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Tanner J, Faskowitz J, Kennedy DP, Betzel RF. Dynamic adaptation to novelty in the brain is related to arousal and intelligence. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.02.606380. [PMID: 39149315 PMCID: PMC11326181 DOI: 10.1101/2024.08.02.606380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
How does the human brain respond to novelty? Here, we address this question using fMRI data wherein human participants watch the same movie scene four times. On the first viewing, this movie scene is novel, and on later viewings it is not. We find that brain activity is lower-dimensional in response to novelty. At a finer scale, we find that this reduction in the dimensionality of brain activity is the result of increased coupling in specific brain systems, most specifically within and between the control and dorsal attention systems. Additionally, we found that novelty induced an increase in between-subject synchronization of brain activity in the same brain systems. We also find evidence that adaptation to novelty, herein operationalized as the difference between baseline coupling and novelty-response coupling, is related to fluid intelligence. Finally, using separately collected out-of-sample data, we find that the above results may be linked to psychological arousal.
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Affiliation(s)
- Jacob Tanner
- Luddy School of Informatics, Computing, and Engineering
- Cognitive Science Program
| | | | - Daniel P. Kennedy
- Cognitive Science Program
- Department of Psychological and Brain Sciences
- Program in Neuroscience, Indiana University, Bloomington, IN 47405
| | - Richard F. Betzel
- Luddy School of Informatics, Computing, and Engineering
- Cognitive Science Program
- Department of Psychological and Brain Sciences
- Program in Neuroscience, Indiana University, Bloomington, IN 47405
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2
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Combrisson E, Basanisi R, Gueguen MCM, Rheims S, Kahane P, Bastin J, Brovelli A. Neural interactions in the human frontal cortex dissociate reward and punishment learning. eLife 2024; 12:RP92938. [PMID: 38941238 PMCID: PMC11213568 DOI: 10.7554/elife.92938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2024] Open
Abstract
How human prefrontal and insular regions interact while maximizing rewards and minimizing punishments is unknown. Capitalizing on human intracranial recordings, we demonstrate that the functional specificity toward reward or punishment learning is better disentangled by interactions compared to local representations. Prefrontal and insular cortices display non-selective neural populations to rewards and punishments. Non-selective responses, however, give rise to context-specific interareal interactions. We identify a reward subsystem with redundant interactions between the orbitofrontal and ventromedial prefrontal cortices, with a driving role of the latter. In addition, we find a punishment subsystem with redundant interactions between the insular and dorsolateral cortices, with a driving role of the insula. Finally, switching between reward and punishment learning is mediated by synergistic interactions between the two subsystems. These results provide a unifying explanation of distributed cortical representations and interactions supporting reward and punishment learning.
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Affiliation(s)
- Etienne Combrisson
- Institut de Neurosciences de la Timone, Aix Marseille UniversitéMarseilleFrance
| | - Ruggero Basanisi
- Institut de Neurosciences de la Timone, Aix Marseille UniversitéMarseilleFrance
| | - Maelle CM Gueguen
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut NeurosciencesGrenobleFrance
| | - Sylvain Rheims
- Department of Functional Neurology and Epileptology, Hospices Civils de Lyon and University of LyonLyonFrance
| | - Philippe Kahane
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut NeurosciencesGrenobleFrance
| | - Julien Bastin
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut NeurosciencesGrenobleFrance
| | - Andrea Brovelli
- Institut de Neurosciences de la Timone, Aix Marseille UniversitéMarseilleFrance
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3
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Dorst KE, Ramirez S. Engrams: From Behavior to Brain-Wide Networks. ADVANCES IN NEUROBIOLOGY 2024; 38:13-28. [PMID: 39008008 DOI: 10.1007/978-3-031-62983-9_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Animals utilize a repertoire of behavioral responses during everyday experiences. During a potentially dangerous encounter, defensive actions such as "fight, flight, or freeze" are selected for survival. The successful use of behavior is determined by a series of real-time computations combining an animal's internal (i.e., body) and external (i.e., environment) state. Brain-wide neural pathways are engaged throughout this process to detect stimuli, integrate information, and command behavioral output. The hippocampus, in particular, plays a role in the encoding and storing of the episodic information surrounding these encounters as putative "engram" or experience-modified cellular ensembles. Recalling a negative experience then reactivates a dedicated engram ensemble and elicits a behavioral response. How hippocampus-based engrams modulate brain-wide states and an animal's internal/external milieu to influence behavior is an exciting area of investigation for contemporary neuroscience. In this chapter, we provide an overview of recent technological advancements that allow researchers to tag, manipulate, and visualize putative engram ensembles, with an overarching goal of casually connecting their brain-wide underpinnings to behavior. We then discuss how hippocampal fear engrams alter behavior in a manner that is contingent on an environment's physical features as well as how they influence brain-wide patterns of cellular activity. Overall, we propose here that studies on memory engrams offer an exciting avenue for contemporary neuroscience to casually link the activity of cells to cognition and behavior while also offering testable theoretical and experimental frameworks for how the brain organizes experience.
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Affiliation(s)
- Kaitlyn E Dorst
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
- Department of Neuroscience, Tufts University School of Medicine, Boston, MA, USA
- Graduate Program for Neuroscience, Boston University, Boston, MA, USA
| | - Steve Ramirez
- Department of Psychological & Brain Sciences, Boston University, Boston, MA, USA.
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Uehara K, Yasuhara M, Koguchi J, Oku T, Shiotani S, Morise M, Furuya S. Brain network flexibility as a predictor of skilled musical performance. Cereb Cortex 2023; 33:10492-10503. [PMID: 37566918 DOI: 10.1093/cercor/bhad298] [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: 04/29/2023] [Revised: 07/25/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
Interactions between the body and the environment are dynamically modulated by upcoming sensory information and motor execution. To adapt to this behavioral state-shift, brain activity must also be flexible and possess a large repertoire of brain networks so as to switch them flexibly. Recently, flexible internal brain communications, i.e. brain network flexibility, have come to be recognized as playing a vital role in integrating various sensorimotor information. Therefore, brain network flexibility is one of the key factors that define sensorimotor skill. However, little is known about how flexible communications within the brain characterize the interindividual variation of sensorimotor skill and trial-by-trial variability within individuals. To address this, we recruited skilled musical performers and used a novel approach that combined multichannel-scalp electroencephalography, behavioral measurements of musical performance, and mathematical approaches to extract brain network flexibility. We found that brain network flexibility immediately before initiating the musical performance predicted interindividual differences in the precision of tone timbre when required for feedback control, but not for feedforward control. Furthermore, brain network flexibility in broad cortical regions predicted skilled musical performance. Our results provide novel evidence that brain network flexibility plays an important role in building skilled sensorimotor performance.
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Affiliation(s)
- Kazumasa Uehara
- Neural Information Dynamics Laboratory, Department of Computer Science and Engineering, Toyohashi University of Technology, Toyohashi, Japan
- Sony Computer Science Laboratories Inc, Tokyo 1410022, Japan
| | - Masaki Yasuhara
- Sony Computer Science Laboratories Inc, Tokyo 1410022, Japan
- Neural Engineering Laboratory, Department of Science of Technology Innovation, Nagaoka University of Technology, Nagaoka, Japan
| | - Junya Koguchi
- Sony Computer Science Laboratories Inc, Tokyo 1410022, Japan
- Graduate School of Advanced Mathematical Sciences, Meiji University, Tokyo, Japan
| | | | | | - Masanori Morise
- Sony Computer Science Laboratories Inc, Tokyo 1410022, Japan
- School of Interdisciplinary Mathematical Sciences, Meiji University, Tokyo, Japan
| | - Shinichi Furuya
- Sony Computer Science Laboratories Inc, Tokyo 1410022, Japan
- NeuroPiano Institute, Kyoto 6008086, Japan
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Wang M, Zheng H, Zhou W, Yang B, Wang L, Chen S, Dong GH. Disrupted dynamic network reconfiguration of the executive and reward networks in internet gaming disorder. Psychol Med 2023; 53:5478-5487. [PMID: 36004801 DOI: 10.1017/s0033291722002665] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND Studies have shown that people with internet gaming disorder (IGD) exhibit impaired executive control of gaming cravings; however, the neural mechanisms underlying this process remain unknown. In addition, these conclusions were based on the hypothesis that brain networks are temporally static, neglecting dynamic changes in cognitive processes. METHODS Resting-state fMRI data were collected from 402 subjects [162 subjects with IGD and 240 recreational game users (RGUs)]. The community structure (recruitment and integration) of the executive control network (ECN) and the basal ganglia network (BGN), which represents the reward network, of patients with IGD and RGUs were compared. Mediation effects among the different networks were analyzed. RESULTS Compared to RGUs, subjects with IGD had a lower recruitment coefficient within the right ECN. Further analysis showed that only male subjects had a lower recruitment coefficient. Mediation analysis showed that the integration coefficient of the right ECN mediated the relationship between the recruitment coefficients of both the right ECN and the BGN in RGUs. CONCLUSIONS Male subjects with IGD had a lower recruitment coefficient than RGUs, which impairing their impulse control. The mediation results suggest that top-down executive control of the ECN is absent in subjects with IGD. Together, these findings could explain why subjects with IGD exhibit impaired executive control of gaming cravings; these results have important therapeutic implications for developing effective interventions for IGD.
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Affiliation(s)
- Min Wang
- Center for Cognition and Brain Disorders, School of Clinical Medicine, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
- The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - Hui Zheng
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, PR China
| | - Weiran Zhou
- Center for Cognition and Brain Disorders, School of Clinical Medicine, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - Bo Yang
- Center for Cognition and Brain Disorders, School of Clinical Medicine, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - Lingxiao Wang
- Center for Cognition and Brain Disorders, School of Clinical Medicine, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
- The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - Shuaiyu Chen
- Center for Cognition and Brain Disorders, School of Clinical Medicine, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
- The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - Guang-Heng Dong
- Center for Cognition and Brain Disorders, School of Clinical Medicine, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
- The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China
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Wang Y, Zhang N, Qian S, Liu J, Yu S, Li N, Xia C. Classify patients with Moyamoya disease according to their cognitive performance might be helpful in clinical and practical with support vector machine based on hypergraph. Hum Brain Mapp 2023; 44:2407-2417. [PMID: 36799621 PMCID: PMC10028655 DOI: 10.1002/hbm.26218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 01/10/2023] [Accepted: 01/12/2023] [Indexed: 02/18/2023] Open
Abstract
Moyamoya disease (MMD) patients were now classified according to their cerebrovascular manifestations, with cognition and emotion ignored, which attenuated the therapy. The present study tried to classify them based on their cognitive and emotional performance and explored the neural basis underlying this classification using resting-state fMRI (rs-fMRI). Thirty-nine MMD patients were recruited, assessed mental function and MRI scanned. We adopted hierarchical analysis of their mental performance for new subtypes. Next, a three-step analysis, with each step consisting of 10 random cross validation, was conducted for robust brain regions in classifying the three subtypes of patients in a support vector machine (SVM) model with hypergraph of rs-fMRI. We found three new subtypes including high depression-high anxiety-low cognition (HE-LC, 50%), low depression-low anxiety-high cognition (LE-HC, 14%), and low depression-low anxiety-low cognition (LE-LC, 36%), and no hemorrhagic MMD patients fell into the LE-HC group. The temporal and the bilateral superior frontal cortex, and so forth were included in all 10 randomized SVM modeling. The classification accuracy of the final three-way classification model was 67.5% in average of 10 random cross validation. In addition, the S value between the frontal cortex and the angular cortex was positively correlated with the anxiety score and backward digit span (p < .05). Our results might provide a new perspective for MMD classification concerning patients' mental status, guide timely surgery and suggest angular cortex, and so forth should be protected in surgery for cognitive consideration.
