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Singleton SP, Velidi P, Schilling L, Luppi AI, Jamison K, Parkes L, Kuceyeski A. Altered Structural Connectivity and Functional Brain Dynamics in Individuals With Heavy Alcohol Use Elucidated via Network Control Theory. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:1010-1018. [PMID: 38839036 PMCID: PMC11456392 DOI: 10.1016/j.bpsc.2024.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/03/2024] [Accepted: 05/18/2024] [Indexed: 06/07/2024]
Abstract
BACKGROUND Heavy alcohol use and its associated conditions, such as alcohol use disorder, impact millions of individuals worldwide. While our understanding of the neurobiological correlates of alcohol use has evolved substantially, we still lack models that incorporate whole-brain neuroanatomical, functional, and pharmacological information under one framework. METHODS Here, we utilized diffusion and functional magnetic resonance imaging to investigate alterations to brain dynamics in 130 individuals with a high amount of current alcohol use. We compared these alcohol-using individuals to 308 individuals with minimal use of any substances. RESULTS We found that individuals with heavy alcohol use had less dynamic and complex brain activity, and through leveraging network control theory, had increased control energy to complete transitions between activation states. Furthermore, using separately acquired positron emission tomography data, we deployed an in silico evaluation demonstrating that decreased D2 receptor levels, as found previously in individuals with alcohol use disorder, may relate to our observed findings. CONCLUSIONS This work demonstrates that whole-brain, multimodal imaging information can be combined under a network control framework to identify and evaluate neurobiological correlates and mechanisms of heavy alcohol use.
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Affiliation(s)
- S Parker Singleton
- Department of Radiology, Weill Cornell Medicine, New York University, New York, New York.
| | - Puneet Velidi
- Department of Statistics and Data Science, Cornell University, Ithaca, New York
| | - Louisa Schilling
- Department of Radiology, Weill Cornell Medicine, New York University, New York, New York
| | - Andrea I Luppi
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Keith Jamison
- Department of Radiology, Weill Cornell Medicine, New York University, New York, New York
| | - Linden Parkes
- Department of Psychiatry, Rutgers University, Piscataway, New Jersey
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York University, New York, New York
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2
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Parkes L, Kim JZ, Stiso J, Brynildsen JK, Cieslak M, Covitz S, Gur RE, Gur RC, Pasqualetti F, Shinohara RT, Zhou D, Satterthwaite TD, Bassett DS. A network control theory pipeline for studying the dynamics of the structural connectome. Nat Protoc 2024:10.1038/s41596-024-01023-w. [PMID: 39075309 DOI: 10.1038/s41596-024-01023-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 05/16/2024] [Indexed: 07/31/2024]
Abstract
Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms. To date, NCT has been validated to study different aspects of the human structural connectome. NCT outputs can be monitored throughout developmental stages to study the effects of connectome topology on neural dynamics and, separately, to test the coherence of empirical datasets with brain function and stimulation. Here, we provide a comprehensive pipeline for applying NCT to structural connectomes by following two procedures. The main procedure focuses on computing the control energy associated with the transitions between specific neural activity states. The second procedure focuses on computing average controllability, which indexes nodes' general capacity to control the dynamics of the system. We provide recommendations for comparing NCT outputs against null network models, and we further support this approach with a Python-based software package called 'network control theory for python'. The procedures in this protocol are appropriate for users with a background in network neuroscience and experience in dynamical systems theory.
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Affiliation(s)
- Linden Parkes
- Department of Psychiatry, Rutgers University, Piscataway, NJ, USA.
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Jason Z Kim
- Department of Physics, Cornell University, Ithaca, NY, USA
| | - Jennifer Stiso
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Julia K Brynildsen
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel E Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, Riverside, CA, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Philadelphia, PA, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dale Zhou
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S Bassett
- Department of Bioengineering, 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, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
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3
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Yan J, Bai H, Sun Y, Sun X, Hu Z, Liu B, He C, Zhang X. Frontoparietal Response to Working Memory Load Mediates the Association between Sleep Duration and Cognitive Function in Children. Brain Sci 2024; 14:706. [PMID: 39061446 PMCID: PMC11274878 DOI: 10.3390/brainsci14070706] [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: 06/14/2024] [Revised: 07/07/2024] [Accepted: 07/10/2024] [Indexed: 07/28/2024] Open
Abstract
Lack of sleep has been found to be associated with cognitive impairment in children, yet the neural mechanism underlying this relationship remains poorly understood. To address this issue, this study utilized the data from the Adolescent Brain Cognitive Development (ABCD) study (n = 4930, aged 9-10), involving their sleep assessments, cognitive measures, and functional magnetic resonance imaging (fMRI) during an emotional n-back task. Using partial correlations analysis, we found that the out-of-scanner cognitive performance was positively correlated with sleep duration. Additionally, the activation of regions of interest (ROIs) in frontal and parietal cortices for the 2-back versus 0-back contrast was positively correlated with both sleep duration and cognitive performance. Mediation analysis revealed that this activation significantly mediated the relationship between sleep duration and cognitive function at both individual ROI level and network level. After performing analyses separately for different sexes, it was revealed that the mediation effect of the task-related activation was present in girls (n = 2546). These findings suggest that short sleep duration may lead to deficit in cognitive function of children, particularly in girls, through the modulation of frontoparietal activation during working memory load.
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Affiliation(s)
- Jie Yan
- Department of Physiology, Institute of Brain and Intelligence, Third Military Medical University, Chongqing 400038, China
| | - Haolei Bai
- Department of Physiology, Institute of Brain and Intelligence, Third Military Medical University, Chongqing 400038, China
| | - Yuqing Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Xueqi Sun
- Department of Physiology, Institute of Brain and Intelligence, Third Military Medical University, Chongqing 400038, China
| | - Zhian Hu
- Department of Physiology, Institute of Brain and Intelligence, Third Military Medical University, Chongqing 400038, China
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Chao He
- Department of Physiology, Institute of Brain and Intelligence, Third Military Medical University, Chongqing 400038, China
| | - Xiaolong Zhang
- Department of Physiology, Institute of Brain and Intelligence, Third Military Medical University, Chongqing 400038, China
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Stanford W, Mucha PJ, Dayan E. Age-related differences in network controllability are mitigated by redundancy in large-scale brain networks. Commun Biol 2024; 7:701. [PMID: 38849512 PMCID: PMC11161655 DOI: 10.1038/s42003-024-06392-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 05/28/2024] [Indexed: 06/09/2024] Open
Abstract
The aging brain undergoes major changes in its topology. The mechanisms by which the brain mitigates age-associated changes in topology to maintain robust control of brain networks are unknown. Here we use diffusion MRI data from cognitively intact participants (n = 480, ages 40-90) to study age-associated differences in the average controllability of structural brain networks, topological features that could mitigate these differences, and the overall effect on cognitive function. We find age-associated declines in average controllability in control hubs and large-scale networks, particularly within the frontoparietal control and default mode networks. Further, we find that redundancy, a hypothesized mechanism of reserve, quantified via the assessment of multi-step paths within networks, mitigates the effects of topological differences on average network controllability. Lastly, we discover that average network controllability, redundancy, and grey matter volume, each uniquely contribute to predictive models of cognitive function. In sum, our results highlight the importance of redundancy for robust control of brain networks and in cognitive function in healthy-aging.
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Affiliation(s)
- William Stanford
- Biological and Biomedical Sciences Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Peter J Mucha
- Department of Mathematics, Dartmouth College, Hanover, NH, USA
| | - Eran Dayan
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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Pan C, Ma Y, Wang L, Zhang Y, Wang F, Zhang X. From Connectivity to Controllability: Unraveling the Brain Biomarkers of Major Depressive Disorder. Brain Sci 2024; 14:509. [PMID: 38790487 PMCID: PMC11119370 DOI: 10.3390/brainsci14050509] [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: 04/17/2024] [Revised: 05/02/2024] [Accepted: 05/16/2024] [Indexed: 05/26/2024] Open
Abstract
Major Depressive Disorder (MDD) is a significant neurological condition associated with aberrations in brain functional networks. Traditional studies have predominantly analyzed these from a network topology perspective. However, given the brain's dynamic and complex nature, exploring its mechanisms from a network control standpoint provides a fresh and insightful framework. This research investigates the integration of network controllability and machine learning to pinpoint essential biomarkers for MDD using functional magnetic resonance imaging (fMRI) data. By employing network controllability methods, we identify crucial brain regions that are instrumental in facilitating transitions between brain states. These regions demonstrate the brain's ability to navigate various functional states, emphasizing the utility of network controllability metrics as potential biomarkers. Furthermore, these metrics elucidate the complex dynamics of MDD and support the development of precision medicine strategies that incorporate machine learning to improve the precision of diagnostics and the efficacy of treatments. This study underscores the value of merging machine learning with network neuroscience to craft personalized interventions that align with the unique pathological profiles of individuals, ultimately enhancing the management and treatment of MDD.
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Affiliation(s)
- Chunyu Pan
- School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China;
| | - Ying Ma
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 210033, China; (Y.M.); (Y.Z.)
| | - Lifei Wang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210024, China; (L.W.); (F.W.)
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing 210024, China
| | - Yan Zhang
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 210033, China; (Y.M.); (Y.Z.)
| | - Fei Wang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210024, China; (L.W.); (F.W.)
