display widespread increased functional connectivity during rest. Sci Rep 2022; 12:9836. [PMID: 35701489 PMCID: PMC9198034 DOI: 10.1038/s41598-022-13437-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 05/16/2022] [Indexed: 12/31/2022] Open
Abstract
Studies of resting-state functional connectivity in young people with Down syndrome (DS) have yielded conflicting results. Some studies have found increased connectivity while others have found a mix of increased and decreased connectivity. No studies have examined whole-brain connectivity at the voxel level in youth with DS during an eyes-open resting-state design. Additionally, no studies have examined the relationship between connectivity and network selectivity in youth with DS. Thus, the current study sought to fill this gap in the literature. Nineteen youth with DS (Mage = 16.5; range 7-23; 13 F) and 33 typically developing (TD) youth (Mage = 17.5; range 6-24; 18 F), matched on age and sex, completed a 5.25-min eyes-open resting-state fMRI scan. Whole-brain functional connectivity (average Pearson correlation of each voxel with every other voxel) was calculated for each individual and compared between groups. Network selectivity was then calculated and correlated with functional connectivity for the DS group. Results revealed that whole-brain functional connectivity was significantly higher in youth with DS compared to TD controls in widespread regions throughout the brain. Additionally, participants with DS had significantly reduced network selectivity compared to TD peers, and selectivity was significantly related to connectivity in all participants. Exploratory behavioral analyses revealed that regions showing increased connectivity in DS predicted Verbal IQ, suggesting differences in connectivity may be related to verbal abilities. These results indicate that network organization is disrupted in youth with DS such that disparate networks are overly connected and less selective, suggesting a potential target for clinical interventions.
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Affiliation(s)
- Kelsey D Csumitta
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, 19103, USA.
| | - Stephen J Gotts
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Liv S Clasen
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Alex Martin
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Nancy Raitano Lee
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, 19103, USA.
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Rocchi F, Canella C, Noei S, Gutierrez-Barragan D, Coletta L, Galbusera A, Stuefer A, Vassanelli S, Pasqualetti M, Iurilli G, Panzeri S, Gozzi A. Increased fMRI connectivity upon chemogenetic inhibition of the mouse prefrontal cortex. Nat Commun 2022; 13:1056. [PMID: 35217677 PMCID: PMC8881459 DOI: 10.1038/s41467-022-28591-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 01/27/2022] [Indexed: 12/25/2022] Open
Abstract
While shaped and constrained by axonal connections, fMRI-based functional connectivity reorganizes in response to varying interareal input or pathological perturbations. However, the causal contribution of regional brain activity to whole-brain fMRI network organization remains unclear. Here we combine neural manipulations, resting-state fMRI and in vivo electrophysiology to probe how inactivation of a cortical node causally affects brain-wide fMRI coupling in the mouse. We find that chronic inhibition of the medial prefrontal cortex (PFC) via overexpression of a potassium channel increases fMRI connectivity between the inhibited area and its direct thalamo-cortical targets. Acute chemogenetic inhibition of the PFC produces analogous patterns of fMRI overconnectivity. Using in vivo electrophysiology, we find that chemogenetic inhibition of the PFC enhances low frequency (0.1–4 Hz) oscillatory power via suppression of neural firing not phase-locked to slow rhythms, resulting in increased slow and δ band coherence between areas that exhibit fMRI overconnectivity. These results provide causal evidence that cortical inactivation can counterintuitively increase fMRI connectivity via enhanced, less-localized slow oscillatory processes. Pathological perturbation affects whole brain network activity. Here the authors show in mice that cortical inactivation unexpectedly results in increased fMRI connectivity between the manipulated regions and its direct axonal targets.