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Affiliation(s)
- Ying Wang
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, People's Republic of China
- Anhui Provincial Stereotactic Neurosurgical Institute, Hefei, Anhui, People's Republic of China
- Anhui Key Laboratory of Brain Function and Brain Disease, Hefei, Anhui, People's Republic of China
| | - Nan Zhang
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, People's Republic of China
| | - Sheng Qian
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, People's Republic of China
| | - Jian Liu
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, People's Republic of China
| | - Shaojie Yu
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, People's Republic of China
| | - Nan Li
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, Anhui, People's Republic of China
| | - Chengyu Xia
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, People's Republic of China
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7
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Uehara K, Togo H, Hanakawa T. Precise motor rhythmicity relies on motor network responsivity. Cereb Cortex 2022; 33:4432-4447. [PMID: 36218995 DOI: 10.1093/cercor/bhac353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/14/2022] [Accepted: 08/15/2022] [Indexed: 11/14/2022] Open
Abstract
Rhythmic movements are the building blocks of human behavior. However, given that rhythmic movements are achieved through complex interactions between neural modules, it remains difficult to clarify how the central nervous system controls motor rhythmicity. Here, using a novel tempo-precision trade-off paradigm, we first modeled interindividual behavioral differences in tempo-dependent rhythmicity for various external tempi. We identified 2 behavioral extremes: conventional and paradoxical tempo-precision trade-off types. We then explored the neural substrates of these behavioral differences using task and resting-state functional magnetic resonance imaging. We found that the responsibility of interhemispheric motor network connectivity to tempi was a key to the behavioral repertoire. In the paradoxical trade-off type, interhemispheric connectivity was low at baseline but increased in response to increasing tempo; in the conventional trade-off type, strong baseline connectivity was coupled with low responsivity. These findings suggest that tunable interhemispheric connectivity underlies tempo-dependent rhythmicity control.
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Affiliation(s)
- Kazumasa Uehara
- Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo 1878551, Japan.,Division of Neural Dynamics, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Aichi 4448585, Japan.,Department of Physiological Sciences, School of Life Sciences, SOKENDAI (The Graduate University for Advanced Studies), Okazaki, Aichi 4448585, Japan
| | - Hiroki Togo
- Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo 1878551, Japan.,Department of Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto 6068501, Japan
| | - Takashi Hanakawa
- Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo 1878551, Japan.,Department of Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto 6068501, Japan
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8
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Blevins AS, Bassett DS, Scott EK, Vanwalleghem GC. From calcium imaging to graph topology. Netw Neurosci 2022; 6:1125-1147. [PMID: 38800465 PMCID: PMC11117109 DOI: 10.1162/netn_a_00262] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 06/13/2022] [Indexed: 05/29/2024] Open
Abstract
Systems neuroscience is facing an ever-growing mountain of data. Recent advances in protein engineering and microscopy have together led to a paradigm shift in neuroscience; using fluorescence, we can now image the activity of every neuron through the whole brain of behaving animals. Even in larger organisms, the number of neurons that we can record simultaneously is increasing exponentially with time. This increase in the dimensionality of the data is being met with an explosion of computational and mathematical methods, each using disparate terminology, distinct approaches, and diverse mathematical concepts. Here we collect, organize, and explain multiple data analysis techniques that have been, or could be, applied to whole-brain imaging, using larval zebrafish as an example model. We begin with methods such as linear regression that are designed to detect relations between two variables. Next, we progress through network science and applied topological methods, which focus on the patterns of relations among many variables. Finally, we highlight the potential of generative models that could provide testable hypotheses on wiring rules and network progression through time, or disease progression. While we use examples of imaging from larval zebrafish, these approaches are suitable for any population-scale neural network modeling, and indeed, to applications beyond systems neuroscience. Computational approaches from network science and applied topology are not limited to larval zebrafish, or even to systems neuroscience, and we therefore conclude with a discussion of how such methods can be applied to diverse problems across the biological sciences.
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Affiliation(s)
- Ann S. Blevins
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S. Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - Ethan K. Scott
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
- Department of Anatomy and Physiology, School of Biomedical Sciences, University of Melbourne, Parkville, Australia
| | - Gilles C. Vanwalleghem
- Danish Research Institute of Translational Neuroscience (DANDRITE), Nordic EMBL Partnership for Molecular Medicine, Aarhus University, Aarhus, Denmark
- Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
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9
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Wang M, Wang L, Yang B, Yuan L, Wang X, Potenza MN, Dong GH. Disrupted dynamic network reconfiguration of the brain functional networks of individuals with autism spectrum disorder. Brain Commun 2022; 4:fcac177. [PMID: 35950094 PMCID: PMC9356733 DOI: 10.1093/braincomms/fcac177] [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: 12/17/2021] [Revised: 04/06/2022] [Accepted: 07/31/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
Human and animal studies on brain functions in subjects with autism spectrum disorder have confirmed the aberrant organization of functional networks. However, little is known about the neural features underlying these impairments.
Using community structure analyses (recruitment and integration), the current study explored the functional network features of individuals with autism spectrum disorder from one database (101 individuals with autism spectrum disorder and 120 healthy controls) and tested the replicability in an independent database (50 individuals with autism spectrum disorder and 74 healthy controls). Additionally, the study divided subjects into different age groups and tested the features in different subgroups.
As for recruitment, subjects with autism spectrum disorder had lower coefficients in the default mode network and basal ganglia network than healthy controls. The integration results showed that subjects with autism spectrum disorder had a lower coefficient than healthy controls in the default mode network -medial frontal network and basal ganglia network -limbic networks. The results for the default mode network were mostly replicated in the independent database, but the results for the basal ganglia network were not. The results for different age groups were also analyzed, and the replicability was tested in different databases.
The lower recruitment in subjects with autism spectrum disorder suggests that they are less efficient at engaging these networks when performing relevant tasks. The lower integration results suggest impaired flexibility in cognitive functions in individuals with autism spectrum disorder. All these findings might explain why subjects with autism spectrum disorder show impaired brain networks and have important therapeutic implications for developing potentially effective interventions.
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Affiliation(s)
- Min Wang
- Center for Cognition and Brain Disorders, School of Clinical Medicine and the Affiliated Hospital of Hangzhou Normal University , Hangzhou, Zhejiang Province , PR China
| | - Lingxiao Wang
- Center for Cognition and Brain Disorders, School of Clinical Medicine and the Affiliated Hospital of Hangzhou Normal University , Hangzhou, Zhejiang Province , PR China
| | - Bo Yang
- Center for Cognition and Brain Disorders, School of Clinical Medicine and the Affiliated Hospital of Hangzhou Normal University , Hangzhou, Zhejiang Province , PR China
| | - Lixia Yuan
- Center for Cognition and Brain Disorders, School of Clinical Medicine and the Affiliated Hospital of Hangzhou Normal University , Hangzhou, Zhejiang Province , PR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments , Hangzhou, Zhejiang Province , PR China
| | - Xiuqin Wang
- Center for Cognition and Brain Disorders, School of Clinical Medicine and the Affiliated Hospital of Hangzhou Normal University , Hangzhou, Zhejiang Province , PR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments , Hangzhou, Zhejiang Province , PR China
| | - Marc N Potenza
- Department of Psychiatry and Child Study Center, Yale University School of Medicine , New Haven, CT , USA
- Connecticut Mental Health Center , New Haven, CT , USA
- Connecticut Council on Problem Gambling , Wethersfield, CT , USA
- Department of Neuroscience and Wu Tsai Institute, Yale University , New Haven, CT , USA
| | - Guang Heng Dong
- Center for Cognition and Brain Disorders, School of Clinical Medicine and the Affiliated Hospital of Hangzhou Normal University , Hangzhou, Zhejiang Province , PR China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments , Hangzhou, Zhejiang Province , PR China
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10
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Learning induces coordinated neuronal plasticity of metabolic demands and functional brain networks. Commun Biol 2022; 5:428. [PMID: 35534605 PMCID: PMC9085889 DOI: 10.1038/s42003-022-03362-4] [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: 11/26/2021] [Accepted: 04/12/2022] [Indexed: 12/21/2022] Open
Abstract
The neurobiological basis of learning is reflected in adaptations of brain structure, network organization and energy metabolism. However, it is still unknown how different neuroplastic mechanisms act together and if cognitive advancements relate to general or task-specific changes. Therefore, we tested how hierarchical network interactions contribute to improvements in the performance of a visuo-spatial processing task by employing simultaneous PET/MR neuroimaging before and after a 4-week learning period. We combined functional PET and metabolic connectivity mapping (MCM) to infer directional interactions across brain regions. Learning altered the top-down regulation of the salience network onto the occipital cortex, with increases in MCM at resting-state and decreases during task execution. Accordingly, a higher divergence between resting-state and task-specific effects was associated with better cognitive performance, indicating that these adaptations are complementary and both required for successful visuo-spatial skill learning. Simulations further showed that changes at resting-state were dependent on glucose metabolism, whereas those during task performance were driven by functional connectivity between salience and visual networks. Referring to previous work, we suggest that learning establishes a metabolically expensive skill engram at rest, whose retrieval serves for efficient task execution by minimizing prediction errors between neuronal representations of brain regions on different hierarchical levels. Brain network analyses reveal coupled changes between functional connectivity and metabolic demands that relate to cognitive performance improvements induced by learning a challenging visuo-spatial task for four weeks.
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11
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Krendl AC, Betzel RF. Social cognitive network neuroscience. Soc Cogn Affect Neurosci 2022; 17:510-529. [PMID: 35352125 PMCID: PMC9071476 DOI: 10.1093/scan/nsac020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 01/27/2022] [Accepted: 03/10/2022] [Indexed: 12/31/2022] Open
Abstract
Over the past three decades, research from the field of social neuroscience has identified a constellation of brain regions that relate to social cognition. Although these studies have provided important insights into the specific neural regions underlying social behavior, they may overlook the broader neural context in which those regions and the interactions between them are embedded. Network neuroscience is an emerging discipline that focuses on modeling and analyzing brain networks-collections of interacting neural elements. Because human cognition requires integrating information across multiple brain regions and systems, we argue that a novel social cognitive network neuroscience approach-which leverages methods from the field of network neuroscience and graph theory-can advance our understanding of how brain systems give rise to social behavior. This review provides an overview of the field of network neuroscience, discusses studies that have leveraged this approach to advance social neuroscience research, highlights the potential contributions of social cognitive network neuroscience to understanding social behavior and provides suggested tools and resources for conducting network neuroscience research.