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing 210024, China
- Department of Mental Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Xizhe Zhang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210024, China; (L.W.); (F.W.)
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Yangüez M, Raine L, Chanal J, Bavelier D, Hillman CH. Aerobic fitness and academic achievement: Disentangling the indirect role of executive functions and intelligence. PSYCHOLOGY OF SPORT AND EXERCISE 2024; 70:102514. [PMID: 37683338 DOI: 10.1016/j.psychsport.2023.102514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 06/30/2023] [Accepted: 08/18/2023] [Indexed: 09/10/2023]
Abstract
Research in children points to aerobic fitness as a source of individual differences in academic achievement. By examining the indirect effects of executive functions (EF) and intelligence on the relationship between aerobic fitness and academic achievement, the present study provides novel insight about the cognitive mechanisms underlying this relationship. 218 children (8-10 years) completed the following assessments: (i) a VO2max test to assess aerobic fitness; (ii) four tasks tapping components of EF (i.e., inhibition and cognitive flexibility); (iii) sub-tests of the Kaufman Brief Intelligence Test to assess fluid and crystallized intelligence; and (iv) sub-tests of arithmetic, spelling, and reading achievement (WRAT 3rd edition). Structural equation modeling (SEM) was conducted to examine the indirect role of EF and intelligence on the relationship between aerobic fitness and sub-domains of academic achievement. Covariate analyses included age, pubertal timing, and socio-economic status. Preliminary analysis via linear regression showed a direct effect of aerobic fitness on arithmetic achievement, whereas no effect was observed on spelling and reading achievement. Importantly, multiple mediation SEM revealed the direct effect of aerobic fitness on arithmetic achievement disappeared after accounting for the indirect effects of EF, whereas intelligence did not contribute significantly on this complex mediation process. Moreover, among EF components, cognitive flexibility, was the main driver of the relationship between aerobic fitness and arithmetic achievement. Unpacking which components of EF and intelligence affect the link between aerobic fitness and academic achievement, holds the promise of better understanding the heterogeneity still present in the literature.
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Affiliation(s)
- Marc Yangüez
- Faculty of Psychology, University of Geneva, Switzerland; Campus Biotech, University of Geneva, Switzerland.
| | | | - Julien Chanal
- Faculty of Psychology, University of Geneva, Switzerland; Distance Learning University, Brig, Switzerland.
| | - Daphne Bavelier
- Faculty of Psychology, University of Geneva, Switzerland; Campus Biotech, University of Geneva, Switzerland.
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Manjunatha KKH, Baron G, Benozzo D, Silvestri E, Corbetta M, Chiuso A, Bertoldo A, Suweis S, Allegra M. Controlling target brain regions by optimal selection of input nodes. PLoS Comput Biol 2024; 20:e1011274. [PMID: 38215166 PMCID: PMC10810536 DOI: 10.1371/journal.pcbi.1011274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 01/25/2024] [Accepted: 12/04/2023] [Indexed: 01/14/2024] Open
Abstract
The network control theory framework holds great potential to inform neurostimulation experiments aimed at inducing desired activity states in the brain. However, the current applicability of the framework is limited by inappropriate modeling of brain dynamics, and an overly ambitious focus on whole-brain activity control. In this work, we leverage recent progress in linear modeling of brain dynamics (effective connectivity) and we exploit the concept of target controllability to focus on the control of a single region or a small subnetwork of nodes. We discuss when control may be possible with a reasonably low energy cost and few stimulation loci, and give general predictions on where to stimulate depending on the subset of regions one wishes to control. Importantly, using the robustly asymmetric effective connectome instead of the symmetric structural connectome (as in previous research), we highlight the fundamentally different roles in- and out-hubs have in the control problem, and the relevance of inhibitory connections. The large degree of inter-individual variation in the effective connectome implies that the control problem is best formulated at the individual level, but we discuss to what extent group results may still prove useful.
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Affiliation(s)
- Karan Kabbur Hanumanthappa Manjunatha
- Physics and Astronomy Department “Galileo Galilei”, University of Padova, Padova, Italy
- Modeling and Engineering Risk and Complexity, Scuola Superiore Meridionale, Napoli, Italy
| | - Giorgia Baron
- Information Engineering Department, University of Padova, Padova, Italy
| | - Danilo Benozzo
- Information Engineering Department, University of Padova, Padova, Italy
| | - Erica Silvestri
- Information Engineering Department, University of Padova, Padova, Italy
| | - Maurizio Corbetta
- Neuroscience Department, University of Padova, Padova, Italy
- Venetian Institute of Molecular Medicine (VIMM), Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Alessandro Chiuso
- Information Engineering Department, University of Padova, Padova, Italy
| | - Alessandra Bertoldo
- Information Engineering Department, University of Padova, Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Samir Suweis
- Physics and Astronomy Department “Galileo Galilei”, University of Padova, Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Michele Allegra
- Physics and Astronomy Department “Galileo Galilei”, University of Padova, Padova, Italy
- Padova Neuroscience Center, University of Padova, Padova, Italy
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Zhu L, He A, Chen D, Dong X, Xiong X, Chen A. Cardiorespiratory fitness as a mediator between body fat rate and executive function in college students. Front Endocrinol (Lausanne) 2023; 14:1293388. [PMID: 38174333 PMCID: PMC10764023 DOI: 10.3389/fendo.2023.1293388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 12/04/2023] [Indexed: 01/05/2024] Open
Abstract
Purpose To examine whether body fat rate (BF%) is associated with cardiorespiratory fitness (CRF) and whether cardiorespiratory fitness (CRF) mediates the association between BF% and Executive function (EF) in young adults. Methods In this cross-sectional study, 226 college students were recruited from an university. Flanker, 2-back, and odder and shifting tasks were used to assess EF. The incremental cardiopulmonary exercise tests were performed, and maximal oxygen consumption was recorded during test. The body composition measuring instrument was used to evaluate the participants' BF%. Results The BF% of college students was negatively correlated with each EF, BF% was negatively correlated with CRF, and CRF was negatively correlated with EF (P< 0.001). Structural equation modeling (SEM) and simultaneous analysis of several groups were used to construct mediator model. The CRF of college students plays a partial mediating role between BF% and EF, and the mediating effect accounts for 48.8% of the total effect value. Sex has no moderate effect on the relationship between BF%, CRF, and EF. Conclusions College students with high BF% can improve their CRF by strengthening physical exercise, thereby indirectly improving their EF. Therefore, college students who have a higher body fat percentage should be compensated for engaging in physical exercise in order to enhance their CRF and mitigate the detrimental effects of obesity and overweight on EF.
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Affiliation(s)
- Lina Zhu
- College of Physical Education, Yangzhou University, Yangzhou, Jiangsu, China
| | - Aihong He
- College of Physical Education, Yangzhou Polytechnic College, Yangzhou, Jiangsu, China
| | - Dandan Chen
- College of Physical Education, Yangzhou University, Yangzhou, Jiangsu, China
| | - Xiaoxiao Dong
- College of Physical Education, Yangzhou University, Yangzhou, Jiangsu, China
| | - Xuan Xiong
- College of Physical Education, Yangzhou University, Yangzhou, Jiangsu, China
| | - Aiguo Chen
- College of Physical Education, Yangzhou University, Yangzhou, Jiangsu, China
- Nanjing Sport Institute, Nanjing, Jiangsu, China
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9
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Singleton SP, Velidi P, Schilling L, Luppi AI, Jamison K, Parkes L, Kuceyeski A. Altered structural connectivity and functional brain dynamics in individuals with heavy alcohol use. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.27.568762. [PMID: 38077021 PMCID: PMC10705230 DOI: 10.1101/2023.11.27.568762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2023]
Abstract
Heavy alcohol use and its associated conditions, such as alcohol use disorder (AUD), impact millions of individuals worldwide. While our understanding of the neurobiological correlates of AUD has evolved substantially, we still lack models incorporating whole-brain neuroanatomical, functional, and pharmacological information under one framework. Here, we utilize diffusion and functional magnetic resonance imaging to investigate alterations to brain dynamics in N = 130 individuals with a high amount of current alcohol use. We compared these alcohol using individuals to N = 308 individuals with minimal use of any substances. We find that individuals with heavy alcohol use had less dynamic and complex brain activity, and through leveraging network control theory, had increased control energy to complete transitions between activation states. Further, using separately acquired positron emission tomography (PET) data, we deploy an in silico evaluation demonstrating that decreased D2 receptor levels, as found previously in individuals with AUD, may relate to our observed findings. This work demonstrates that whole-brain, multimodal imaging information can be combined under a network control framework to identify and evaluate neurobiological correlates and mechanisms of AUD.