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Affiliation(s)
- Federico Rocchi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy.,Center for Mind and Brain Sciences, University of Trento, Rovereto, Italy
| | - Carola Canella
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy.,Center for Mind and Brain Sciences, University of Trento, Rovereto, Italy
| | - Shahryar Noei
- Center for Mind and Brain Sciences, University of Trento, Rovereto, Italy.,Neural Computational Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Daniel Gutierrez-Barragan
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Ludovico Coletta
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy.,Center for Mind and Brain Sciences, University of Trento, Rovereto, Italy
| | - Alberto Galbusera
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Alexia Stuefer
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy.,Center for Mind and Brain Sciences, University of Trento, Rovereto, Italy
| | - Stefano Vassanelli
- Dept. of Biomedical Sciences and Padua Neuroscience Center, University of Padova, Padova, Italy
| | - Massimo Pasqualetti
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy.,Biology Department, University of Pisa, Pisa, Italy
| | - Giuliano Iurilli
- Systems Neurobiology Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Stefano Panzeri
- Neural Computational Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy. .,Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive systems, Istituto Italiano di Tecnologia, Rovereto, Italy.
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Ellwood-Lowe M, Irving C, Bunge S. Exploring neural correlates of behavioral and academic resilience among children in poverty. Dev Cogn Neurosci 2022; 54:101090. [PMID: 35248821 PMCID: PMC8899231 DOI: 10.1016/j.dcn.2022.101090] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 02/15/2022] [Accepted: 02/19/2022] [Indexed: 11/26/2022] Open
Abstract
Children in poverty must contend with systems that do not meet their needs. We explored what, at a neural level, helps explain children’s resilience in these contexts. Lower coupling between lateral frontoparietal network (LFPN) and default mode network (DMN)—linked, respectively, to externally- and internally-directed thought—has previously been associated with better cognitive performance. However, we recently found the opposite pattern for children in poverty. Here, we probed ecologically-valid assessments of performance. In a pre-registered study, we investigated trajectories of network coupling over ages 9–13 and their relation to school grades and attention problems. We analyzed longitudinal data from ABCD Study (N = 8366 children at baseline; 1303 below poverty). The link between cognitive performance and grades was weaker for children in poverty, highlighting the importance of ecologically-valid measures. As predicted, higher LFPN-DMN connectivity was linked to worse grades and attentional problems for children living above poverty, while children below poverty showed opposite tendencies. This interaction between LFPN-DMN connectivity and poverty related to children’s grades two years later; however, it was attenuated when controlling for baseline grades and was not related to attention longitudinally. Together, these findings suggest network connectivity is differentially related to performance in real-world settings for children above and below poverty.
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Xing XX. Globally Aging Cortical Spontaneous Activity Revealed by Multiple Metrics and Frequency Bands Using Resting-State Functional MRI. Front Aging Neurosci 2021; 13:803436. [PMID: 35027890 PMCID: PMC8748263 DOI: 10.3389/fnagi.2021.803436] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 12/09/2021] [Indexed: 11/24/2022] Open
Abstract
Most existing aging studies using functional MRI (fMRI) are based on cross-sectional data but misinterpreted their findings (i.e., age-related differences) as longitudinal outcomes (i.e., aging-related changes). To delineate aging-related changes the of human cerebral cortex, we employed the resting-state fMRI (rsfMRI) data from 24 healthy elders in the PREVENT-AD cohort, obtaining five longitudinal scans per subject. Cortical spontaneous activity is measured globally with three rsfMRI metrics including its amplitude, homogeneity, and homotopy at three different frequency bands (slow-5: 0.02-0.03 Hz, slow-4: 0.03-0.08 Hz, and slow-3 band: 0.08-0.22 Hz). General additive mixed models revealed a universal pattern of the aging-related changes for the global cortical spontaneous activity, indicating increases of these rsfMRI metrics during aging. This aging pattern follows specific frequency and spatial profiles where higher slow bands show more non-linear curves and the amplitude exhibits more extensive and significant aging-related changes than the connectivity. These findings provide strong evidence that cortical spontaneous activity is aging globally, inspiring its clinical utility as neuroimaging markers for neruodegeneration disorders.