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Affiliation(s)
- Anne C Krendl
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Richard F Betzel
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN 47405, USA
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12
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Gu S, Jiang M, Guzzi PH, Milenković T. Modeling multi-scale data via a network of networks. Bioinformatics 2022; 38:2544-2553. [PMID: 35238343 PMCID: PMC9048659 DOI: 10.1093/bioinformatics/btac133] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 02/01/2022] [Accepted: 02/28/2022] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Prediction of node and graph labels are prominent network science tasks. Data analyzed in these tasks are sometimes related: entities represented by nodes in a higher-level (higher scale) network can themselves be modeled as networks at a lower level. We argue that systems involving such entities should be integrated with a 'network of networks' (NoNs) representation. Then, we ask whether entity label prediction using multi-level NoN data via our proposed approaches is more accurate than using each of single-level node and graph data alone, i.e. than traditional node label prediction on the higher-level network and graph label prediction on the lower-level networks. To obtain data, we develop the first synthetic NoN generator and construct a real biological NoN. We evaluate accuracy of considered approaches when predicting artificial labels from the synthetic NoNs and proteins' functions from the biological NoN. RESULTS For the synthetic NoNs, our NoN approaches outperform or are as good as node- and network-level ones depending on the NoN properties. For the biological NoN, our NoN approaches outperform the single-level approaches for just under half of the protein functions, and for 30% of the functions, only our NoN approaches make meaningful predictions, while node- and network-level ones achieve random accuracy. So, NoN-based data integration is important. AVAILABILITY AND IMPLEMENTATION The software and data are available at https://nd.edu/~cone/NoNs. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Shawn Gu
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Meng Jiang
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy
| | - Tijana Milenković
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
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13
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Huang Z, Xie Z. A patent keywords extraction method using TextRank model with prior public knowledge. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-021-00343-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
AbstractFor large amount of patent texts, how to extract their keywords in an unsupervised way is a very important problem. In existing methods, only the own information of patent texts is analyzed. In this study, an improved TextRank model is proposed, in which prior public knowledge is effectively utilized. Specifically, two following points are first considered: (1) a TextRank network is constructed for each patent text, (2) a prior knowledge network is constructed based on public dictionary data, in which network edges represent the prior interpretation relationship among all dictionary words in dictionary entries. Then, an improved node rank value evaluation formula is designed for TextRank networks of patent texts, in which prior interpretation information in prior knowledge network are introduced. Finally, patent keywords can be extracted by finding top-k node words with higher node rank values. In our experiments, patent text clustering task is used to examine the performance of proposed method, wherein several comparison experiments are executed. Corresponding results demonstrate that, new method can markedly obtain better performance than existing methods for patent keywords extraction task in an unsupervised way.
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14
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Sanchez-Alonso S, Aslin RN. Towards a model of language neurobiology in early development. BRAIN AND LANGUAGE 2022; 224:105047. [PMID: 34894429 DOI: 10.1016/j.bandl.2021.105047] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 10/24/2021] [Accepted: 10/27/2021] [Indexed: 06/14/2023]
Abstract
Understanding language neurobiology in early childhood is essential for characterizing the developmental structural and functional changes that lead to the mature adult language network. In the last two decades, the field of language neurodevelopment has received increasing attention, particularly given the rapid advances in the implementation of neuroimaging techniques and analytic approaches that allow detailed investigations into the developing brain across a variety of cognitive domains. These methodological and analytical advances hold the promise of developing early markers of language outcomes that allow diagnosis and clinical interventions at the earliest stages of development. Here, we argue that findings in language neurobiology need to be integrated within an approach that captures the dynamic nature and inherent variability that characterizes the developing brain and the interplay between behavior and (structural and functional) neural patterns. Accordingly, we describe a framework for understanding language neurobiology in early development, which minimally requires an explicit characterization of the following core domains: i) computations underlying language learning mechanisms, ii) developmental patterns of change across neural and behavioral measures, iii) environmental variables that reinforce language learning (e.g., the social context), and iv) brain maturational constraints for optimal neural plasticity, which determine the infant's sensitivity to learning from the environment. We discuss each of these domains in the context of recent behavioral and neuroimaging findings and consider the need for quantitatively modeling two main sources of variation: individual differences or trait-like patterns of variation and within-subject differences or state-like patterns of variation. The goal is to enable models that allow prediction of language outcomes from neural measures that take into account these two types of variation. Finally, we examine how future methodological approaches would benefit from the inclusion of more ecologically valid paradigms that complement and allow generalization of traditional controlled laboratory methods.
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Affiliation(s)
| | - Richard N Aslin
- Haskins Laboratories, New Haven, CT, USA; Department of Psychology, Yale University, New Haven, CT, USA; Child Study Center, Yale University, New Haven, CT, USA.
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15
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Iliopoulos AC, Papasotiriou I. Functional Complex Networks Based on Operational Architectonics: Application on Electroencephalography-Brain-computer Interface for Imagined Speech. Neuroscience 2021; 484:98-118. [PMID: 34871742 DOI: 10.1016/j.neuroscience.2021.11.045] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 11/26/2021] [Accepted: 11/29/2021] [Indexed: 01/18/2023]
Abstract
A new method for analyzing brain complex dynamics and states is presented. This method constructs functional brain graphs and is comprised of two pylons: (a) Operational architectonics (OA) concept of brain and mind functioning. (b) Network neuroscience. In particular, the algorithm utilizes OA framework for a non-parametric segmentation of EEGs, which leads to the identification of change points, namely abrupt jumps in EEG amplitude, called Rapid Transition Processes (RTPs). Subsequently, the time coordinates of RTPs are used for the generation of undirected weighted complex networks fulfilling a scale-free topology criterion, from which various network metrics of brain connectivity are estimated. These metrics form feature vectors, which can be used in machine learning algorithms for classification and/or prediction. The method is tested in classification problems on an EEG-based BCI data set, acquired from individuals during imagery pronunciation tasks of various words/vowels. The classification results, based on a Naïve Bayes classifier, show that the overall accuracies were found to be above chance level in all tested cases. This method was also compared with other state-of-the-art computational approaches commonly used for functional network generation, exhibiting competitive performance. The method can be useful to neuroscientists wishing to enhance their repository of brain research algorithms.
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Affiliation(s)
- A C Iliopoulos
- Research Genetic Cancer Centre S.A. Industrial Area of Florina, 53100 Florina, Greece
| | - I Papasotiriou
- Research Genetic Cancer Centre International GmbH, Zug 6300, Switzerland.
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16
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Patrick LM, Anderson KM, Holmes AJ. Local and distributed cortical markers of effort expenditure during sustained goal pursuit. Neuroimage 2021; 244:118602. [PMID: 34563679 DOI: 10.1016/j.neuroimage.2021.118602] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 09/13/2021] [Accepted: 09/18/2021] [Indexed: 11/16/2022] Open
Abstract
The adaptive adjustment of behavior in pursuit of desired goals is critical for survival. To accomplish this complex feat, individuals must weigh the potential benefits of a given action against time, energy, and resource costs. Here, we examine brain responses associated with willingness to exert physical effort during the sustained pursuit of desired goals. Our analyses reveal a distributed pattern of brain activity in aspects of ventral medial prefrontal cortex that tracks with trial-level variability in effort expenditure. Indicating the brain represents echoes of effort at the point of feedback, whole-brain searchlights identified signals reflecting past effort expenditure in medial and lateral prefrontal cortices, encompassing broad swaths of frontoparietal and dorsal attention networks. These data have important implications for our understanding of how the brain's valuation mechanisms contend with the complexity of real-world dynamic environments with relevance for the study of behavior across health and disease.
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Affiliation(s)
- Lauren M Patrick
- Department of Psychology, Yale University, New Haven, CT 06520, United States.
| | - Kevin M Anderson
- Department of Psychology, Yale University, New Haven, CT 06520, United States
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, CT 06520, United States; Department of Psychiatry, Yale University, New Haven, CT 06520, United States; Wu Tsai Institute, Yale University, New Haven, CT 06520, United States.
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17
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Morin TM, Chang AE, Ma W, McGuire JT, Stern CE. Dynamic Network Analysis Demonstrates the Formation of Stable Functional Networks During Rule Learning. Cereb Cortex 2021; 31:5511-5525. [PMID: 34313717 DOI: 10.1093/cercor/bhab175] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 04/20/2021] [Accepted: 05/25/2021] [Indexed: 11/13/2022] Open
Abstract
Variations in the functional connectivity of large-scale cortical brain networks may explain individual differences in learning ability. We used a dynamic network analysis of fMRI data to identify changes in functional brain networks that are associated with context-dependent rule learning. During fMRI scanning, naïve subjects performed a cognitive task designed to test their ability to learn context-dependent rules. Notably, subjects were given minimal instructions about the task prior to scanning. We identified several key network characteristics associated with fast and accurate rule learning. First, consistent with the formation of stable functional networks, a dynamic community detection analysis revealed regionally specific reductions in flexible switching between different functional communities in successful learners. Second, successful rule learners showed decreased centrality of ventral attention regions and increased assortative mixing of cognitive control regions as the rules were learned. Finally, successful subjects showed greater decoupling of default and attention communities throughout the entire task, whereas ventral attention and cognitive control regions became more connected during learning. Overall, the results support a framework by which a stable ventral attention community and more flexible cognitive control community support sustained attention and the formation of rule representations in successful learners.
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Affiliation(s)
- Thomas M Morin
- Graduate Program for Neuroscience, Boston University, Boston, MA 02215, USA.,Cognitive Neuroimaging Center, Boston University, Boston, MA 02215, USA
| | - Allen E Chang
- Cognitive Neuroimaging Center, Boston University, Boston, MA 02215, USA.,Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215, USA
| | - Weida Ma
- Cognitive Neuroimaging Center, Boston University, Boston, MA 02215, USA.,Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Joseph T McGuire
- Graduate Program for Neuroscience, Boston University, Boston, MA 02215, USA.,Cognitive Neuroimaging Center, Boston University, Boston, MA 02215, USA.,Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215, USA
| | - Chantal E Stern
- Graduate Program for Neuroscience, Boston University, Boston, MA 02215, USA.,Cognitive Neuroimaging Center, Boston University, Boston, MA 02215, USA.,Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215, USA
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18
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Dimitriadis SI, Messaritaki E, K Jones D. The impact of graph construction scheme and community detection algorithm on the repeatability of community and hub identification in structural brain networks. Hum Brain Mapp 2021; 42:4261-4280. [PMID: 34170066 PMCID: PMC8356981 DOI: 10.1002/hbm.25545] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 05/14/2021] [Indexed: 12/20/2022] Open
Abstract
A critical question in network neuroscience is how nodes cluster together to form communities, to form the mesoscale organisation of the brain. Various algorithms have been proposed for identifying such communities, each identifying different communities within the same network. Here, (using test–retest data from the Human Connectome Project), the repeatability of thirty‐three community detection algorithms, each paired with seven different graph construction schemes were assessed. Repeatability of community partition depended heavily on both the community detection algorithm and graph construction scheme. Hard community detection algorithms (in which each node is assigned to only one community) outperformed soft ones (in which each node can belong to more than one community). The highest repeatability was observed for the fast multi‐scale community detection algorithm paired with a graph construction scheme that combines nine white matter metrics. This pair also gave the highest similarity between representative group community affiliation and individual community affiliation. Connector hubs had higher repeatability than provincial hubs. Our results provide a workflow for repeatable identification of structural brain networks communities, based on the optimal pairing of community detection algorithm and graph construction scheme.