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Affiliation(s)
- S Parker Singleton
- Department of Radiology, Weill Cornell Medicine, New York, New York, U.S.A
| | - Puneet Velidi
- Department of Statistics and Data Science, Cornell University, Ithaca, New York, U.S.A
| | - Louisa Schilling
- Montreal Neurological Institute, McGill Univeristy, Montreal, CA
| | - Andrea I Luppi
- Department of Radiology, Weill Cornell Medicine, New York, New York, U.S.A
| | - Keith Jamison
- Department of Radiology, Weill Cornell Medicine, New York, New York, U.S.A
| | - Linden Parkes
- Department of Psychiatry, Rutgers University, Piscataway, NJ 08854, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, New York, U.S.A
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10
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Parkes L, Kim JZ, Stiso J, Brynildsen JK, Cieslak M, Covitz S, Gur RE, Gur RC, Pasqualetti F, Shinohara RT, Zhou D, Satterthwaite TD, Bassett DS. Using network control theory to study the dynamics of the structural connectome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.23.554519. [PMID: 37662395 PMCID: PMC10473719 DOI: 10.1101/2023.08.23.554519] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains dynamics. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter dynamics in a desired way. We have extensively developed and validated the application of NCT to the human structural connectome. Through these efforts, we have studied (i) how different aspects of connectome topology affect neural dynamics, (ii) whether NCT outputs cohere with empirical data on brain function and stimulation, and (iii) how NCT outputs vary across development and correlate with behavior and mental health symptoms. In this protocol, we introduce a framework for applying NCT to structural connectomes following two main pathways. Our primary pathway focuses on computing the control energy associated with transitioning between specific neural activity states. Our second pathway focuses on computing average controllability, which indexes nodes' general capacity to control dynamics. We also provide recommendations for comparing NCT outputs against null network models. Finally, we support this protocol with a Python-based software package called network control theory for python (nctpy).
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Affiliation(s)
- Linden Parkes
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Rutgers University, Piscataway, NJ 08854, USA
| | - Jason Z Kim
- Department of Physics, Cornell University, Ithaca, NY 14853, USA
| | - Jennifer Stiso
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
| | | | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, Riverside, CA 92521, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dale Zhou
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, Philadelphia, PA 19104, USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, PA 19104, USA
- Department of Physics and Astronomy, University of Pennsylvania, PA 19104, USA
- Santa Fe Institute, Santa Fe, NM 87501, USA
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11
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Luppi AI, Cabral J, Cofre R, Mediano PAM, Rosas FE, Qureshi AY, Kuceyeski A, Tagliazucchi E, Raimondo F, Deco G, Shine JM, Kringelbach ML, Orio P, Ching S, Sanz Perl Y, Diringer MN, Stevens RD, Sitt JD. Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness. Neuroimage 2023; 275:120162. [PMID: 37196986 PMCID: PMC10262065 DOI: 10.1016/j.neuroimage.2023.120162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 04/16/2023] [Accepted: 05/09/2023] [Indexed: 05/19/2023] Open
Abstract
Disorders of consciousness are complex conditions characterised by persistent loss of responsiveness due to brain injury. They present diagnostic challenges and limited options for treatment, and highlight the urgent need for a more thorough understanding of how human consciousness arises from coordinated neural activity. The increasing availability of multimodal neuroimaging data has given rise to a wide range of clinically- and scientifically-motivated modelling efforts, seeking to improve data-driven stratification of patients, to identify causal mechanisms for patient pathophysiology and loss of consciousness more broadly, and to develop simulations as a means of testing in silico potential treatment avenues to restore consciousness. As a dedicated Working Group of clinicians and neuroscientists of the international Curing Coma Campaign, here we provide our framework and vision to understand the diverse statistical and generative computational modelling approaches that are being employed in this fast-growing field. We identify the gaps that exist between the current state-of-the-art in statistical and biophysical computational modelling in human neuroscience, and the aspirational goal of a mature field of modelling disorders of consciousness; which might drive improved treatments and outcomes in the clinic. Finally, we make several recommendations for how the field as a whole can work together to address these challenges.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia and Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
| | - Joana Cabral
- Life and Health Sciences Research Institute, University of Minho, Portugal
| | - Rodrigo Cofre
- CIMFAV-Ingemat, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso, Chile; Centre National de la Recherche Scientifique (CNRS), Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Gif-sur-Yvette, France
| | - Pedro A M Mediano
- Department of Computing, Imperial College London, London, UK; Department of Psychology, University of Cambridge, Cambridge, UK
| | - Fernando E Rosas
- Department of Informatics, University of Sussex, Brighton, UK; Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK; Centre for Complexity Science, Imperial College London, London, UK; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
| | - Abid Y Qureshi
- University of Kansas Medical Center, Kansas City, MO, USA
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, USA
| | - Enzo Tagliazucchi
- Departamento de Física (UBA) e Instituto de Fisica de Buenos Aires (CONICET), Buenos Aires, Argentina; Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Federico Raimondo
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Germany; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Gustavo Deco
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain; Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
| | - James M Shine
- Brain and Mind Center, The University of Sydney, Sydney, Australia
| | - Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK; Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Patricio Orio
- Centro Interdisciplinario de Neurociencia de Valparaíso and Instituto de Neurociencia, Universidad de Valparaíso, Valparaíso, Chile
| | - ShiNung Ching
- Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Yonatan Sanz Perl
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Institut du Cerveau et de la Moelle épinière - Paris Brain Institute, ICM, Paris, France; National Scientific and Technical Research Council (CONICET), Godoy Cruz, CABA 2290, Argentina
| | - Michael N Diringer
- Department of Neurology and Neurosurgery, Washington University in St. Louis, St. Louis, MO, USA
| | - Robert D Stevens
- Departments of Anesthesiology and Critical Care Medicine, Neurology, and Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jacobo Diego Sitt
- Institut du Cerveau et de la Moelle épinière - Paris Brain Institute, ICM, Paris, France; Sorbonne Université, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France.
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12
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Wilmskoetter J, Busby N, He X, Caciagli L, Roth R, Kristinsson S, Davis KA, Rorden C, Bassett DS, Fridriksson J, Bonilha L. Dynamic network properties of the superior temporal gyrus mediate the impact of brain age gap on chronic aphasia severity. Commun Biol 2023; 6:727. [PMID: 37452209 PMCID: PMC10349039 DOI: 10.1038/s42003-023-05119-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 07/07/2023] [Indexed: 07/18/2023] Open
Abstract
Brain structure deteriorates with aging and predisposes an individual to more severe language impairments (aphasia) after a stroke. However, the underlying mechanisms of this relation are not well understood. Here we use an approach to model brain network properties outside the stroke lesion, network controllability, to investigate relations among individualized structural brain connections, brain age, and aphasia severity in 93 participants with chronic post-stroke aphasia. Controlling for the stroke lesion size, we observe that lower average controllability of the posterior superior temporal gyrus (STG) mediates the relation between advanced brain aging and aphasia severity. Lower controllability of the left posterior STG signifies that activity in the left posterior STG is less likely to yield a response in other brain regions due to the topological properties of the structural brain networks. These results indicate that advanced brain aging among individuals with post-stroke aphasia is associated with disruption of dynamic properties of a critical language-related area, the STG, which contributes to worse aphasic symptoms. Because brain aging is variable among individuals with aphasia, our results provide further insight into the mechanisms underlying the variance in clinical trajectories in post-stroke aphasia.
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Affiliation(s)
- Janina Wilmskoetter
- Department of Health and Rehabilitation Sciences, College of Health Professions, Medical University of South Carolina, Charleston, SC, USA.
| | - Natalie Busby
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA
| | - Xiaosong He
- Department of Psychology, University of Science and Technology of China, Beijing, China
| | - Lorenzo Caciagli
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Rebecca Roth
- Department of Neurology, Emory University, Atlanta, GA, USA
| | - Sigfus Kristinsson
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA
| | - Kathryn A Davis
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chris Rorden
- Department of Psychology, University of South Carolina, Columbia, SC, USA
| | - Dani S Bassett
- Department of Bioengineering, School of Engineering & Applied Science, 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 & Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics & Astronomy, School of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, New Mexico, NM, USA
| | - Julius Fridriksson
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA
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13
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Singleton SP, Timmermann C, Luppi AI, Eckernäs E, Roseman L, Carhart-Harris RL, Kuceyeski A. Time-resolved network control analysis links reduced control energy under DMT with the serotonin 2a receptor, signal diversity, and subjective experience. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.11.540409. [PMID: 37214949 PMCID: PMC10197635 DOI: 10.1101/2023.05.11.540409] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Psychedelics offer a profound window into the functioning of the human brain and mind through their robust acute effects on perception, subjective experience, and brain activity patterns. In recent work using a receptor-informed network control theory framework, we demonstrated that the serotonergic psychedelics lysergic acid diethylamide (LSD) and psilocybin flatten the brain's control energy landscape in a manner that covaries with more dynamic and entropic brain activity. Contrary to LSD and psilocybin, whose effects last for hours, the serotonergic psychedelic N,N-dimethyltryptamine (DMT) rapidly induces a profoundly immersive altered state of consciousness lasting less than 20 minutes, allowing for the entirety of the drug experience to be captured during a single resting-state fMRI scan. Using network control theory, which quantifies the amount of input necessary to drive transitions between functional brain states, we integrate brain structure and function to map the energy trajectories of 14 individuals undergoing fMRI during DMT and placebo. Consistent with previous work, we find that global control energy is reduced following injection with DMT compared to placebo. We additionally show longitudinal trajectories of global control energy correlate with longitudinal trajectories of EEG signal diversity (a measure of entropy) and subjective ratings of drug intensity. We interrogate these same relationships on a regional level and find that the spatial patterns of DMT's effects on these metrics are correlated with serotonin 2a receptor density (obtained from separately acquired PET data). Using receptor distribution and pharmacokinetic information, we were able to successfully recapitulate the effects of DMT on global control energy trajectories, demonstrating a proof-of-concept for the use of control models in predicting pharmacological intervention effects on brain dynamics.