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Affiliation(s)
- Xiu-Xia Xing
- Department of Applied Mathematics, College of Mathematics, Faculty of Science, Beijing University of Technology, Beijing, China
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Ngo GH, Khosla M, Jamison K, Kuceyeski A, Sabuncu MR. Predicting Individual Task Contrasts From Resting-state Functional Connectivity using a Surface-based Convolutional Network. Neuroimage 2021; 248:118849. [PMID: 34965456 PMCID: PMC10155599 DOI: 10.1016/j.neuroimage.2021.118849] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 11/20/2021] [Accepted: 12/21/2021] [Indexed: 12/23/2022] Open
Abstract
Task-based and resting-state represent the two most common experimental paradigms of functional neuroimaging. While resting-state offers a flexible and scalable approach for characterizing brain function, task-based techniques provide superior localization. In this paper, we build on recent deep learning methods to create a model that predicts task-based contrast maps from resting-state fMRI scans. Specifically, we propose BrainSurfCNN, a surface-based fully-convolutional neural network model that works with a representation of the brain's cortical sheet. BrainSurfCNN achieves exceptional predictive accuracy on independent test data from the Human Connectome Project, which is on par with the repeat reliability of the measured subject-level contrast maps. Conversely, our analyses reveal that a previously published benchmark is no better than group-average contrast maps. Finally, we demonstrate that BrainSurfCNN can generalize remarkably well to novel domains with limited training data.
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Affiliation(s)
- Gia H Ngo
- School of Electrical & Computer Engineering, Cornell University and Cornell Tech, United States
| | - Meenakshi Khosla
- School of Electrical & Computer Engineering, Cornell University and Cornell Tech, United States
| | | | | | - Mert R Sabuncu
- School of Electrical & Computer Engineering, Cornell University and Cornell Tech, United States; Radiology, Weill Cornell Medicine, United States.
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43
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Resting state network connectivity is attenuated by fMRI acoustic noise. Neuroimage 2021; 247:118791. [PMID: 34920084 DOI: 10.1016/j.neuroimage.2021.118791] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 10/21/2021] [Accepted: 12/07/2021] [Indexed: 12/11/2022] Open
Abstract
INTRODUCTION During the past decades there has been an increasing interest in tracking brain network fluctuations in health and disease by means of resting state functional magnetic resonance imaging (rs-fMRI). Rs-fMRI however does not provide the ideal environmental setting, as participants are continuously exposed to noise generated by MRI coils during acquisition of Echo Planar Imaging (EPI). We investigated the effect of EPI noise on resting state activity and connectivity using magnetoencephalography (MEG), by reproducing the acoustic characteristics of rs-fMRI environment during the recordings. As compared to fMRI, MEG has little sensitivity to brain activity generated in deep brain structures, but has the advantage to capture both the dynamic of cortical magnetic oscillations with high temporal resolution and the slow magnetic fluctuations highly correlated with BOLD signal. METHODS Thirty healthy subjects were enrolled in a counterbalanced design study including three conditions: a) silent resting state (Silence), b) resting state upon EPI noise (fMRI), and c) resting state upon white noise (White). White noise was employed to test the specificity of fMRI noise effect. The amplitude envelope correlation (AEC) in alpha band measured the connectivity of seven Resting State Networks (RSN) of interest (default mode network, dorsal attention network, language, left and right auditory and left and right sensory-motor). Vigilance dynamic was estimated from power spectral activity. RESULTS fMRI and White acoustic noise consistently reduced connectivity of cortical networks. The effects were widespread, but noise and network specificities were also present. For fMRI noise, decreased connectivity was found in the right auditory and sensory-motor networks. Progressive increase of slow theta-delta activity related to drowsiness was found in all conditions, but was significantly higher for fMRI . Theta-delta significantly and positively correlated with variations of cortical connectivity. DISCUSSION rs-fMRI connectivity is biased by unavoidable environmental factors during scanning, which warrant more careful control and improved experimental designs. MEG is free from acoustic noise and allows a sensitive estimation of resting state connectivity in cortical areas. Although underutilized, MEG could overcome issues related to noise during fMRI, in particular when investigation of motor and auditory networks is needed.