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Affiliation(s)
- Stavros I Dimitriadis
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK.,Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.,School of Psychology, Cardiff University, Cardiff, UK.,Neuroinformatics Group, Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK.,Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK.,MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, UK
| | - Eirini Messaritaki
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK.,Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.,School of Psychology, Cardiff University, Cardiff, UK.,BRAIN Biomedical Research Unit, Cardiff University, Cardiff, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK.,School of Psychology, Cardiff University, Cardiff, UK
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19
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Abstract
Strong foundational skills in mathematical problem solving, acquired in early childhood, are critical not only for success in the science, technology, engineering, and mathematical (STEM) fields but also for quantitative reasoning in everyday life. The acquisition of mathematical skills relies on protracted interactive specialization of functional brain networks across development. Using a systems neuroscience approach, this review synthesizes emerging perspectives on neurodevelopmental pathways of mathematical learning, highlighting the functional brain architecture that supports these processes and sources of heterogeneity in mathematical skill acquisition. We identify the core neural building blocks of numerical cognition, anchored in the posterior parietal and ventral temporal-occipital cortices, and describe how memory and cognitive control systems, anchored in the medial temporal lobe and prefrontal cortex, help scaffold mathematical skill development. We highlight how interactive specialization of functional circuits influences mathematical learning across different stages of development. Functional and structural brain integrity and plasticity associated with math learning can be examined using an individual differences approach to better understand sources of heterogeneity in learning, including cognitive, affective, motivational, and sociocultural factors. Our review emphasizes the dynamic role of neurodevelopmental processes in mathematical learning and cognitive development more generally.
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Affiliation(s)
- Vinod Menon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA
- Stanford Neuroscience Institute, Stanford University School of Medicine, Stanford, California, USA
- Symbolic Systems Program, Stanford University School of Medicine, Stanford, California, USA
| | - Hyesang Chang
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA
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20
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Yin D, Wang X, Zhang X, Yu Q, Wei Y, Cai Q, Fan M, Li L. Dissociable plasticity of visual-motor system in functional specialization and flexibility in expert table tennis players. Brain Struct Funct 2021; 226:1973-1990. [PMID: 34041612 DOI: 10.1007/s00429-021-02304-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 05/19/2021] [Indexed: 10/21/2022]
Abstract
Specialization and flexibility are two basic attributes of functional brain organization, enabling efficient cognition and behavior. However, it is largely unknown what plastic changes in specialization and flexibility in visual-motor areas occur in support of extraordinary motor skills in expert athletes and how the selective adaptability of the visual-motor system affects general perceptual or cognitive domains. Here, we used a dynamic network framework to investigate intrinsic functional specialization and flexibility of visual-motor system in expert table tennis players (TTP). Our results showed that sensorimotor areas increased intrinsic functional flexibility, whereas visual areas increased intrinsic functional specialization in expert TTP compared to nonathletes. Moreover, the flexibility of the left putamen was positively correlated with skill level, and that of the left lingual gyrus was positively correlated with behavioral accuracy of a sport-unrelated attention task. This study has uncovered dissociable plasticity of the visual-motor system and their predictions of individual differences in skill level and general attention processing. Furthermore, our time-resolved analytic approach is applicable across other professional athletes for understanding their brain plasticity and superior behavior.
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Affiliation(s)
- Dazhi Yin
- Key Laboratory of Brain Functional Genomics (Ministry of Education and Shanghai), School of Psychology and Cognitive Science, East China Normal University, Shanghai, 200062, China.
| | - Xuefei Wang
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, 200062, China
| | - Xiaoyou Zhang
- Key Laboratory of Adolescent Health Assessment and Exercise Intervention of Ministry of Education, College of Physical Education and Health, East China Normal University, Shanghai, 200062, China
| | - Qiurong Yu
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, 200062, China
| | - Yu Wei
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, 200062, China
| | - Qing Cai
- Key Laboratory of Brain Functional Genomics (Ministry of Education and Shanghai), School of Psychology and Cognitive Science, East China Normal University, Shanghai, 200062, China
| | - Mingxia Fan
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, 200062, China.
| | - Lin Li
- Key Laboratory of Adolescent Health Assessment and Exercise Intervention of Ministry of Education, College of Physical Education and Health, East China Normal University, Shanghai, 200062, China.
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21
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Zhang Y, Wang C, Yao Y, Zhou C, Chen F. Adaptive Reconfiguration of Intrinsic Community Structure in Children with 5-Year Abacus Training. Cereb Cortex 2021; 31:3122-3135. [PMID: 33585902 DOI: 10.1093/cercor/bhab010] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 12/17/2020] [Accepted: 01/04/2020] [Indexed: 01/21/2023] Open
Abstract
Human learning can be understood as a network phenomenon, underpinned by the adaptive reconfiguration of modular organization. However, the plasticity of community structure (CS) in resting-state network induced by cognitive intervention has never been investigated. Here, we explored the individual difference of intrinsic CS between children with 5-year abacus-based mental calculation (AMC) training (35 subjects) and their peers without prior experience in AMC (31 subjects). Using permutation-based analysis between subjects in the two groups, we found the significant alteration of intrinsic CS, with training-attenuated individual difference. The alteration of CS focused on selective subsets of cortical regions ("core areas"), predominantly affiliated to the visual, somatomotor, and default-mode subsystems. These subsystems exhibited training-promoted cohesion with attenuated interaction between them, from the perspective of individuals' CS. Moreover, the cohesion of visual network could predict training-improved math ability in the AMC group, but not in the control group. Finally, the whole network displayed enhanced segregation in the AMC group, including higher modularity index, more provincial hubs, lower participation coefficient, and fewer between-module links, largely due to the segregation of "core areas." Collectively, our findings suggested that the intrinsic CS could get reconfigured toward more localized processing and segregated architecture after long-term cognitive training.
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Affiliation(s)
- Yi Zhang
- Bio-X Laboratory, Interdisciplinary Center for Quantum Information, State Key Laboratory of Modern Optical Instrumentation, and Zhejiang Province Key Laboratory of Quantum Technology and Device, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Chunjie Wang
- Bio-X Laboratory, Interdisciplinary Center for Quantum Information, State Key Laboratory of Modern Optical Instrumentation, and Zhejiang Province Key Laboratory of Quantum Technology and Device, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Yuzhao Yao
- Bio-X Laboratory, Interdisciplinary Center for Quantum Information, State Key Laboratory of Modern Optical Instrumentation, and Zhejiang Province Key Laboratory of Quantum Technology and Device, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Changsong Zhou
- Bio-X Laboratory, Interdisciplinary Center for Quantum Information, State Key Laboratory of Modern Optical Instrumentation, and Zhejiang Province Key Laboratory of Quantum Technology and Device, Department of Physics, Zhejiang University, Hangzhou 310027, China.,Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
| | - Feiyan Chen
- Bio-X Laboratory, Interdisciplinary Center for Quantum Information, State Key Laboratory of Modern Optical Instrumentation, and Zhejiang Province Key Laboratory of Quantum Technology and Device, Department of Physics, Zhejiang University, Hangzhou 310027, China
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22
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Meshulam M, Hasenfratz L, Hillman H, Liu YF, Nguyen M, Norman KA, Hasson U. Neural alignment predicts learning outcomes in students taking an introduction to computer science course. Nat Commun 2021; 12:1922. [PMID: 33771999 PMCID: PMC7997890 DOI: 10.1038/s41467-021-22202-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 02/23/2021] [Indexed: 11/10/2022] Open
Abstract
Despite major advances in measuring human brain activity during and after educational experiences, it is unclear how learners internalize new content, especially in real-life and online settings. In this work, we introduce a neural approach to predicting and assessing learning outcomes in a real-life setting. Our approach hinges on the idea that successful learning involves forming the right set of neural representations, which are captured in canonical activity patterns shared across individuals. Specifically, we hypothesized that learning is mirrored in neural alignment: the degree to which an individual learner's neural representations match those of experts, as well as those of other learners. We tested this hypothesis in a longitudinal functional MRI study that regularly scanned college students enrolled in an introduction to computer science course. We additionally scanned graduate student experts in computer science. We show that alignment among students successfully predicts overall performance in a final exam. Furthermore, within individual students, we find better learning outcomes for concepts that evoke better alignment with experts and with other students, revealing neural patterns associated with specific learned concepts in individuals.
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Affiliation(s)
- Meir Meshulam
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA. .,Department of Psychology, Princeton University, Princeton, NJ, USA.
| | - Liat Hasenfratz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.,Department of Psychology, Princeton University, Princeton, NJ, USA
| | - Hanna Hillman
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.,Department of Psychology, Princeton University, Princeton, NJ, USA
| | - Yun-Fei Liu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.,Department of Psychology, Princeton University, Princeton, NJ, USA
| | - Mai Nguyen
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.,Department of Psychology, Princeton University, Princeton, NJ, USA
| | - Kenneth A Norman
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.,Department of Psychology, Princeton University, Princeton, NJ, USA
| | - Uri Hasson
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.,Department of Psychology, Princeton University, Princeton, NJ, USA
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23
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Supekar K, Chang H, Mistry PK, Iuculano T, Menon V. Neurocognitive modeling of latent memory processes reveals reorganization of hippocampal-cortical circuits underlying learning and efficient strategies. Commun Biol 2021; 4:405. [PMID: 33767350 PMCID: PMC7994581 DOI: 10.1038/s42003-021-01872-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 02/10/2021] [Indexed: 11/08/2022] Open
Abstract
Efficient memory-based problem-solving strategies are a cardinal feature of expertise across a wide range of cognitive domains in childhood. However, little is known about the neurocognitive mechanisms that underlie the acquisition of efficient memory-based problem-solving strategies. Here we develop, to the best of our knowledge, a novel neurocognitive process model of latent memory processes to investigate how cognitive training designed to improve children's problem-solving skills alters brain network organization and leads to increased use and efficiency of memory retrieval-based strategies. We found that training increased both the use and efficiency of memory retrieval. Functional brain network analysis revealed training-induced changes in modular network organization, characterized by increase in network modules and reorganization of hippocampal-cortical circuits. Critically, training-related changes in modular network organization predicted performance gains, with emergent hippocampal, rather than parietal cortex, circuitry driving gains in efficiency of memory retrieval. Our findings elucidate a neurocognitive process model of brain network mechanisms that drive learning and gains in children's efficient problem-solving strategies.
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Affiliation(s)
- Kaustubh Supekar
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA.
| | - Hyesang Chang
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Percy K Mistry
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Teresa Iuculano
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA
- Developmental Psychology and Child Education Laboratory, University Paris Descartes, Paris, France
| | - Vinod Menon
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA.
- Stanford Neuroscience Institute, Stanford University, Stanford, CA, USA.