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Affiliation(s)
| | - Christopher Timmermann
- Center for Psychedelic Research, Department of Brain Science, Imperial College London, London, United Kingdom
| | | | - Emma Eckernäs
- Unit for Pharmacokinetics and Drug Metabolism, Department of Pharmacology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Leor Roseman
- Center for Psychedelic Research, Department of Brain Science, Imperial College London, London, United Kingdom
| | - Robin L. Carhart-Harris
- Center for Psychedelic Research, Department of Brain Science, Imperial College London, London, United Kingdom
- Psychedelics Division, Neuroscape, University of California San Francisco, USA
| | - Amy Kuceyeski
- Department of Computational Biology, Cornell University, Ithaca, USA
- Department of Radiology, Weill Cornell Medicine, New York, USA
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14
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Luppi AI, Mediano PAM, Rosas FE, Allanson J, Pickard JD, Williams GB, Craig MM, Finoia P, Peattie ARD, Coppola P, Menon DK, Bor D, Stamatakis EA. Reduced emergent character of neural dynamics in patients with a disrupted connectome. Neuroimage 2023; 269:119926. [PMID: 36740030 PMCID: PMC9989666 DOI: 10.1016/j.neuroimage.2023.119926] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 01/23/2023] [Accepted: 02/02/2023] [Indexed: 02/05/2023] Open
Abstract
High-level brain functions are widely believed to emerge from the orchestrated activity of multiple neural systems. However, lacking a formal definition and practical quantification of emergence for experimental data, neuroscientists have been unable to empirically test this long-standing conjecture. Here we investigate this fundamental question by leveraging a recently proposed framework known as "Integrated Information Decomposition," which establishes a principled information-theoretic approach to operationalise and quantify emergence in dynamical systems - including the human brain. By analysing functional MRI data, our results show that the emergent and hierarchical character of neural dynamics is significantly diminished in chronically unresponsive patients suffering from severe brain injury. At a functional level, we demonstrate that emergence capacity is positively correlated with the extent of hierarchical organisation in brain activity. Furthermore, by combining computational approaches from network control theory and whole-brain biophysical modelling, we show that the reduced capacity for emergent and hierarchical dynamics in severely brain-injured patients can be mechanistically explained by disruptions in the patients' structural connectome. Overall, our results suggest that chronic unresponsiveness resulting from severe brain injury may be related to structural impairment of the fundamental neural infrastructures required for brain dynamics to support emergence.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Leverhulme Centre for the Future of Intelligence, Cambridge, UK; The Alan Turing Institute, London, UK.
| | - Pedro A M Mediano
- Department of Computing, Imperial College London, London, UK; Department of Psychology, University of Cambridge, Cambridge, UK
| | - Fernando E Rosas
- Department of Brain Science, Center for Psychedelic Research, Imperial College London, London, UK; Data Science Institute, Imperial College London, London, UK; Centre for Complexity Science, Imperial College London, London, UK; Center for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK; Department of Informatics, University of Sussex, Brighton, UK
| | - Judith Allanson
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Department of Neurosciences, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation, Cambridge, UK
| | - John D Pickard
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Guy B Williams
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Michael M Craig
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Paola Finoia
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Alexander R D Peattie
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Peter Coppola
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - David K Menon
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Daniel Bor
- Department of Psychology, University of Cambridge, Cambridge, UK; Department of Psychology, Queen Mary University of London, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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15
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Hahn T, Winter NR, Ernsting J, Gruber M, Mauritz MJ, Fisch L, Leenings R, Sarink K, Blanke J, Holstein V, Emden D, Beisemann M, Opel N, Grotegerd D, Meinert S, Heindel W, Witt S, Rietschel M, Nöthen MM, Forstner AJ, Kircher T, Nenadic I, Jansen A, Müller-Myhsok B, Andlauer TFM, Walter M, van den Heuvel MP, Jamalabadi H, Dannlowski U, Repple J. Genetic, individual, and familial risk correlates of brain network controllability in major depressive disorder. Mol Psychiatry 2023; 28:1057-1063. [PMID: 36639510 PMCID: PMC10005934 DOI: 10.1038/s41380-022-01936-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 12/14/2022] [Accepted: 12/16/2022] [Indexed: 01/15/2023]
Abstract
Many therapeutic interventions in psychiatry can be viewed as attempts to influence the brain's large-scale, dynamic network state transitions. Building on connectome-based graph analysis and control theory, Network Control Theory is emerging as a powerful tool to quantify network controllability-i.e., the influence of one brain region over others regarding dynamic network state transitions. If and how network controllability is related to mental health remains elusive. Here, from Diffusion Tensor Imaging data, we inferred structural connectivity and inferred calculated network controllability parameters to investigate their association with genetic and familial risk in patients diagnosed with major depressive disorder (MDD, n = 692) and healthy controls (n = 820). First, we establish that controllability measures differ between healthy controls and MDD patients while not varying with current symptom severity or remission status. Second, we show that controllability in MDD patients is associated with polygenic scores for MDD and psychiatric cross-disorder risk. Finally, we provide evidence that controllability varies with familial risk of MDD and bipolar disorder as well as with body mass index. In summary, we show that network controllability is related to genetic, individual, and familial risk in MDD patients. We discuss how these insights into individual variation of network controllability may inform mechanistic models of treatment response prediction and personalized intervention-design in mental health.
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Affiliation(s)
- Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.
| | - Nils R Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jan Ernsting
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.,Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Marco J Mauritz
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Lukas Fisch
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Ramona Leenings
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.,Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany
| | - Kelvin Sarink
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Julian Blanke
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Vincent Holstein
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Daniel Emden
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Marie Beisemann
- Department of Statistics, TU Dortmund University, Dortmund, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.,Interdisciplinary Centre for Clinical Research IZKF, University of Münster, Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.,Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Walter Heindel
- Institute of Clinical Radiology, University of Münster, Münster, Germany
| | - Stephanie Witt
- Department of Genetic Epidemiology, Central Institute of Mental Health, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology, Central Institute of Mental Health, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Andreas J Forstner
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Phillips University Marburg, Marburg, Germany
| | - Igor Nenadic
- Department of Psychiatry and Psychotherapy, Phillips University Marburg, Marburg, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, Phillips University Marburg, Marburg, Germany.,Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
| | | | - Till F M Andlauer
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands.,Department of Child Psychiatry, Amsterdam University Medical Center, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, Phillips University Marburg, Marburg, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.,Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
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16
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Li D, Mao M, Zhang X, Hou D, Zhang S, Hao J, Cui X, Niu Y, Xiang J, Wang B. Gender effects on the controllability of hemispheric white matter networks. Cereb Cortex 2023; 33:1643-1658. [PMID: 35483707 DOI: 10.1093/cercor/bhac162] [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: 12/31/2021] [Revised: 04/03/2022] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
Male and female adults exhibited significant group differences in brain white matter (WM) asymmetry and WM network controllability. However, gender differences in controllability of hemispheric WM networks between males and females remain to be determined. Based on 1 principal atlas and 1 replication atlas, this work characterized the average controllability (AC) and modal controllability (MC) of hemispheric WM network based on 1 principal dataset and 2 replication datasets. All results showed that males had higher AC of left hemispheric networks than females. And significant hemispheric asymmetry was revealed in regional AC and MC. Furthermore, significant gender differences in the AC asymmetry were mainly found in regions lie in the frontoparietal network, and the MC asymmetry was found in regions involving auditory and emotion process. Finally, we found significant associations between regional controllability and cognitive features. Taken together, this work could provide a novel perspective for understanding gender differences in hemispheric WM asymmetry and cognitive function between males and females.
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Affiliation(s)
- Dandan Li
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Min Mao
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Xi Zhang
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Dianni Hou
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Shanshan Zhang
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Jiangping Hao
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Xiaohong Cui
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Yan Niu
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
| | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, Shanxi, 030024, China
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17
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Tang B, Zhang W, Liu J, Deng S, Hu N, Li S, Zhao Y, Liu N, Zeng J, Cao H, Sweeney JA, Gong Q, Gu S, Lui S. Altered controllability of white matter networks and related brain function changes in first-episode drug-naive schizophrenia. Cereb Cortex 2023; 33:1527-1535. [PMID: 36790361 DOI: 10.1093/cercor/bhac421] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/25/2022] [Accepted: 09/26/2022] [Indexed: 11/06/2022] Open
Abstract
Understanding how structural connectivity alterations affect aberrant dynamic function using network control theory will provide new mechanistic insights into the pathophysiology of schizophrenia. The study included 140 drug-naive schizophrenia patients and 119 healthy controls (HCs). The average controllability (AC) quantifying capacity of brain regions/networks to shift the system into easy-to-reach states was calculated based on white matter connectivity and was compared between patients and HCs as well as functional network topological and dynamic properties. The correlation analysis between AC and duration of untreated psychosis (DUP) were conducted to characterize the controllability progression pattern without treatment effects. Relative to HCs, patients exhibited reduced AC in multiple nodes, mainly distributed in default mode network (DMN), visual network (VN), and subcortical regions, and increased AC in somatomotor network. These networks also had impaired functional topology and increased temporal variability in dynamic functional connectivity analysis. Longer DUP was related to greater reductions of AC in VN and DMN. The current study highlighted potential structural substrates underlying altered functional dynamics in schizophrenia, providing a novel understanding of the relationship of anatomic and functional network alterations.