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Saarimäki H, Glerean E, Smirnov D, Mynttinen H, Jääskeläinen IP, Sams M, Nummenmaa L. Classification of emotion categories based on functional connectivity patterns of the human brain. Neuroimage 2021; 247:118800. [PMID: 34896586 PMCID: PMC8803541 DOI: 10.1016/j.neuroimage.2021.118800] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 12/05/2021] [Accepted: 12/08/2021] [Indexed: 12/01/2022] Open
Abstract
Neurophysiological and psychological models posit that emotions depend on connections across wide-spread corticolimbic circuits. While previous studies using pattern recognition on neuroimaging data have shown differences between various discrete emotions in brain activity patterns, less is known about the differences in functional connectivity. Thus, we employed multivariate pattern analysis on functional magnetic resonance imaging data (i) to develop a pipeline for applying pattern recognition in functional connectivity data, and (ii) to test whether connectivity patterns differ across emotion categories. Six emotions (anger, fear, disgust, happiness, sadness, and surprise) and a neutral state were induced in 16 participants using one-minute-long emotional narratives with natural prosody while brain activity was measured with functional magnetic resonance imaging (fMRI). We computed emotion-wise connectivity matrices both for whole-brain connections and for 10 previously defined functionally connected brain subnetworks and trained an across-participant classifier to categorize the emotional states based on whole-brain data and for each subnetwork separately. The whole-brain classifier performed above chance level with all emotions except sadness, suggesting that different emotions are characterized by differences in large-scale connectivity patterns. When focusing on the connectivity within the 10 subnetworks, classification was successful within the default mode system and for all emotions. We thus show preliminary evidence for consistently different sustained functional connectivity patterns for instances of emotion categories particularly within the default mode system.
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Affiliation(s)
- Heini Saarimäki
- Faculty of Social Sciences, Tampere University, FI-33014 Tampere University, Tampere, Finland; Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland.
| | - Enrico Glerean
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland; Advanced Magnetic Imaging (AMI) Centre, Aalto NeuroImaging, School of Science, Aalto University, Espoo, Finland; Turku PET Centre and Department of Psychology, University of Turku, Turku, Finland; Department of Computer Science, School of Science, Aalto University, Espoo, Finland; International Laboratory of Social Neurobiology, Institute for Cognitive Neuroscience, HSE University, Moscow, Russian Federation
| | - Dmitry Smirnov
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland
| | - Henri Mynttinen
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland
| | - Iiro P Jääskeläinen
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland; International Laboratory of Social Neurobiology, Institute for Cognitive Neuroscience, HSE University, Moscow, Russian Federation
| | - Mikko Sams
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland; Department of Computer Science, School of Science, Aalto University, Espoo, Finland
| | - Lauri Nummenmaa
- Turku PET Centre and Department of Psychology, University of Turku, Turku, Finland
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45
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Liu M, Li B, Hu D. Autism Spectrum Disorder Studies Using fMRI Data and Machine Learning: A Review. Front Neurosci 2021; 15:697870. [PMID: 34602966 PMCID: PMC8480393 DOI: 10.3389/fnins.2021.697870] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 08/09/2021] [Indexed: 01/01/2023] Open
Abstract
Machine learning methods have been frequently applied in the field of cognitive neuroscience in the last decade. A great deal of attention has been attracted to introduce machine learning methods to study the autism spectrum disorder (ASD) in order to find out its neurophysiological underpinnings. In this paper, we presented a comprehensive review about the previous studies since 2011, which applied machine learning methods to analyze the functional magnetic resonance imaging (fMRI) data of autistic individuals and the typical controls (TCs). The all-round process was covered, including feature construction from raw fMRI data, feature selection methods, machine learning methods, factors for high classification accuracy, and critical conclusions. Applying different machine learning methods and fMRI data acquired from different sites, classification accuracies were obtained ranging from 48.3% up to 97%, and informative brain regions and networks were located. Through thorough analysis, high classification accuracies were found to usually occur in the studies which involved task-based fMRI data, single dataset for some selection principle, effective feature selection methods, or advanced machine learning methods. Advanced deep learning together with the multi-site Autism Brain Imaging Data Exchange (ABIDE) dataset became research trends especially in the recent 4 years. In the future, advanced feature selection and machine learning methods combined with multi-site dataset or easily operated task-based fMRI data may appear to have the potentiality to serve as a promising diagnostic tool for ASD.