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24
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Gonzalez-Astudillo J, Cattai T, Bassignana G, Corsi MC, De Vico Fallani F. Network-based brain computer interfaces: principles and applications. J Neural Eng 2020; 18. [PMID: 33147577 DOI: 10.1088/1741-2552/abc760] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 11/04/2020] [Indexed: 12/17/2022]
Abstract
Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback (NFB) rehabilitation. In general, BCI usability critically depends on the ability to comprehensively characterize brain functioning and correctly identify the user's mental state. To this end, much of the efforts have focused on improving the classification algorithms taking into account localized brain activities as input features. Despite considerable improvement BCI performance is still unstable and, as a matter of fact, current features represent oversimplified descriptors of brain functioning. In the last decade, growing evidence has shown that the brain works as a networked system composed of multiple specialized and spatially distributed areas that dynamically integrate information. While more complex, looking at how remote brain regions functionally interact represents a grounded alternative to better describe brain functioning. Thanks to recent advances in network science, i.e. a modern field that draws on graph theory, statistical mechanics, data mining and inferential modelling, scientists have now powerful means to characterize complex brain networks derived from neuroimaging data. Notably, summary features can be extracted from these networks to quantitatively measure specific organizational properties across a variety of topological scales. In this topical review, we aim to provide the state-of-the-art supporting the development of a network theoretic approach as a promising tool for understanding BCIs and improve usability.
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25
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Nydam AS, Sewell DK, Dux PE. Effects of tDCS on visual statistical learning. Neuropsychologia 2020; 148:107652. [PMID: 33069791 DOI: 10.1016/j.neuropsychologia.2020.107652] [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: 04/03/2020] [Revised: 09/25/2020] [Accepted: 10/03/2020] [Indexed: 11/25/2022]
Abstract
Visual statistical learning describes the encoding of structure in sensory input, and it has important consequences for cognition and behaviour. Higher-order brain regions in the prefrontal and posterior parietal cortices have been associated with statistical learning behaviours. Yet causal evidence of a cortical contribution remains limited. In a recent study, the modulation of cortical activity by transcranial direct current stimulation (tDCS) disrupted statistical learning in a spatial contextual cueing phenomenon; supporting a cortical role. Here, we examined whether the same tDCS protocol would influence statistical learning assessed by the Visual Statistical Learning phenomenon (i.e., Fiser and Aslin, 2001), which uses identity-based regularities while controlling for spatial location. In Experiment 1, we employed the popular exposure-test design to tap the learning of structure after passive viewing. Using a large sample (N = 150), we found no effect of the tDCS protocol when compared to a sham control nor to an active control region. In Experiment 2 (N = 80), we developed an online task that was sensitive to the timecourse of learning. Under these task conditions, we did observe a stimulation effect on learning, consistent with the previous work. The way tDCS affected learning appeared to be task-specific; expediting statistical learning in this case. Together with the existing evidence, these findings support the hypothesis that cortical areas are involved in the visual statistical learning process, and suggest the mechanisms of cortical involvement may be task-dependent and dynamic across time.
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Affiliation(s)
- Abbey S Nydam
- School of Psychology, The University of Queensland, Brisbane, Australia.
| | - David K Sewell
- School of Psychology, The University of Queensland, Brisbane, Australia
| | - Paul E Dux
- School of Psychology, The University of Queensland, Brisbane, Australia
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26
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Sanchez-Alonso S, Aslin RN. Predictive modeling of neurobehavioral state and trait variation across development. Dev Cogn Neurosci 2020; 45:100855. [PMID: 32942148 PMCID: PMC7501421 DOI: 10.1016/j.dcn.2020.100855] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 08/26/2020] [Accepted: 09/04/2020] [Indexed: 11/24/2022] Open
Abstract
A key goal of human neurodevelopmental research is to map neural and behavioral trajectories across both health and disease. A growing number of developmental consortia have begun to address this gap by providing open access to cross-sectional and longitudinal 'big data' repositories. However, it remains challenging to develop models that enable prediction of both within-subject and between-subject neurodevelopmental variation. Here, we present a conceptual and analytical perspective of two essential ingredients for mapping neurodevelopmental trajectories: state and trait components of variance. We focus on mapping variation across a range of neural and behavioral measurements and consider concurrent alterations of state and trait variation across development. We present a quantitative framework for combining both state- and trait-specific sources of neurobehavioral variation across development. Specifically, we argue that non-linear mixed growth models that leverage state and trait components of variance and consider environmental factors are necessary to comprehensively map brain-behavior relationships. We discuss this framework in the context of mapping language neurodevelopmental changes in early childhood, with an emphasis on measures of functional connectivity and their reliability for establishing robust neurobehavioral relationships. The ultimate goal is to statistically unravel developmental trajectories of neurobehavioral relationships that involve a combination of individual differences and age-related changes.
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27
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Wen X, Wang R, Yin W, Lin W, Zhang H, Shen D. Development of Dynamic Functional Architecture during Early Infancy. Cereb Cortex 2020; 30:5626-5638. [PMID: 32537641 DOI: 10.1093/cercor/bhaa128] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Revised: 03/24/2020] [Accepted: 04/21/2020] [Indexed: 02/07/2023] Open
Abstract
Uncovering the moment-to-moment dynamics of functional connectivity (FC) in the human brain during early development is crucial for understanding emerging complex cognitive functions and behaviors. To this end, this paper leveraged a longitudinal resting-state functional magnetic resonance imaging dataset from 51 typically developing infants and, for the first time, thoroughly investigated how the temporal variability of the FC architecture develops at the "global" (entire brain), "mesoscale" (functional system), and "local" (brain region) levels in the first 2 years of age. Our results revealed that, in such a pivotal stage, 1) the whole-brain FC dynamic is linearly increased; 2) the high-order functional systems tend to display increased FC dynamics for both within- and between-network connections, while the primary systems show the opposite trajectories; and 3) many frontal regions have increasing FC dynamics despite large heterogeneity in developmental trajectories and velocities. All these findings indicate that the brain is gradually reconfigured toward a more flexible, dynamic, and adaptive system with globally increasing but locally heterogeneous trajectories in the first 2 postnatal years, explaining why infants have rapidly developing high-order cognitive functions and complex behaviors.
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Affiliation(s)
- Xuyun Wen
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China.,Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Rifeng Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Weiyan Yin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Han Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.,Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
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Stiso J, Corsi MC, Vettel JM, Garcia J, Pasqualetti F, De Vico Fallani F, Lucas TH, Bassett DS. Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior. J Neural Eng 2020; 17:046018. [PMID: 32369802 PMCID: PMC7734596 DOI: 10.1088/1741-2552/ab9064] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Motor imagery-based brain-computer interfaces (BCIs) use an individual's ability to volitionally modulate localized brain activity, often as a therapy for motor dysfunction or to probe causal relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limiting the efficacy of BCI for therapy and for basic scientific inquiry. Formal experiments designed to probe the nature of BCI learning have offered initial evidence that coherent activity across spatially distributed and functionally diverse cognitive systems is a hallmark of individuals who can successfully learn to control the BCI. However, little is known about how these distributed networks interact through time to support learning. APPROACH Here, we address this gap in knowledge by constructing and applying a multimodal network approach to decipher brain-behavior relations in motor imagery-based brain-computer interface learning using magnetoencephalography. Specifically, we employ a minimally constrained matrix decomposition method - non-negative matrix factorization - to simultaneously identify regularized, covarying subgraphs of functional connectivity, to assess their similarity to task performance, and to detect their time-varying expression. MAIN RESULTS We find that learning is marked by diffuse brain-behavior relations: good learners displayed many subgraphs whose temporal expression tracked performance. Individuals also displayed marked variation in the spatial properties of subgraphs such as the connectivity between the frontal lobe and the rest of the brain, and in the temporal properties of subgraphs such as the stage of learning at which they reached maximum expression. From these observations, we posit a conceptual model in which certain subgraphs support learning by modulating brain activity in sensors near regions important for sustaining attention. To test this model, we use tools that stipulate regional dynamics on a networked system (network control theory), and find that good learners display a single subgraph whose temporal expression tracked performance and whose architecture supports easy modulation of sensors located near brain regions important for attention. SIGNIFICANCE The nature of our contribution to the neuroscience of BCI learning is therefore both computational and theoretical; we first use a minimally-constrained, individual specific method of identifying mesoscale structure in dynamic brain activity to show how global connectivity and interactions between distributed networks supports BCI learning, and then we use a formal network model of control to lend theoretical support to the hypothesis that these identified subgraphs are well suited to modulate attention.
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Affiliation(s)
- Jennifer Stiso
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marie-Constance Corsi
- Inria Paris, Aramis project-team, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Epinire, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Universit, F-75013, Paris, France
| | - Jean M. Vettel
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Human Research & Engineering Directorate, US CCDC Army Research Laboratory, Aberdeen, MD, USA
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, CA, USA
| | - Javier Garcia
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Human Research & Engineering Directorate, US CCDC Army Research Laboratory, Aberdeen, MD, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, CA 92521
| | - Fabrizio De Vico Fallani
- Inria Paris, Aramis project-team, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Epinire, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Universit, F-75013, Paris, France
| | - Timothy H. Lucas
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S. Bassett
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- The Santa Fe Institute, Santa Fe, NM 87501, USA
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29
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Baek EC, Porter MA, Parkinson C. Social network analysis for social neuroscientists. Soc Cogn Affect Neurosci 2020; 16:883-901. [PMID: 32415969 PMCID: PMC8343567 DOI: 10.1093/scan/nsaa069] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 02/11/2020] [Accepted: 05/11/2020] [Indexed: 01/11/2023] Open
Abstract
Although social neuroscience is concerned with understanding how the brain interacts with its social environment, prevailing research in the field has primarily considered the human brain in isolation, deprived of its rich social context. Emerging work in social neuroscience that leverages tools from network analysis has begun to advance knowledge of how the human brain influences and is influenced by the structures of its social environment. In this paper, we provide an overview of key theory and methods in network analysis (especially for social systems) as an introduction for social neuroscientists who are interested in relating individual cognition to the structures of an individual’s social environments. We also highlight some exciting new work as examples of how to productively use these tools to investigate questions of relevance to social neuroscientists. We include tutorials to help with practical implementations of the concepts that we discuss. We conclude by highlighting a broad range of exciting research opportunities for social neuroscientists who are interested in using network analysis to study social systems.
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Affiliation(s)
- Elisa C Baek
- Department of Psychology, University of California, Los Angeles, CA 90095, USA
| | - Mason A Porter
- Department of Mathematics, University of California, Los Angeles, CA 90095, USA
| | - Carolyn Parkinson
- Department of Psychology, University of California, Los Angeles, CA 90095, USA.,Brain Research Institute, University of California, Los Angeles, CA 90095, USA
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30
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Allegra M, Seyed-Allaei S, Schuck NW, Amati D, Laio A, Reverberi C. Brain network dynamics during spontaneous strategy shifts and incremental task optimization. Neuroimage 2020; 217:116854. [PMID: 32334091 DOI: 10.1016/j.neuroimage.2020.116854] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 03/06/2020] [Accepted: 04/10/2020] [Indexed: 01/08/2023] Open
Abstract
With practice, humans improve their performance in a task by either optimizing a known strategy or discovering a novel, potentially more fruitful strategy. We investigated the neural processes underlying these two fundamental abilities by applying fMRI in a task with two possible alternative strategies. For analysis we combined time-resolved network analysis with Coherence Density Peak Clustering (Allegra et al., 2017), univariate GLM, and multivariate pattern classification. Converging evidence showed that the posterior portion of the default network, i.e. the precuneus and the angular gyrus bilaterally, has a central role in the optimization of the current strategy. These regions encoded the relevant spatial information, increased the strength of local connectivity as well as the long-distance connectivity with other relevant regions in the brain (e.g., visual cortex, dorsal attention network). The connectivity increase was proportional to performance optimization. By contrast, the anterior portion of the default network (i.e. medial prefrontal cortex) and the rostral portion of the fronto-parietal network were associated with new strategy discovery: an early increase of local and long-range connectivity centered on these regions was only observed in the subjects who would later shift to a new strategy. Overall, our findings shed light on the dynamic interactions between regions related to attention and with cognitive control, underlying the balance between strategy exploration and exploitation. Results suggest that the default network, far from being "shut-down" during task performance, has a pivotal role in the background exploration and monitoring of potential alternative courses of action.