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Affiliation(s)
- Biqiu Tang
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Wenjing Zhang
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Jiang Liu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, No. 2006 Xiyuan Avenue, Chengdu 611731, China
| | - Shikuang Deng
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, No. 2006 Xiyuan Avenue, Chengdu 611731, China
| | - Na Hu
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Siyi Li
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Youjin Zhao
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Nian Liu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, No 1 Maoyuan South Road, Shunqing District, Nanchong 637000, China
| | - Jiaxin Zeng
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Hengyi Cao
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, 350 Community Drive, Manhasset, NY 11030, United States.,Division of Psychiatry Research, Zucker Hillside Hospital, 75-59 263rd Street, Glen Oaks, NY 11004, United States
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, 260 Stetson Street, Cincinnati, OH 45219, United States
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Shi Gu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, No. 2006 Xiyuan Avenue, Chengdu 611731, China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
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18
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Hahn T, Jamalabadi H, Nozari E, Winter NR, Ernsting J, Gruber M, Mauritz MJ, Grumbach P, Fisch L, Leenings R, Sarink K, Blanke J, Vennekate LK, Emden D, Opel N, Grotegerd D, Enneking V, Meinert S, Borgers T, Klug M, Leehr EJ, Dohm K, Heindel W, Gross J, Dannlowski U, Redlich R, Repple J. Towards a network control theory of electroconvulsive therapy response. PNAS NEXUS 2023; 2:pgad032. [PMID: 36874281 PMCID: PMC9982063 DOI: 10.1093/pnasnexus/pgad032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 12/09/2022] [Accepted: 01/17/2023] [Indexed: 02/04/2023]
Abstract
Electroconvulsive Therapy (ECT) is arguably the most effective intervention for treatment-resistant depression. While large interindividual variability exists, a theory capable of explaining individual response to ECT remains elusive. To address this, we posit a quantitative, mechanistic framework of ECT response based on Network Control Theory (NCT). Then, we empirically test our approach and employ it to predict ECT treatment response. To this end, we derive a formal association between Postictal Suppression Index (PSI)-an ECT seizure quality index-and whole-brain modal and average controllability, NCT metrics based on white-matter brain network architecture, respectively. Exploiting the known association of ECT response and PSI, we then hypothesized an association between our controllability metrics and ECT response mediated by PSI. We formally tested this conjecture in N = 50 depressive patients undergoing ECT. We show that whole-brain controllability metrics based on pre-ECT structural connectome data predict ECT response in accordance with our hypotheses. In addition, we show the expected mediation effects via PSI. Importantly, our theoretically motivated metrics are at least on par with extensive machine learning models based on pre-ECT connectome data. In summary, we derived and tested a control-theoretic framework capable of predicting ECT response based on individual brain network architecture. It makes testable, quantitative predictions regarding individual therapeutic response, which are corroborated by strong empirical evidence. Our work might constitute a starting point for a comprehensive, quantitative theory of personalized ECT interventions rooted in control theory.
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Affiliation(s)
- Tim Hahn
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, University of Tübingen, 72076 Tübingen, Germany
| | - Erfan Nozari
- Department of Mechanical Engineering, University of California, 92521 Riverside, USA
| | - Nils R Winter
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Jan Ernsting
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany.,Faculty of Mathematics and Computer Science, University of Münster, 48149 Münster, Germany
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Marco J Mauritz
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Pascal Grumbach
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Lukas Fisch
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Ramona Leenings
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany.,Faculty of Mathematics and Computer Science, University of Münster, 48149 Münster, Germany
| | - Kelvin Sarink
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Julian Blanke
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Leon Kleine Vennekate
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Daniel Emden
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Verena Enneking
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany.,Institute for Translational Neuroscience, University of Münster, 48149 Münster, Germany
| | - Tiana Borgers
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Melissa Klug
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Elisabeth J Leehr
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Katharina Dohm
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Walter Heindel
- Institute of Clinical Radiology, University of Münster, 48149 Münster, Germany
| | - Joachim Gross
- Institute for Biomagnetism and Biosignalanalysis, University Hospital Münster, 48149 Münster, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Ronny Redlich
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany.,Department of Psychology, University of Halle, 06099 Halle (Saale), Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, 48149 Münster, Germany
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19
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Zhou D, Kang Y, Cosme D, Jovanova M, He X, Mahadevan A, Ahn J, Stanoi O, Brynildsen JK, Cooper N, Cornblath EJ, Parkes L, Mucha PJ, Ochsner KN, Lydon-Staley DM, Falk EB, Bassett DS. Mindful attention promotes control of brain network dynamics for self-regulation and discontinues the past from the present. Proc Natl Acad Sci U S A 2023; 120:e2201074119. [PMID: 36595675 PMCID: PMC9926276 DOI: 10.1073/pnas.2201074119] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 10/17/2022] [Indexed: 01/05/2023] Open
Abstract
Mindful attention is characterized by acknowledging the present experience as a transient mental event. Early stages of mindfulness practice may require greater neural effort for later efficiency. Early effort may self-regulate behavior and focalize the present, but this understanding lacks a computational explanation. Here we used network control theory as a model of how external control inputs-operationalizing effort-distribute changes in neural activity evoked during mindful attention across the white matter network. We hypothesized that individuals with greater network controllability, thereby efficiently distributing control inputs, effectively self-regulate behavior. We further hypothesized that brain regions that utilize greater control input exhibit shorter intrinsic timescales of neural activity. Shorter timescales characterize quickly discontinuing past processing to focalize the present. We tested these hypotheses in a randomized controlled study that primed participants to either mindfully respond or naturally react to alcohol cues during fMRI and administered text reminders and measurements of alcohol consumption during 4 wk postscan. We found that participants with greater network controllability moderated alcohol consumption. Mindful regulation of alcohol cues, compared to one's own natural reactions, reduced craving, but craving did not differ from the baseline group. Mindful regulation of alcohol cues, compared to the natural reactions of the baseline group, involved more-effortful control of neural dynamics across cognitive control and attention subnetworks. This effort persisted in the natural reactions of the mindful group compared to the baseline group. More-effortful neural states had shorter timescales than less effortful states, offering an explanation for how mindful attention promotes being present.
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Affiliation(s)
- Dale Zhou
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Yoona Kang
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA 19104
| | - Danielle Cosme
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA 19104
| | - Mia Jovanova
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA 19104
| | - Xiaosong He
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychology, School of Humanities and Social Sciences, University of Science and Technology of China, 230026 Hefei, People’s Republic of China
| | - Arun Mahadevan
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104
| | - Jeesung Ahn
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104
| | - Ovidia Stanoi
- Department of Psychology, Columbia University, New York, NY 19104
| | - Julia K. Brynildsen
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104
| | - Nicole Cooper
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA 19104
| | - Eli J. Cornblath
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104
| | - Linden Parkes
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104
| | - Peter J. Mucha
- Department of Mathematics, Dartmouth College, Hanover, NH 03755
| | - Kevin N. Ochsner
- Department of Psychology, Columbia University, New York, NY 19104
| | - David M. Lydon-Staley
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA 19104
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA 19104
| | - Emily B. Falk
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104
- Marketing Department, Wharton School, University of Pennsylvania, Philadelphia, PA 19104
| | - Dani S. Bassett
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104
- Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
- Santa Fe Institute, Santa Fe, NM 87501
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20
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Efrat B, Orna T. Sex differences in the sustained attention of elementary school children. BMC Psychol 2022; 10:307. [PMID: 36522790 PMCID: PMC9753246 DOI: 10.1186/s40359-022-01007-z] [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: 05/19/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The study investigates sex differences in sustained attention among children. METHODS Forty-five children (23 girls) from Grades 2-5 (mean age of 7.47 ± 0.73 years) wore an actigraph for a continuous five to seven days including school and non-school days. Sustained attention using the psychomotor vigilance test (PVT) was measured twice a day on two school days and on one non-school day. RESULTS No sex differences were found for sleep patterns. However, sex differences in PVT performance were documented. While boys were faster (shorter reaction time) and showed fewer lapses than girls, they showed higher number of false starts than girls, on both weekdays and weekends. CONCLUSIONS The findings suggest that sex differences should been taken into account in studies investigating neurobehavioral functioning, particularly, sustained attention across various age groups.