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Affiliation(s)
- Meijie Liu
- Engineering Training Center, Xi'an University of Science and Technology, Xi'an, China.,College of Missile Engineering, Rocket Force University of Engineering, Xi'an, China
| | - Baojuan Li
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Dewen Hu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
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46
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Nichols ES, Gao Y, Fregni S, Liu L, Joanisse MF. Individual differences in representational similarity of first and second languages in the bilingual brain. Hum Brain Mapp 2021; 42:5433-5445. [PMID: 34469016 PMCID: PMC8519873 DOI: 10.1002/hbm.25633] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 07/22/2021] [Accepted: 08/06/2021] [Indexed: 01/15/2023] Open
Abstract
Current theories of bilingualism disagree on the extent to which separate brain regions are used to maintain or process one's first and second language. The present study took a novel multivariate approach to address this question. We examined whether bilinguals maintain distinct neural representations of two languages; specifically, we tested whether brain areas that are involved in processing word meaning in either language are reliably representing each language differently, and whether language representation is influenced by individual differences in proficiency level and age of acquisition (AoA) of L2. Thirty‐one English–Mandarin bilingual adults performed a picture–word matching task in both languages. We then used representational similarity analysis to examine which brain regions reliably showed different patterns of activity for each language. We found that both proficiency and AoA predicted dissimilarity between language representations in several brain areas within the language network as well as several regions of the ventral visual pathway, demonstrating that top‐down language knowledge and individual language experience shapes concept representation in this processing stream. The results support the model of an integrated language system in bilinguals, along with a novel description of how representations for each language change with proficiency level and L2 AoA.
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Affiliation(s)
- Emily S Nichols
- Department of Applied Psychology, Faculty of Education, The University of Western Ontario, London, Canada.,Brain and Mind Institute, The University of Western Ontario, London, Canada
| | - Yue Gao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Sofia Fregni
- Faculty of Psychology, Dresden University of Technology, Dresden, Germany
| | - Li Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Marc F Joanisse
- Brain and Mind Institute, The University of Western Ontario, London, Canada.,Department of Psychology, The University of Western Ontario, London, Canada
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Farahibozorg SR, Bijsterbosch JD, Gong W, Jbabdi S, Smith SM, Harrison SJ, Woolrich MW. Hierarchical modelling of functional brain networks in population and individuals from big fMRI data. Neuroimage 2021; 243:118513. [PMID: 34450262 PMCID: PMC8526871 DOI: 10.1016/j.neuroimage.2021.118513] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 06/30/2021] [Accepted: 08/23/2021] [Indexed: 11/17/2022] Open
Abstract
We introduce stochastic PROFUMO (sPROFUMO) for inferring functional brain networks from big data. sPROFUMO hierarchically estimates fMRI networks for the population and every individual. We characterised high dimensional resting state fMRI networks from UK Biobank. Model outperforms ICA and dual regression for estimation of individual-specific network topography. We demonstrate the model's utility for predicting cognitive traits, and capturing subject variability in network topographies versus connectivity.