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Affiliation(s)
- Michele Allegra
- Scuola Internazionale Superiore di Studi Avanzati, Trieste, 34136, Trieste, Italy; Institut de Neurosciences de la Timone, Aix Marseille Université, UMR 7289 CNRS, 13005, Marseille, France.
| | - Shima Seyed-Allaei
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5746, Tehran, Iran
| | - Nicolas W Schuck
- Max Planck Research Group NeuroCode, Max Planck Institute for Human Development, 14195, Berlin, Germany; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
| | - Daniele Amati
- Scuola Internazionale Superiore di Studi Avanzati, Trieste, 34136, Trieste, Italy
| | - Alessandro Laio
- Scuola Internazionale Superiore di Studi Avanzati, Trieste, 34136, Trieste, Italy; International Centre for Theoretical Physics, 34100, Trieste, Italy
| | - Carlo Reverberi
- Department of Psychology, Università Milano - Bicocca, Milan, Italy; NeuroMI - Milan Center for Neuroscience, Milan, Italy.
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31
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A new method for vibration-based neurophenotyping of zebrafish. J Neurosci Methods 2020; 333:108563. [DOI: 10.1016/j.jneumeth.2019.108563] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 12/12/2019] [Accepted: 12/17/2019] [Indexed: 02/08/2023]
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32
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Inoue DS, Antunes BM, Maideen MFB, Lira FS. Pathophysiological Features of Obesity and its Impact on Cognition: Exercise Training as a Non-Pharmacological Approach. Curr Pharm Des 2020; 26:916-931. [PMID: 31942854 DOI: 10.2174/1381612826666200114102524] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 11/25/2019] [Indexed: 02/08/2023]
Abstract
BACKGROUND The number of individuals with obesity is growing worldwide and this is a worrying trend, as obesity has shown to cause pathophysiological changes, which result in the emergence of comorbidities such as cardiovascular disease, diabetes mellitus type 2 and cancer. In addition, cognitive performance may be compromised by immunometabolic deregulation of obesity. Although in more critical cases, the use of medications is recommended, a physically active lifestyle is one of the main foundations for health maintenance, making physical training an important tool to reduce the harmful effects of excessive fat accumulation. AIM The purpose of this review of the literature is to present the impact of immunometabolic alterations on cognitive function in individuals with obesity, and the role of exercise training as a non-pharmacological approach to improve the inflammatory profile, energy metabolism and neuroplasticity in obesity. METHOD An overview of the etiology and pathophysiology of obesity to establish a possible link with cognitive performance in obese individuals, with the executive function being one of the most affected cognitive components. In addition, the brain-derived neurotrophic factor (BDNF) profile and its impact on cognition in obese individuals are discussed. Lastly, studies showing regular resistance and/or aerobic training, which may be able to improve the pathophysiological condition and cognitive performance through the improvement of the inflammatory profile, decreased insulin resistance and higher BDNF production are discussed. CONCLUSION Exercise training is essential for reestablishment and maintenance of health by increasing energy expenditure, insulin resistance reduction, anti-inflammatory proteins and neurotrophin production corroborating to upregulation of body function.
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Affiliation(s)
- Daniela S Inoue
- Exercise and Immunometabolism Research Group, Post-Graduation Program in Movement Sciences, Department of Physical Education, State University (UNESP), School of Technology and Sciences, Presidente Prudente, Sao Paulo, Brazil
| | - Bárbara M Antunes
- Exercise and Immunometabolism Research Group, Post-Graduation Program in Movement Sciences, Department of Physical Education, State University (UNESP), School of Technology and Sciences, Presidente Prudente, Sao Paulo, Brazil
| | - Mohammad F B Maideen
- Faculty of Health Sciences, Thermal Ergonomics Laboratory, The University of Sydney, NSW, Australia.,Charles Perkins Centre, The University of Sydney, NSW, Australia
| | - Fábio S Lira
- Exercise and Immunometabolism Research Group, Post-Graduation Program in Movement Sciences, Department of Physical Education, State University (UNESP), School of Technology and Sciences, Presidente Prudente, Sao Paulo, Brazil
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33
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Betzel RF, Wood KC, Angeloni C, Neimark Geffen M, Bassett DS. Stability of spontaneous, correlated activity in mouse auditory cortex. PLoS Comput Biol 2019; 15:e1007360. [PMID: 31815941 PMCID: PMC6968873 DOI: 10.1371/journal.pcbi.1007360] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 01/17/2020] [Accepted: 08/24/2019] [Indexed: 12/31/2022] Open
Abstract
Neural systems can be modeled as complex networks in which neural elements are represented as nodes linked to one another through structural or functional connections. The resulting network can be analyzed using mathematical tools from network science and graph theory to quantify the system’s topological organization and to better understand its function. Here, we used two-photon calcium imaging to record spontaneous activity from the same set of cells in mouse auditory cortex over the course of several weeks. We reconstruct functional networks in which cells are linked to one another by edges weighted according to the correlation of their fluorescence traces. We show that the networks exhibit modular structure across multiple topological scales and that these multi-scale modules unfold as part of a hierarchy. We also show that, on average, network architecture becomes increasingly dissimilar over time, with similarity decaying monotonically with the distance (in time) between sessions. Finally, we show that a small fraction of cells maintain strongly-correlated activity over multiple days, forming a stable temporal core surrounded by a fluctuating and variable periphery. Our work indicates a framework for studying spontaneous activity measured by two-photon calcium imaging using computational methods and graphical models from network science. The methods are flexible and easily extended to additional datasets, opening the possibility of studying cellular level network organization of neural systems and how that organization is modulated by stimuli or altered in models of disease. Neurons coordinate their activity with one another, forming networks that help support adaptive, flexible behavior. Still, little is known about the organization of these networks at the cellular scale and their stability over time. Here, we reconstruct networks from calcium imaging data recorded in mouse primary auditory cortex. We show that these networks exhibit spatially constrained, hierarchical modular structure, which may facilitate specialized information processing. However, we show that connection weights and modular structure are also variable over time, changing on a timescale of days and adopting novel network configurations. Despite this, a small subset of neurons maintain their connections to one another and preserve their modular organization across time, forming a stable temporal core surrounded by a flexible periphery. These findings represent a conceptual bridge linking network analyses of macroscale and cellular-level neuroimaging data. They also represent a complementary approach to existing circuits- and systems-based interrogation of nervous system function, opening the door for deeper and more targeted analysis in the future.
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Affiliation(s)
- Richard F Betzel
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, United States of America.,Cognitive Science Program, Indiana University, Bloomington, Indiana, United States of America.,Program in Neuroscience, Indiana University, Bloomington, Indiana, United States of America.,Network Science Institute, Indiana University, Bloomington, Indiana, United States of America
| | - Katherine C Wood
- Department of Otorhinolaryngology: HNS, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Christopher Angeloni
- Department of Otorhinolaryngology: HNS, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Maria Neimark Geffen
- Department of Otorhinolaryngology: HNS, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.,Santa Fe Institute, Santa Fa, New Mexico, United States of America
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34
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Seghier ML, Fahim MA, Habak C. Educational fMRI: From the Lab to the Classroom. Front Psychol 2019; 10:2769. [PMID: 31866920 PMCID: PMC6909003 DOI: 10.3389/fpsyg.2019.02769] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 11/25/2019] [Indexed: 12/23/2022] Open
Abstract
Functional MRI (fMRI) findings hold many potential applications for education, and yet, the translation of fMRI findings to education has not flowed. Here, we address the types of fMRI that could better support applications of neuroscience to the classroom. This 'educational fMRI' comprises eight main challenges: (1) collecting artifact-free fMRI data in school-aged participants and in vulnerable young populations, (2) investigating heterogenous cohorts with wide variability in learning abilities and disabilities, (3) studying the brain under natural and ecological conditions, given that many practical topics of interest for education can be addressed only in ecological contexts, (4) depicting complex age-dependent associations of brain and behaviour with multi-modal imaging, (5) assessing changes in brain function related to developmental trajectories and instructional intervention with longitudinal designs, (6) providing system-level mechanistic explanations of brain function, so that useful individualized predictions about learning can be generated, (7) reporting negative findings, so that resources are not wasted on developing ineffective interventions, and (8) sharing data and creating large-scale longitudinal data repositories to ensure transparency and reproducibility of fMRI findings for education. These issues are of paramount importance to the development of optimal fMRI practices for educational applications.
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Affiliation(s)
- Mohamed L Seghier
- Cognitive Neuroimaging Unit, Emirates College for Advanced Education (ECAE), Abu Dhabi, United Arab Emirates
| | - Mohamed A Fahim
- Cognitive Neuroimaging Unit, Emirates College for Advanced Education (ECAE), Abu Dhabi, United Arab Emirates
| | - Claudine Habak
- Cognitive Neuroimaging Unit, Emirates College for Advanced Education (ECAE), Abu Dhabi, United Arab Emirates
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35
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Chai MT, Amin HU, Izhar LI, Saad MNM, Abdul Rahman M, Malik AS, Tang TB. Exploring EEG Effective Connectivity Network in Estimating Influence of Color on Emotion and Memory. Front Neuroinform 2019; 13:66. [PMID: 31649522 PMCID: PMC6794354 DOI: 10.3389/fninf.2019.00066] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 09/18/2019] [Indexed: 11/20/2022] Open
Abstract
Color is a perceptual stimulus that has a significant impact on improving human emotion and memory. Studies have revealed that colored multimedia learning materials (MLMs) have a positive effect on learner's emotion and learning where it was assessed by subjective/objective measurements. This study aimed to quantitatively assess the influence of colored MLMs on emotion, cognitive processes during learning, and long-term memory (LTM) retention using electroencephalography (EEG). The dataset consisted of 45 healthy participants, and MLMs were designed in colored or achromatic illustrations to elicit emotion and that to assess its impact on LTM retention after 30-min and 1-month delay. The EEG signal analysis was first started to estimate the effective connectivity network (ECN) using the phase slope index and expand it to characterize the ECN pattern using graph theoretical analysis. EEG results showed that colored MLMs had influences on theta and alpha networks, including (1) an increased frontal-parietal connectivity (top-down processing), (2) a larger number of brain hubs, (3) a lower clustering coefficient, and (4) a higher local efficiency, indicating that color influences information processing in the brain, as reflected by ECN, together with a significant improvement in learner's emotion and memory performance. This is evidenced by a more positive emotional valence and higher recall accuracy for groups who learned with colored MLMs than that of achromatic MLMs. In conclusion, this paper demonstrated how the EEG ECN parameters could help quantify the influences of colored MLMs on emotion and cognitive processes during learning.