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Affiliation(s)
- Barel Efrat
- grid.454270.00000 0001 2150 0053Department of Behavioral Sciences and the Center for Psychobiological Research, The Max Stern Academic College of Emek Yezreel, Emek Yezreel, Israel
| | - Tzischinsky Orna
- grid.454270.00000 0001 2150 0053Department of Behavioral Sciences and the Center for Psychobiological Research, The Max Stern Academic College of Emek Yezreel, Emek Yezreel, Israel
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21
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Tang R, Elman JA, Franz CE, Dale AM, Eyler LT, Fennema-Notestine C, Hagler DJ, Lyons MJ, Panizzon MS, Puckett OK, Kremen WS. Longitudinal association of executive function and structural network controllability in the aging brain. GeroScience 2022; 45:837-849. [PMID: 36269506 PMCID: PMC9886719 DOI: 10.1007/s11357-022-00676-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 10/12/2022] [Indexed: 02/03/2023] Open
Abstract
Executive function encompasses effortful cognitive processes that are particularly susceptible to aging. Functional brain networks supporting executive function-such as the frontoparietal control network and the multiple demand system-have been extensively investigated. However, it remains unclear how structural networks facilitate and constrain the dynamics of functional networks to contribute to aging-related executive function declines. We examined whether changes in structural network modal controllability-a network's ability to facilitate effortful brain state transitions that support cognitive functions-are associated with changes in executive function cross-sectionally and longitudinally. Diffusion-weighted imaging and neuropsychological testing were conducted at two time points (Time 1: ages 56 to 66, N = 172; Time 2: ages 61 to 70, N = 267) in community-dwelling men from the Vietnam Era Twin Study of Aging. An executive function factor score was computed from six neuropsychological tasks. Structural networks constructed from white matter connectivity were used to estimate modal controllability in control network and multiple demand system. We showed that higher modal controllability in control network and multiple demand system was associated with better executive function at Time 2, after controlling for age, young adult general cognitive ability, and physical health status. Moreover, changes in executive function over a period of 5 to 6 years (Time 1-Time 2, N = 105) were associated with changes in modal controllability of the multiple demand system and weakly in the control network over the same time period. These findings suggest that changes in the ability of structural brain networks in facilitating effortful brain state transitions may be a key neural mechanism underlying aging-related executive function declines and cognitive aging.
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Affiliation(s)
- Rongxiang Tang
- Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA. .,Center for Behavior Genetics of Aging, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
| | - Jeremy A. Elman
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093 USA ,Center for Behavior Genetics of Aging, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093 USA
| | - Carol E. Franz
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093 USA ,Center for Behavior Genetics of Aging, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093 USA
| | - Anders M. Dale
- Department of Radiology, University of California San Diego, La Jolla, CA 92093 USA ,Department of Neurosciences, University of California San Diego, La Jolla, CA 92093 USA
| | - Lisa T. Eyler
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093 USA ,Desert Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare System, San Diego, CA 92093 USA
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093 USA ,Department of Radiology, University of California San Diego, La Jolla, CA 92093 USA
| | - Donald J. Hagler
- Department of Radiology, University of California San Diego, La Jolla, CA 92093 USA
| | - Michael J. Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02212 USA
| | - Matthew S. Panizzon
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093 USA ,Center for Behavior Genetics of Aging, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093 USA
| | - Olivia K. Puckett
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093 USA ,Center for Behavior Genetics of Aging, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093 USA
| | - William S. Kremen
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093 USA ,Center for Behavior Genetics of Aging, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093 USA
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22
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Fernández‐Sánchez A, Redondo‐Tébar A, Sánchez‐López M, Visier‐Alfonso ME, Muñoz‐Rodríguez JR, Martínez‐Vizcaíno V. Sex differences on the relation among gross motor competence, cognition, and academic achievement in children. Scand J Psychol 2022; 63:504-512. [PMID: 35614556 PMCID: PMC9790688 DOI: 10.1111/sjop.12827] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 04/11/2022] [Accepted: 04/15/2022] [Indexed: 12/30/2022]
Abstract
An association between gross motor competence (GMC) and academic achievement (AA) has been described, but the potential mechanisms behind this association are still unknown. It is not known either whether these mechanisms are similar for boys and girls. The aim of this study was to analyse whether the association between GMC and AA is mediated by executive functions (EFs), and to investigate whether this mediation differs by sex. This cross-sectional study involved 451 children aged 8 to 10 (234 girls; mean age 9.95 ± 0.59). The Movement Assessment Battery for Children-Second Edition (MABC-2), NIH Toolbox, and grades in language and mathematics were used to test GMC, EFs, and AA, respectively. Multifactorial structural equation model (SEM) was used to evaluate a possible relation between variables, controlling for confounders. The differences by sex were examined using a multi-group SEM approach. The results showed that EFs acted as a full mediator of the relationship between GMC and AA in boys (β = 0.14, p = 0.012) but not in girls (β = 0.10, p = 0.326). These results show that the benefit of GMC on AA is mediated by EFs in boys but not in girls. Nevertheless, these conclusions should be carefully considered due to the cross-sectional nature of the study.
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Affiliation(s)
- Antonio Fernández‐Sánchez
- Social and Health Research CenterUniversidad de Castilla‐La ManchaCuencaSpain,Faculty of EducationUniversidad de Castilla‐La ManchaCiudad RealSpain
| | | | - Mairena Sánchez‐López
- Social and Health Research CenterUniversidad de Castilla‐La ManchaCuencaSpain,Faculty of EducationUniversidad de Castilla‐La ManchaCiudad RealSpain
| | - María Eugenia Visier‐Alfonso
- Social and Health Research CenterUniversidad de Castilla‐La ManchaCuencaSpain,Faculty of NursingUniversidad de Castilla‐La ManchaCuencaSpain
| | - José Ramón Muñoz‐Rodríguez
- Translational Research UnitUniversity General Hospital of Ciudad Real, Servicio de Salud de Castilla‐La Mancha (SESCAM)Ciudad RealSpain,Faculty of MedicineUniversidad de Castilla La ManchaCiudad RealSpain
| | - Vicente Martínez‐Vizcaíno
- Social and Health Research CenterUniversidad de Castilla‐La ManchaCuencaSpain,Facultad de Ciencias de la SaludUniversidad Autónoma de ChileTalcaChile
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23
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Vilca LW. The moderating role of sex in the relationship between executive functions and academic procrastination in undergraduate students. Front Psychol 2022; 13:928425. [PMID: 36072020 PMCID: PMC9444057 DOI: 10.3389/fpsyg.2022.928425] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 07/29/2022] [Indexed: 12/04/2022] Open
Abstract
The objective of the study was to determine if sex plays a moderating role in the relationship between executive functions and academic procrastination in 106 university students of both genders (28.3% male and 71.7% female) between the ages of 18 and 30 years (M = 19.7; SD = 2.7). The Academic Procrastination Scale and the Neuropsychological Battery of Executive Functions and Frontal Lobes (BANFE-2) were used to measure the variables. The results of the study showed that the degree of prediction of the tasks linked to the orbitomedial cortex (involves the orbitofrontal cortex [OFC] and the medial prefrontal cortex [mPFC]) on academic procrastination is significantly moderated by the sex of the university students (β3 = 0.53; p < 0.01). For men, the estimated effect of the tasks linked to the orbitomedial cortex on the degree of academic procrastination is −0.81. For women, the estimated effect of the tasks linked to the orbitomedial cortex on the degree of academic procrastination is −0.28. In addition, it was shown that sex does not play a moderating role in the relationship between the tasks linked to the dorsolateral prefrontal cortex (dlPFC) and academic procrastination (β3 = 0.12; p > 0.05). It was also determined that sex does not play a moderating role in the relationship between the tasks linked to the anterior prefrontal cortex (aPFC) and academic procrastination (β3 = 0.05; p > 0.05). It is concluded that only the executive functions associated with the orbitomedial cortex are moderated by the sex of the university students, where the impact of the tasks linked to the orbitomedial cortex on academic procrastination in men is significantly greater than in women.
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24
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Wang B, Zhang S, Yu X, Niu Y, Niu J, Li D, Zhang S, Xiang J, Yan T, Yang J, Wu J, Liu M. Alterations in white matter network dynamics in patients with schizophrenia and bipolar disorder. Hum Brain Mapp 2022; 43:3909-3922. [PMID: 35567336 PMCID: PMC9374889 DOI: 10.1002/hbm.25892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/17/2022] [Accepted: 04/08/2022] [Indexed: 11/12/2022] Open
Abstract
Emerging evidence suggests white matter network abnormalities in patients with schizophrenia (SZ) and bipolar disorder (BD), but the alterations in dynamics of the white matter network in patients with SZ and BD are largely unknown. The white matter network of patients with SZ (n = 45) and BD (n = 47) and that of healthy controls (HC, n = 105) were constructed. We used dynamics network control theory to quantify the dynamics metrics of the network, including controllability and synchronizability, to measure the ability to transfer between different states. Experiments show that the patients with SZ and BD showed decreasing modal controllability and synchronizability and increasing average controllability. The correlations between the average controllability and synchronizability of patients were broken, especially for those with SZ. The patients also showed alterations in brain regions with supercontroller roles and their distribution in the cognitive system. Finally, we were able to accurately discriminate and predict patients with SZ and BD. Our findings provide novel dynamic metrics evidence that patients with SZ and BD are characterized by a selective disruption of brain network controllability, potentially leading to reduced brain state transfer capacity, and offer new guidance for the clinical diagnosis of mental illness.