A major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose for the first time a technique, Stochastic Probabilistic Functional Modes (sPROFUMO), that is scalable to UK Biobank (UKB) with expected 100,000 participants, and hierarchically estimates functional brain networks in individuals and the population, while allowing for bidirectional flow of information between the two. Using simulations, we show the model's utility, especially in scenarios that involve significant cross-subject variability, or require delineation of fine-grained differences between the networks. Subsequently, by applying the model to resting-state fMRI from 4999 UKB subjects, we mapped resting state networks (RSNs) in single subjects with greater detail than has been possible previously in UKB (>100 RSNs), and demonstrate that these RSNs can predict a range of sensorimotor and higher-level cognitive functions. Furthermore, we demonstrate several advantages of the model over independent component analysis combined with dual-regression (ICA-DR), particularly with respect to the estimation of the spatial configuration of the RSNs and the predictive power for cognitive traits. The proposed model and results can open a new door for future investigations into individualised profiles of brain function from big data.
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Affiliation(s)
- Seyedeh-Rezvan Farahibozorg
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United Kingdom.
| | - Janine D Bijsterbosch
- Department of Radiology, Washington University School of Medicine, St. Louis, United States
| | - Weikang Gong
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United Kingdom
| | - Saad Jbabdi
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United Kingdom
| | - Stephen M Smith
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United Kingdom
| | - Samuel J Harrison
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United Kingdom; Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland; New Zealand Brain Research Institute, University of Otago, Christchurch, New Zealand
| | - Mark W Woolrich
- FMRIB, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, United Kingdom; OHBA, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, Oxford University, Oxford, United Kingdom
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Abstract
Action videogames have been shown to induce modifications in perceptual and cognitive systems, as well as in brain structure and function. Nevertheless, whether such changes are correlated with brain functional connectivity modifications outlasting the training period is not known. Functional magnetic resonance imaging (fMRI) was used in order to quantify acute and long-lasting connectivity changes following a sustained gaming experience on a first-person shooter (FPS) game. Thirty-five healthy participants were assigned to either a gaming or a control group prior to the acquisition of resting state fMRI data and a comprehensive cognitive assessment at baseline (T0), post-gaming (T1) and at a 3 months' follow-up (T2). Seed-based resting-state functional connectivity (rs-FC) analysis revealed a significant greater connectivity between left thalamus and left parahippocampal gyrus in the gamer group, both at T1 and at T2. Furthermore, a positive increase in the rs-FC between the cerebellum, Heschl's gyrus and the middle frontal gyrus paralleled improvements of in-gaming performance. In addition, baseline rs-FC of left supramarginal gyrus, left middle frontal gyrus and right cerebellum were associated with individual changes in videogame performance. Finally, enhancement of perceptual and attentional measures was observed at both T1 and T2, which correlated with a pattern of rs-FC changes in bilateral occipito-temporal regions belonging to the visual and attention fMRI networks. The present findings increase knowledge on functional connectivity changes induced by action videogames, pointing to a greater and long-lasting synchronization between brain regions associated with spatial orientation, visual discrimination and motor learning even after a relatively short multi-day gaming exposure.
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Raut RV, Snyder AZ, Mitra A, Yellin D, Fujii N, Malach R, Raichle ME. Global waves synchronize the brain's functional systems with fluctuating arousal. SCIENCE ADVANCES 2021; 7:7/30/eabf2709. [PMID: 34290088 PMCID: PMC8294763 DOI: 10.1126/sciadv.abf2709] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 06/04/2021] [Indexed: 05/04/2023]
Abstract
We propose and empirically support a parsimonious account of intrinsic, brain-wide spatiotemporal organization arising from traveling waves linked to arousal. We hypothesize that these waves are the predominant physiological process reflected in spontaneous functional magnetic resonance imaging (fMRI) signal fluctuations. The correlation structure ("functional connectivity") of these fluctuations recapitulates the large-scale functional organization of the brain. However, a unifying physiological account of this structure has so far been lacking. Here, using fMRI in humans, we show that ongoing arousal fluctuations are associated with global waves of activity that slowly propagate in parallel throughout the neocortex, thalamus, striatum, and cerebellum. We show that these waves can parsimoniously account for many features of spontaneous fMRI signal fluctuations, including topographically organized functional connectivity. Last, we demonstrate similar, cortex-wide propagation of neural activity measured with electrocorticography in macaques. These findings suggest that traveling waves spatiotemporally pattern brain-wide excitability in relation to arousal.