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Affiliation(s)
- Meei Tyng Chai
- Centre for Intelligent Signal and Imaging Research, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
| | - Hafeez Ullah Amin
- Centre for Intelligent Signal and Imaging Research, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
| | - Lila Iznita Izhar
- Centre for Intelligent Signal and Imaging Research, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
| | - Mohamad Naufal Mohamad Saad
- Centre for Intelligent Signal and Imaging Research, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
| | - Mohammad Abdul Rahman
- Faculty of Medicine, Royal College of Medicine Perak, Universiti Kuala Lumpur, Ipoh, Malaysia
| | | | - Tong Boon Tang
- Centre for Intelligent Signal and Imaging Research, Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia
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36
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Girn M, Mills C, Christoff K. Linking brain network reconfiguration and intelligence: Are we there yet? Trends Neurosci Educ 2019; 15:62-70. [PMID: 31176472 DOI: 10.1016/j.tine.2019.04.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 02/22/2019] [Accepted: 04/04/2019] [Indexed: 01/08/2023]
Abstract
Recent applications of dynamic network analyses to functional neuroimaging data have revealed relationships between a number of cognition conditions and the dynamic reconfiguration of brain networks. Here we critically review such applications of network neuroscience to intelligence. After providing an overview of network neuroscience, we center our discussion around the recently proposed Network Neuroscience Theory of Intelligence (Barbey, 2017). We evaluate and review existing empirical support for the theses made by this theory and argue that while studies strongly suggest their plausibility, evidence to date has largely been indirect. We propose avenues for future research to directly evaluate these theses by overcoming the methodological and analytical shortcomings of previous studies. In doing so, our goal is to stimulate future empirical investigations and present valuable ways forward in the network neuroscience of intelligence.
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Affiliation(s)
- Manesh Girn
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Department of Psychology, University of British Columbia, Vancouver, British Columbia.
| | - Caitlin Mills
- Department of Psychology, University of New Hampshire, Durham, New Hampshire
| | - Kalina Christoff
- Department of Psychology, University of British Columbia, Vancouver, British Columbia; Centre for Brain Health, University of British Columbia, Vancouver, British Columbia
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37
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Papo D. Neurofeedback: Principles, appraisal, and outstanding issues. Eur J Neurosci 2019; 49:1454-1469. [PMID: 30570194 DOI: 10.1111/ejn.14312] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 11/21/2018] [Accepted: 11/27/2018] [Indexed: 12/16/2022]
Abstract
Neurofeedback is a form of brain training in which subjects are fed back information about some measure of their brain activity which they are instructed to modify in a way thought to be functionally advantageous. Over the last 20 years, neurofeedback has been used to treat various neurological and psychiatric conditions, and to improve cognitive function in various contexts. However, in spite of a growing popularity, neurofeedback protocols typically make (often covert) assumptions on what aspects of brain activity to target, where in the brain to act and how, which have far-reaching implications for the assessment of its potential and efficacy. Here we critically examine some conceptual and methodological issues associated with the way neurofeedback's general objectives and neural targets are defined. The neural mechanisms through which neurofeedback may act at various spatial and temporal scales, and the way its efficacy is appraised are reviewed, and the extent to which neurofeedback may be used to control functional brain activity discussed. Finally, it is proposed that gauging neurofeedback's potential, as well as assessing and improving its efficacy will require better understanding of various fundamental aspects of brain dynamics and a more precise definition of functional brain activity and brain-behaviour relationships.
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Affiliation(s)
- David Papo
- SCALab, CNRS, Université de Lille, Villeneuve d'Ascq, France
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38
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Schott N, Rudisch J, Voelcker-Rehage C. Meilensteine der Motorischen Verhaltensforschung. ZEITSCHRIFT FUR SPORTPSYCHOLOGIE 2019. [DOI: 10.1026/1612-5010/a000259] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Zusammenfassung. Die Forschung zum motorischen Verhalten hat eine lange Tradition, wobei eine Vielzahl von Forschern zu einem breiten und tiefgehenden Verständnis des Themas beigetragen haben. Der Erkenntnisgewinn und Fortschritt in der Theorieentwicklung innerhalb des Feldes war zudem meist nicht-linear, sondern gezeichnet durch schnelle Wachstumsphasen nach der Veröffentlichung wichtiger Forschungsartikel und neuer theoretischer Perspektiven. Diese veränderten die Art und Weise wie wir das motorische Verhalten heute konzipieren; und sie sind noch nicht abgeschlossen. Wir werden einige der innovativsten und wirkungsvollsten Theorien und Entwicklungen auf dem Gebiet des motorischen Verhaltens (untergliedert in die drei Hauptbereiche Entwicklung, Kontrolle und Lernen) des letzten Jahrhunderts skizzieren und diskutieren. Darüber hinaus werden wir frühe, wegweisende Forschungsarbeiten vorstellen, die wir für unverzichtbar für das Studium der Motorikforschung halten. Der Blick zurück soll uns erlauben, eine Richtung für die Zukunft zu zeichnen und zu diskutieren. Diese Forschungsthemen können und werden (hoffentlich) in den nächsten Jahrzehnten in vielen Bereichen der Gesellschaft, einschließlich des Sports und der Bewegungswissenschaft, der Robotikforschung und der Klinik, einen wichtigen Einfluss auf die Entwicklung einer gesunden Lebenswelt haben.
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Affiliation(s)
- Nadja Schott
- Institut für Sport und Bewegungswissenschaft, Universität Stuttgart
| | - Julian Rudisch
- Institut für Angewandte Bewegungswissenschaften, Technische Universität Chemnitz
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Allali G, Blumen HM, Devanne H, Pirondini E, Delval A, Van De Ville D. Brain imaging of locomotion in neurological conditions. Neurophysiol Clin 2018; 48:337-359. [PMID: 30487063 PMCID: PMC6563601 DOI: 10.1016/j.neucli.2018.10.004] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 10/05/2018] [Accepted: 10/09/2018] [Indexed: 01/20/2023] Open
Abstract
Impaired locomotion is a frequent and major source of disability in patients with neurological conditions. Different neuroimaging methods have been used to understand the brain substrates of locomotion in various neurological diseases (mainly in Parkinson's disease) during actual walking, and while resting (using mental imagery of gait, or brain-behavior correlation analyses). These studies, using structural (i.e., MRI) or functional (i.e., functional MRI or functional near infra-red spectroscopy) brain imaging, electrophysiology (i.e., EEG), non-invasive brain stimulation (i.e., transcranial magnetic stimulation, or transcranial direct current stimulation) or molecular imaging methods (i.e., PET, or SPECT) reveal extended brain networks involving both grey and white matters in key cortical (i.e., prefrontal cortex) and subcortical (basal ganglia and cerebellum) regions associated with locomotion. However, the specific roles of the various pathophysiological mechanisms encountered in each neurological condition on the phenotype of gait disorders still remains unclear. After reviewing the results of individual brain imaging techniques across the common neurological conditions, such as Parkinson's disease, dementia, stroke, or multiple sclerosis, we will discuss how the development of new imaging techniques and computational analyses that integrate multivariate correlations in "large enough datasets" might help to understand how individual pathophysiological mechanisms express clinically as an abnormal gait. Finally, we will explore how these new analytic methods could drive our rehabilitative strategies.
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Affiliation(s)
- Gilles Allali
- Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland; Department of Neurology, Division of Cognitive and Motor Aging, Albert Einstein College of Medicine, Yeshiva University, Bronx, NY, USA.
| | - Helena M Blumen
- Department of Neurology, Division of Cognitive and Motor Aging, Albert Einstein College of Medicine, Yeshiva University, Bronx, NY, USA; Department of Medicine, Division of Geriatrics, Albert Einstein College of Medicine, Yeshiva University, Bronx, NY, USA
| | - Hervé Devanne
- Department of Clinical Neurophysiology, Lille University Medical Center, Lille, France; EA 7369, URePSSS, Unité de Recherche Pluridisciplinaire Sport Santé Société, Université du Littoral Côte d'Opale, Calais, France
| | - Elvira Pirondini
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland; Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Arnaud Delval
- Department of Clinical Neurophysiology, Lille University Medical Center, Lille, France; Unité Inserm 1171, Faculté de Médecine, Université de Lille, Lille, France
| | - Dimitri Van De Ville
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland; Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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Larivière S, Vos de Wael R, Paquola C, Hong SJ, Mišić B, Bernasconi N, Bernasconi A, Bonilha L, Bernhardt BC. Microstructure-Informed Connectomics: Enriching Large-Scale Descriptions of Healthy and Diseased Brains. Brain Connect 2018; 9:113-127. [PMID: 30079754 DOI: 10.1089/brain.2018.0587] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Rapid advances in neuroimaging and network science have produced powerful tools and measures to appreciate human brain organization at multiple spatial and temporal scales. It is now possible to obtain increasingly meaningful representations of whole-brain structural and functional brain networks and to formally assess macroscale principles of network topology. In addition to its utility in characterizing healthy brain organization, individual variability, and life span-related changes, there is high promise of network neuroscience for the conceptualization and, ultimately, management of brain disorders. In the current review, we argue for a science of the human brain that, while strongly embracing macroscale connectomics, also recommends awareness of brain properties derived from meso- and microscale resolutions. Such features include MRI markers of tissue microstructure, local functional properties, as well as information from nonimaging domains, including cellular, genetic, or chemical data. Integrating these measures with connectome models promises to better define the individual elements that constitute large-scale networks, and clarify the notion of connection strength among them. By enriching the description of large-scale networks, this approach may improve our understanding of fundamental principles of healthy brain organization. Notably, it may also better define the substrate of prevalent brain disorders, including stroke, autism, as well as drug-resistant epilepsies that are each characterized by intriguing interactions between local anomalies and network-level perturbations.
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Affiliation(s)
- Sara Larivière
- 1 Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Reinder Vos de Wael
- 1 Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Casey Paquola
- 1 Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Seok-Jun Hong
- 1 Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.,2 NeuroImaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Bratislav Mišić
- 3 Network Neuroscience Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Neda Bernasconi
- 2 NeuroImaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Andrea Bernasconi
- 2 NeuroImaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Leonardo Bonilha
- 4 Department of Neurosciences, Medical University of South Carolina, Charleston, South Carolina
| | - Boris C Bernhardt
- 1 Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
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41
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Silston B, Bassett DS, Mobbs D. How Dynamic Brain Networks Tune Social Behavior in Real Time. CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE 2018; 27:413-421. [PMID: 31467465 DOI: 10.1177/0963721418773362] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
During social interaction, the brain has the enormous task of interpreting signals that are fleeting, subtle, contextual, abstract, and often ambiguous. Despite the signal complexity, the human brain has evolved to be highly successful in the social landscape. Here, we propose that the human brain makes sense of noisy dynamic signals through accumulation, integration, and prediction, resulting in a coherent representation of the social world. We propose that successful social interaction is critically dependent on a core set of highly connected hubs that dynamically accumulate and integrate complex social information and, in doing so, facilitate social tuning during moment-to-moment social discourse. Successful interactions, therefore, require adaptive flexibility generated by neural circuits composed of highly integrated hubs that coordinate context-appropriate responses. Adaptive properties of the neural substrate, including predictive and adaptive coding, and neural reuse, along with perceptual, inferential, and motivational inputs, provide the ingredients for pliable, hierarchical predictive models that guide our social interactions.