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Affiliation(s)
- Bin Wang
- Department of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Shanshan Zhang
- Department of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Xuexue Yu
- Department of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Yan Niu
- Department of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jinliang Niu
- Department of Medical Imaging, The Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Dandan Li
- Department of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Shan Zhang
- Department of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- Department of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Ting Yan
- Teranslational Medicine Research Center, Shanxi Medical University, Taiyuan, China
| | - Jiajia Yang
- Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama, Japan
| | - Jinglong Wu
- Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama, Japan
| | - Miaomiao Liu
- School of Psychology, Shenzhen University, Shenzhen, China
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25
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Deng S, Li J, Thomas Yeo BT, Gu S. Control theory illustrates the energy efficiency in the dynamic reconfiguration of functional connectivity. Commun Biol 2022; 5:295. [PMID: 35365757 PMCID: PMC8975837 DOI: 10.1038/s42003-022-03196-0] [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: 06/07/2021] [Accepted: 02/22/2022] [Indexed: 11/09/2022] Open
Abstract
The brain's functional connectivity fluctuates over time instead of remaining steady in a stationary mode even during the resting state. This fluctuation establishes the dynamical functional connectivity that transitions in a non-random order between multiple modes. Yet it remains unexplored how the transition facilitates the entire brain network as a dynamical system and what utility this mechanism for dynamic reconfiguration can bring over the widely used graph theoretical measurements. To address these questions, we propose to conduct an energetic analysis of functional brain networks using resting-state fMRI and behavioral measurements from the Human Connectome Project. Through comparing the state transition energy under distinct adjacent matrices, we justify that dynamic functional connectivity leads to 60% less energy cost to support the resting state dynamics than static connectivity when driving the transition through default mode network. Moreover, we demonstrate that combining graph theoretical measurements and our energy-based control measurements as the feature vector can provide complementary prediction power for the behavioral scores (Combination vs. Control: t = 9.41, p = 1.64e-13; Combination vs. Graph: t = 4.92, p = 3.81e-6). Our approach integrates statistical inference and dynamical system inspection towards understanding brain networks.
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Affiliation(s)
- Shikuang Deng
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Jingwei Li
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
| | - Shi Gu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
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Usai MC. Inhibitory abilities in girls and boys: More similarities or differences? J Neurosci Res 2022; 101:689-703. [PMID: 35266196 DOI: 10.1002/jnr.25034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 11/21/2021] [Accepted: 02/07/2022] [Indexed: 11/09/2022]
Abstract
This brief review examined the literature from 1990 to June 2020 on sex differences in inhibitory abilities from early childhood to adolescence, primarily in individuals with typical development (TD) and individuals with atypical development. The 38 articles included (28 on individuals with TD, eight on the attention deficit hyperactivity disorder [ADHD] population, and two on individuals with autism spectrum disorder [ASD]) suggest that the cognitive demand of the task is important, together with contextual factors that may interact with the development of inhibitory ability, for revealing differences between the sexes. The literature has neglected the multicomponential nature of inhibitory abilities, and the emphasis has consequently been placed on response inhibition (vs. other components). The implication of the impurity problem has also been considered. The findings on children and adolescents with ADHD or ASD-even for outcomes that are not conclusive-imply that there is no evidence for a difference in inhibitory abilities between males and females. The literature proposes an asynchrony in the development of inhibitory abilities that may explain what is found in typically developing girls who perform more highly than boys on more demanding tasks.
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Qian W, Papadopoulos L, Lu Z, Kroma-Wiley KA, Pasqualetti F, Bassett DS. Path-dependent dynamics induced by rewiring networks of inertial oscillators. Phys Rev E 2022; 105:024304. [PMID: 35291167 DOI: 10.1103/physreve.105.024304] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 10/14/2021] [Indexed: 06/14/2023]
Abstract
In networks of coupled oscillators, it is of interest to understand how interaction topology affects synchronization. Many studies have gained key insights into this question by studying the classic Kuramoto oscillator model on static networks. However, new questions arise when the network structure is time varying or when the oscillator system is multistable, the latter of which can occur when an inertial term is added to the Kuramoto model. While the consequences of evolving topology and multistability on collective behavior have been examined separately, real-world systems such as gene regulatory networks and the brain may exhibit these properties simultaneously. It is thus relevant to ask how time-varying network connectivity impacts synchronization in systems that can exhibit multistability. To address this question, we study how the dynamics of coupled Kuramoto oscillators with inertia are affected when the topology of the underlying network changes in time. We show that hysteretic synchronization behavior in networks of coupled inertial oscillators can be driven by changes in connection topology alone. Moreover, we find that certain fixed-density rewiring schemes induce significant changes to the level of global synchrony that remain even after the network returns to its initial configuration, and we show that these changes are robust to a wide range of network perturbations. Our findings highlight that the specific progression of network topology over time, in addition to its initial or final static structure, can play a considerable role in modulating the collective behavior of systems evolving on complex networks.
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Affiliation(s)
- William Qian
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Lia Papadopoulos
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Zhixin Lu
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Keith A Kroma-Wiley
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, California 92521, USA
| | - Dani S Bassett
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Department of Mechanical Engineering, University of California, Riverside, California 92521, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Santa Fe Institute, Santa Fe, New Mexico 87501, USA
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28
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Yao R, Xue J, Li H, Wang Q, Deng H, Tan S. Dynamics and synchronization control in schizophrenia for EEG signals. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103118] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Parkes L, Moore TM, Calkins ME, Cieslak M, Roalf DR, Wolf DH, Gur RC, Gur RE, Satterthwaite TD, Bassett DS. Network Controllability in Transmodal Cortex Predicts Positive Psychosis Spectrum Symptoms. Biol Psychiatry 2021; 90:409-418. [PMID: 34099190 PMCID: PMC8842484 DOI: 10.1016/j.biopsych.2021.03.016] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 02/11/2021] [Accepted: 03/15/2021] [Indexed: 01/31/2023]
Abstract
BACKGROUND The psychosis spectrum (PS) is associated with structural dysconnectivity concentrated in transmodal cortex. However, understanding of this pathophysiology has been limited by an overreliance on examining direct interregional connectivity. Using network control theory, we measured variation in both direct and indirect connectivity to a region to gain new insights into the pathophysiology of the PS. METHODS We used psychosis symptom data and structural connectivity in 1068 individuals from the Philadelphia Neurodevelopmental Cohort. Applying a network control theory metric called average controllability, we estimated each brain region's capacity to leverage its direct and indirect structural connections to control linear brain dynamics. Using nonlinear regression, we determined the accuracy with which average controllability could predict PS symptoms in out-of-sample testing. We also examined the predictive performance of regional strength, which indexes only direct connections to a region, as well as several graph-theoretic measures of centrality that index indirect connectivity. Finally, we assessed how the prediction performance for PS symptoms varied over the functional hierarchy spanning unimodal to transmodal cortex. RESULTS Average controllability outperformed all other connectivity features at predicting positive PS symptoms and was the only feature to yield above-chance predictive performance. Improved prediction for average controllability was concentrated in transmodal cortex, whereas prediction performance for strength was uniform across the cortex, suggesting that indexing indirect connections through average controllability is crucial in association cortex. CONCLUSIONS Examining interindividual variation in direct and indirect structural connections to transmodal cortex is crucial for accurate prediction of positive PS symptoms.
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Affiliation(s)
- Linden Parkes
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia
| | - Monica E Calkins
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia
| | - Matthew Cieslak
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, Philadelphia, Pennsylvania
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia
| | - Daniel H Wolf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia; Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania; Department of Radiology, Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia; Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania; Department of Radiology, Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Lifespan Brain Institute, University of Pennsylvania & Children's Hospital of Philadelphia, Philadelphia; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania; Santa Fe Institute, Santa Fe, New Mexico.
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30
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Language Tasks and the Network Control Role of the Left Inferior Frontal Gyrus. eNeuro 2021; 8:ENEURO.0382-20.2021. [PMID: 34244340 PMCID: PMC8431826 DOI: 10.1523/eneuro.0382-20.2021] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 04/30/2021] [Accepted: 05/03/2021] [Indexed: 11/21/2022] Open
Abstract
Recent work has combined cognitive neuroscience and control theory to make predictions about cognitive control functions. Here, we test a link between whole-brain theories of semantics and the role of the left inferior frontal gyrus (LIFG) in controlled language performance using network control theory (NCT), a branch of systems engineering. Specifically, we examined whether two properties of node controllability, boundary and modal controllability, were linked to semantic selection and retrieval on sentence completion and verb generation tasks. We tested whether the controllability of the left IFG moderated language selection and retrieval costs and the effects of continuous θ burst stimulation (cTBS), an inhibitory form of transcranial magnetic stimulation (TMS) on behavior in 41 human subjects (25 active, 16 sham). We predicted that boundary controllability, a measure of the theoretical ability of a node to integrate and segregate brain networks, would be linked to word selection in the contextually-rich sentence completion task. In contrast, we expected that modal controllability, a measure of the theoretical ability of a node to drive the brain into specifically hard-to-reach states, would be linked to retrieval on the low-context verb generation task. Boundary controllability was linked to selection and to the ability of TMS to reduce response latencies on the sentence completion task. In contrast, modal controllability was not linked to performance on the tasks or TMS effects. Overall, our results suggest a link between the network integrating role of the LIFG and selection and the overall semantic demands of sentence completion.