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Affiliation(s)
- Ryan V Raut
- Department of Radiology, Washington University, St. Louis, MO 63110, USA.
| | - Abraham Z Snyder
- Department of Radiology, Washington University, St. Louis, MO 63110, USA
- Department of Neurology, Washington University, St. Louis, MO 63110, USA
| | - Anish Mitra
- Department of Psychiatry, Stanford University, Stanford, CA 94305, USA
| | - Dov Yellin
- Department of Neurobiology, Weizmann Institute of Science, 76100 Rehovot, Israel
| | - Naotaka Fujii
- Laboratory for Adaptive Intelligence, RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan
| | - Rafael Malach
- Department of Neurobiology, Weizmann Institute of Science, 76100 Rehovot, Israel
| | - Marcus E Raichle
- Department of Radiology, Washington University, St. Louis, MO 63110, USA
- Department of Neurology, Washington University, St. Louis, MO 63110, USA
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Guerreiro MJS, Linke M, Lingareddy S, Kekunnaya R, Röder B. The effect of congenital blindness on resting-state functional connectivity revisited. Sci Rep 2021; 11:12433. [PMID: 34127748 PMCID: PMC8203782 DOI: 10.1038/s41598-021-91976-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 06/03/2021] [Indexed: 02/05/2023] Open
Abstract
Lower resting-state functional connectivity (RSFC) between 'visual' and non-'visual' neural circuits has been reported as a hallmark of congenital blindness. In sighted individuals, RSFC between visual and non-visual brain regions has been shown to increase during rest with eyes closed relative to rest with eyes open. To determine the role of visual experience on the modulation of RSFC by resting state condition-as well as to evaluate the effect of resting state condition on group differences in RSFC-, we compared RSFC between visual and somatosensory/auditory regions in congenitally blind individuals (n = 9) and sighted participants (n = 9) during eyes open and eyes closed conditions. In the sighted group, we replicated the increase of RSFC between visual and non-visual areas during rest with eyes closed relative to rest with eyes open. This was not the case in the congenitally blind group, resulting in a lower RSFC between 'visual' and non-'visual' circuits relative to sighted controls only in the eyes closed condition. These results indicate that visual experience is necessary for the modulation of RSFC by resting state condition and highlight the importance of considering whether sighted controls should be tested with eyes open or closed in studies of functional brain reorganization as a consequence of blindness.
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Affiliation(s)
- Maria J S Guerreiro
- Biological Psychology and Neuropsychology, Institute for Psychology, University of Hamburg, Von-Melle-Park 11, 20146, Hamburg, Germany.
- Biological Psychology, Department of Psychology, Carl Von Ossietzky University of Oldenburg, 26111, Oldenburg, Germany.
| | - Madita Linke
- Biological Psychology and Neuropsychology, Institute for Psychology, University of Hamburg, Von-Melle-Park 11, 20146, Hamburg, Germany
| | - Sunitha Lingareddy
- Department of Radiology, Lucid Medical Diagnostics, Banjara Hills, Hyderabad, Telengana, 500082, India
| | - Ramesh Kekunnaya
- Child Sight Institute, Jasti V. Ramanamma Children's Eye Care Center, Department of Pediatric Ophthalmology, Strabismus, and Neuro-Ophthalmology, L. V. Prasad Eye Institute, Kallam Anji Reddy Campus, Hyderabad, Telengana, 500034, India
| | - Brigitte Röder
- Biological Psychology and Neuropsychology, Institute for Psychology, University of Hamburg, Von-Melle-Park 11, 20146, Hamburg, Germany
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