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Affiliation(s)
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania.,Department of Physics and Astronomy, University of Pennsylvania.,Department of Electrical and Systems Engineering, University of Pennsylvania.,Department of Neurology, University of Pennsylvania
| | - Dean Mobbs
- Division of the Humanities and Social Sciences, California Institute of Technology
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42
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Khambhati AN, Sizemore AE, Betzel RF, Bassett DS. Modeling and interpreting mesoscale network dynamics. Neuroimage 2018; 180:337-349. [PMID: 28645844 PMCID: PMC5738302 DOI: 10.1016/j.neuroimage.2017.06.029] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Revised: 06/12/2017] [Accepted: 06/14/2017] [Indexed: 11/28/2022] Open
Abstract
Recent advances in brain imaging techniques, measurement approaches, and storage capacities have provided an unprecedented supply of high temporal resolution neural data. These data present a remarkable opportunity to gain a mechanistic understanding not just of circuit structure, but also of circuit dynamics, and its role in cognition and disease. Such understanding necessitates a description of the raw observations, and a delineation of computational models and mathematical theories that accurately capture fundamental principles behind the observations. Here we review recent advances in a range of modeling approaches that embrace the temporally-evolving interconnected structure of the brain and summarize that structure in a dynamic graph. We describe recent efforts to model dynamic patterns of connectivity, dynamic patterns of activity, and patterns of activity atop connectivity. In the context of these models, we review important considerations in statistical testing, including parametric and non-parametric approaches. Finally, we offer thoughts on careful and accurate interpretation of dynamic graph architecture, and outline important future directions for method development.
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Affiliation(s)
- Ankit N Khambhati
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeautics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ann E Sizemore
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Richard F Betzel
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeautics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
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Zang Z, Geiger LS, Braun U, Cao H, Zangl M, Schäfer A, Moessnang C, Ruf M, Reis J, Schweiger JI, Dixson L, Moscicki A, Schwarz E, Meyer-Lindenberg A, Tost H. Resting-state brain network features associated with short-term skill learning ability in humans and the influence of N-methyl-d-aspartate receptor antagonism. Netw Neurosci 2018; 2:464-480. [PMID: 30320294 PMCID: PMC6175691 DOI: 10.1162/netn_a_00045] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Accepted: 01/11/2018] [Indexed: 01/21/2023] Open
Abstract
Graph theoretical functional magnetic resonance imaging (fMRI) studies have demonstrated that brain networks reorganize significantly during motor skill acquisition, yet the associations between motor learning ability, brain network features, and the underlying biological mechanisms remain unclear. In the current study, we applied a visually guided sequential pinch force learning task and graph theoretical analyses to investigate the associations between short-term motor learning ability and resting-state brain network metrics in 60 healthy subjects. We further probed the test-retest reliability (n = 26) and potential effects of the N-methyl-d-aspartate (NMDA) antagonist ketamine (n = 19) in independent healthy volunteers. Our results show that the improvement of motor performance after short-term training was positively correlated with small-worldness (p = 0.032) and global efficiency (p = 0.025), whereas negatively correlated with characteristic path length (p = 0.014) and transitivity (p = 0.025). In addition, using network-based statistics (NBS), we identified a learning ability–associated (p = 0.037) and ketamine-susceptible (p = 0.027) cerebellar-cortical network with fair to good reliability (intraclass correlation coefficient [ICC] > 0.7) and higher functional connectivity in better learners. Our results provide new evidence for the association of intrinsic brain network features with motor learning and suggest a role of NMDA-related glutamatergic processes in learning-associated subnetworks. Learning a new motor skill prompts immediate reconfigurations of distributed brain networks followed by adaptive changes in intrinsic brain circuits related to synaptic plasticity. Here, we identify global brain network properties and a cerebellar-cortical functional subnetwork that are both significantly associated with motor learning ability in a previously trained visuomotor task in humans. We further show that the associated functional subnetwork connectivity but not the global brain network properties are susceptible to ketamine. Our findings suggest a distinct functional role for learning-related global versus local network metrics and support the idea of a preferential susceptibility of learning-associated subnetworks to N-methyl-d-aspartate antagonist and plasticity-related consolidation effects.
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Affiliation(s)
- Zhenxiang Zang
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Lena S Geiger
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Urs Braun
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Hengyi Cao
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Maria Zangl
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Axel Schäfer
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Carolin Moessnang
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Matthias Ruf
- Department of Neuroimaging, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Janine Reis
- Department of Neurology and Neurophysiology, Albert-Ludwigs-University, Freiburg, Germany
| | - Janina I Schweiger
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Luanna Dixson
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Alexander Moscicki
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Andreas Meyer-Lindenberg
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
| | - Heike Tost
- Department of Psychiatry and Psychotherapy, Research Group System Neuroscience in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Medical Faculty Mannheim, Mannheim, Germany
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Abstract
Network neuroscience strives to understand the networks of the brain on all spatiotemporal scales and levels of observation. Current experimental and theoretical capabilities are beginning to facilitate a more holistic perspective, uniting these networks. This focus feature, "Bridging Scales and Levels," aims to document current research and looks to future progress towards this vision.
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Affiliation(s)
- Emma K Towlson
- Center for Complex Network Research, Northeastern University, Boston, MA 02115, USA
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46
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Resting-State Functional Connectivity Underlying Costly Punishment: A Machine-Learning Approach. Neuroscience 2018; 385:25-37. [DOI: 10.1016/j.neuroscience.2018.05.052] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 05/28/2018] [Accepted: 05/31/2018] [Indexed: 11/23/2022]
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47
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Mattar MG, Wymbs NF, Bock AS, Aguirre GK, Grafton ST, Bassett DS. Predicting future learning from baseline network architecture. Neuroimage 2018; 172:107-117. [PMID: 29366697 PMCID: PMC5910215 DOI: 10.1016/j.neuroimage.2018.01.037] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 01/09/2018] [Accepted: 01/15/2018] [Indexed: 12/24/2022] Open
Abstract
Human behavior and cognition result from a complex pattern of interactions between brain regions. The flexible reconfiguration of these patterns enables behavioral adaptation, such as the acquisition of a new motor skill. Yet, the degree to which these reconfigurations depend on the brain's baseline sensorimotor integration is far from understood. Here, we asked whether spontaneous fluctuations in sensorimotor networks at baseline were predictive of individual differences in future learning. We analyzed functional MRI data from 19 participants prior to six weeks of training on a new motor skill. We found that visual-motor connectivity was inversely related to learning rate: sensorimotor autonomy at baseline corresponded to faster learning in the future. Using three additional scans, we found that visual-motor connectivity at baseline is a relatively stable individual trait. These results suggest that individual differences in motor skill learning can be predicted from sensorimotor autonomy at baseline prior to task execution.
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Affiliation(s)
- Marcelo G Mattar
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Nicholas F Wymbs
- Human Brain Physiology and Stimulation Laboratory, Department of Physical Medicine and Rehabilitation, Johns Hopkins Medical Institution, Baltimore, MD, USA
| | - Andrew S Bock
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Geoffrey K Aguirre
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Scott T Grafton
- Department of Psychological and Brain Sciences and UCSB Brain Imaging Center, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA.
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48
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Yu Q, Du Y, Chen J, Sui J, Adali T, Pearlson G, Calhoun VD. Application of Graph Theory to Assess Static and Dynamic Brain Connectivity: Approaches for Building Brain Graphs. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2018; 106:886-906. [PMID: 30364630 PMCID: PMC6197492 DOI: 10.1109/jproc.2018.2825200] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Human brain connectivity is complex. Graph theory based analysis has become a powerful and popular approach for analyzing brain imaging data, largely because of its potential to quantitatively illuminate the networks, the static architecture in structure and function, the organization of dynamic behavior over time, and disease related brain changes. The first step in creating brain graphs is to define the nodes and edges connecting them. We review a number of approaches for defining brain nodes including fixed versus data-driven nodes. Expanding the narrow view of most studies which focus on static and/or single modality brain connectivity, we also survey advanced approaches and their performances in building dynamic and multi-modal brain graphs. We show results from both simulated and real data from healthy controls and patients with mental illnesse. We outline the advantages and challenges of these various techniques. By summarizing and inspecting recent studies which analyzed brain imaging data based on graph theory, this article provides a guide for developing new powerful tools to explore complex brain networks.
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Affiliation(s)
- Qingbao Yu
- Mind Research Network, Albuquerque NM 87106 USA
| | - Yuhui Du
- Mind Research Network, Albuquerque NM 87106 USA. And also with School of Computer & Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Jiayu Chen
- Mind Research Network, Albuquerque NM 87106 USA
| | - Jing Sui
- University of Chinese Academy of Sciences, Beijing 100049 China. And also with CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Science (CAS), University of CAS, Beijing 100190 China
| | - Tulay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
| | - Godfrey Pearlson
- Olin Neuropsychiatry Research Center, Hartford, CT 06106, USA. And also with Departments of Psychiatry and Neurobiology, Yale University, New Haven, CT 06520, USA
| | - Vince D Calhoun
- Mind Research Network, Albuquerque NM 87106 USA. And also with Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA
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50
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Betzel RF, Bassett DS. Generative models for network neuroscience: prospects and promise. J R Soc Interface 2017; 14:20170623. [PMID: 29187640 PMCID: PMC5721166 DOI: 10.1098/rsif.2017.0623] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Accepted: 11/06/2017] [Indexed: 12/22/2022] Open
Abstract
Network neuroscience is the emerging discipline concerned with investigating the complex patterns of interconnections found in neural systems, and identifying principles with which to understand them. Within this discipline, one particularly powerful approach is network generative modelling, in which wiring rules are algorithmically implemented to produce synthetic network architectures with the same properties as observed in empirical network data. Successful models can highlight the principles by which a network is organized and potentially uncover the mechanisms by which it grows and develops. Here, we review the prospects and promise of generative models for network neuroscience. We begin with a primer on network generative models, with a discussion of compressibility and predictability, and utility in intuiting mechanisms, followed by a short history on their use in network science, broadly. We then discuss generative models in practice and application, paying particular attention to the critical need for cross-validation. Next, we review generative models of biological neural networks, both at the cellular and large-scale level, and across a variety of species including Caenorhabditis elegans, Drosophila, mouse, rat, cat, macaque and human. We offer a careful treatment of a few relevant distinctions, including differences between generative models and null models, sufficiency and redundancy, inferring and claiming mechanism, and functional and structural connectivity. We close with a discussion of future directions, outlining exciting frontiers both in empirical data collection efforts as well as in method and theory development that, together, further the utility of the generative network modelling approach for network neuroscience.
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Affiliation(s)
- Richard F Betzel
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
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