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31
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Wurz A, Ayson G, Smith AM, Brunet J. A proof-of-concept sub-study exploring feasibility and preliminary evidence for the role of physical activity on neural activity during executive functioning tasks among young adults after cancer treatment. BMC Neurol 2021; 21:300. [PMID: 34344355 PMCID: PMC8336393 DOI: 10.1186/s12883-021-02280-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 06/10/2021] [Indexed: 11/28/2022] Open
Abstract
Background Executive functioning (EF) deficits are troubling for adolescents and young adults (AYAs) after cancer treatment. Physical activity (PA) may enhance neural activity underlying EF among older adults affected by cancer. Establishing whether PA enhances neural activity among AYAs is warranted. As part of a two-arm, mixed-methods pilot randomized controlled trial (RCT), this proof-of-concept sub-study sought to answer the following questions: (1) is it feasible to use neuroimaging with EF tasks to assess neural activity changes following a 12-week PA intervention? And (2) is there preliminary evidence that a 12-week PA intervention enhances neural activity among AYAs after cancer treatment? Methods AYAs in the pilot RCT were approached for enrollment into this sub-study. Those who were eligible and enrolled, completed functional magnetic resonance imaging (fMRI) with EF tasks (letter n-back, Go/No Go) pre- and post-PA intervention. Sub-study enrollment, adherence to scheduled fMRI scans, outliers, missing data, and EF task performance data were collected. Data were analyzed with descriptive statistics, blood oxygen level dependent (BOLD) analyses, and paired sample t-tests. Results Nine eligible participants enrolled into this sub-study; six attended scheduled fMRI scans. One outlier was identified and was subsequently removed from the analytical sample. Participants showed no differences in EF task performance from pre- to post-PA intervention. Increases in neural activity in brain regions responsible for motor control, information encoding and processing, and decision-making were observed post-PA intervention (p < 0.05; n = 5). Conclusions Findings show that fMRI scans during EF tasks detected neural activity changes (as assessed by the BOLD signal) from pre- to post-PA intervention. Results thus suggest future trials confirming that PA enhances neural activity underlying EF are needed, though feasibility issues require careful consideration to ensure trial success. Trial registration clinicaltrials.gov, NCT03016728. Registered January 11, 2017, clinicaltrials.gov/ct2/show/NCT03016728. Supplementary Information The online version contains supplementary material available at 10.1186/s12883-021-02280-y.
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Affiliation(s)
- Amanda Wurz
- School of Human Kinetics, University of Ottawa, 125 University Private, Montpetit Hall, Room 339, K1N 6N5, Ottawa, Ontario, Canada.,Present affiliation: Faculty of Kinesiology, University of Calgary, Alberta, Calgary, Canada
| | - Gladys Ayson
- School of Psychology, University of Ottawa, Ottawa, Ontario, Canada
| | - Andra M Smith
- School of Psychology, University of Ottawa, Ottawa, Ontario, Canada
| | - Jennifer Brunet
- School of Human Kinetics, University of Ottawa, 125 University Private, Montpetit Hall, Room 339, K1N 6N5, Ottawa, Ontario, Canada. .,Cancer Therapeutic Program, Ottawa Hospital Research Institute, The Ottawa Hospital, Ottawa, Ontario, Canada. .,Institut du savoir Montfort, Hôpital Montfort, Ottawa, Ontario, Canada.
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32
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Braun U, Harneit A, Pergola G, Menara T, Schäfer A, Betzel RF, Zang Z, Schweiger JI, Zhang X, Schwarz K, Chen J, Blasi G, Bertolino A, Durstewitz D, Pasqualetti F, Schwarz E, Meyer-Lindenberg A, Bassett DS, Tost H. Brain network dynamics during working memory are modulated by dopamine and diminished in schizophrenia. Nat Commun 2021; 12:3478. [PMID: 34108456 PMCID: PMC8190281 DOI: 10.1038/s41467-021-23694-9] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2019] [Accepted: 04/27/2021] [Indexed: 02/06/2023] Open
Abstract
Dynamical brain state transitions are critical for flexible working memory but the network mechanisms are incompletely understood. Here, we show that working memory performance entails brain-wide switching between activity states using a combination of functional magnetic resonance imaging in healthy controls and individuals with schizophrenia, pharmacological fMRI, genetic analyses and network control theory. The stability of states relates to dopamine D1 receptor gene expression while state transitions are influenced by D2 receptor expression and pharmacological modulation. Individuals with schizophrenia show altered network control properties, including a more diverse energy landscape and decreased stability of working memory representations. Our results demonstrate the relevance of dopamine signaling for the steering of whole-brain network dynamics during working memory and link these processes to schizophrenia pathophysiology. Working memory requires the brain to switch between cognitive states and activity patterns. Here, the authors show that the steering of these neural network dynamics is influenced by dopamine D1- and D2-receptor function and altered in schizophrenia.
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Affiliation(s)
- Urs Braun
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany. .,Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
| | - Anais Harneit
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Giulio Pergola
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Tommaso Menara
- Mechanical Engineering Department, University of California at Riverside, Riverside, CA, USA
| | - Axel Schäfer
- Bender Institute of Neuroimaging, Justus Liebig University Giessen, Gießen, Germany.,Center for Mind, Brain and Behavior, University of Marburg and Justus Liebig University Giessen, Gießen, Germany
| | - Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Zhenxiang Zang
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Janina I Schweiger
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Xiaolong Zhang
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Kristina Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Junfang Chen
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Giuseppe Blasi
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Daniel Durstewitz
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Fabio Pasqualetti
- Mechanical Engineering Department, University of California at Riverside, Riverside, CA, USA
| | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Andreas Meyer-Lindenberg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.,Department of Psychiatry, University of Pennsylvania, Philadelphia, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, USA.,Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, USA.,Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, USA.,The Santa Fe Institute, Santa Fe, NM, USA
| | - Heike Tost
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
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33
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Assari S, Boyce S, Jovanovic T. Association between Hippocampal Volume and Working Memory in 10,000+ 9-10-Year-Old Children: Sex Differences. CHILDREN-BASEL 2021; 8:children8050411. [PMID: 34070074 PMCID: PMC8158143 DOI: 10.3390/children8050411] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 05/07/2021] [Accepted: 05/11/2021] [Indexed: 12/19/2022]
Abstract
AIM This study tested sex differences in the association between hippocampal volume and working memory of a national sample of 9-10-year-old children in the US. As the hippocampus is functionally lateralized (especially in task-related activities), we explored the results for the right and the left hippocampus. METHODS This is a cross-sectional study using the Adolescent Brain Cognitive Development (ABCD) Study data. This analysis included baseline ABCD data (n = 10,093) of children between ages 9 and 10 years. The predictor variable was right and left hippocampal volume measured by structural magnetic resonance imaging (sMRI). The primary outcome, list sorting working memory, was measured using the NIH toolbox measure. Sex was the moderator. Age, race, ethnicity, household income, parental education, and family structure were the covariates. RESULTS In the overall sample, larger right (b = 0.0013; p < 0.001) and left (b = 0.0013; p < 0.001) hippocampal volumes were associated with higher children's working memory. Sex had statistically significant interactions with the right (b = -0.0018; p = 0.001) and left (b = -0.0012; p = 0.022) hippocampal volumes on children's working memory. These interactions indicated stronger positive associations between right and left hippocampal volume and working memory for females compared to males. CONCLUSION While right and left hippocampal volumes are determinants of children's list sorting working memory, these effects seem to be more salient for female than male children. Research is needed on the role of socialization, sex hormones, and brain functional connectivity as potential mechanisms that may explain the observed sex differences in the role of hippocampal volume as a correlate of working memory.
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Affiliation(s)
- Shervin Assari
- Department of Family Medicine, Charles R. Drew University of Medicine and Science, Los Angeles, CA 90059, USA
- Department of Urban Public Health, Charles R. Drew University of Medicine and Science, Los Angeles, CA 90059, USA
- Correspondence: ; Tel.: +1-734-232-0445; Fax: +1-734-615-873
| | - Shanika Boyce
- Department of Pediatrics, Charles R. Drew University of Medicine and Science, Los Angeles, CA 90059, USA;
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI 48202, USA;
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Zöller D, Sandini C, Schaer M, Eliez S, Bassett DS, Van De Ville D. Structural control energy of resting-state functional brain states reveals less cost-effective brain dynamics in psychosis vulnerability. Hum Brain Mapp 2021; 42:2181-2200. [PMID: 33566395 PMCID: PMC8046160 DOI: 10.1002/hbm.25358] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 12/01/2020] [Accepted: 01/05/2021] [Indexed: 12/19/2022] Open
Abstract
How the brain's white-matter anatomy constrains brain activity is an open question that might give insights into the mechanisms that underlie mental disorders such as schizophrenia. Chromosome 22q11.2 deletion syndrome (22q11DS) is a neurodevelopmental disorder with an extremely high risk for psychosis providing a test case to study developmental aspects of schizophrenia. In this study, we used principles from network control theory to probe the implications of aberrant structural connectivity for the brain's functional dynamics in 22q11DS. We retrieved brain states from resting-state functional magnetic resonance images of 78 patients with 22q11DS and 85 healthy controls. Then, we compared them in terms of persistence control energy; that is, the control energy that would be required to persist in each of these states based on individual structural connectivity and a dynamic model. Persistence control energy was altered in a broad pattern of brain states including both energetically more demanding and less demanding brain states in 22q11DS. Further, we found a negative relationship between persistence control energy and resting-state activation time, which suggests that the brain reduces energy by spending less time in energetically demanding brain states. In patients with 22q11DS, this behavior was less pronounced, suggesting a deficiency in the ability to reduce energy through brain activation. In summary, our results provide initial insights into the functional implications of altered structural connectivity in 22q11DS, which might improve our understanding of the mechanisms underlying the disease.
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Affiliation(s)
- Daniela Zöller
- Medical Image Processing LaboratoryInstitute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
- Institute of Neuromodulation and NeurotechnologyUniversity of TübingenTübingenGermany
- Developmental Imaging an Psychopathology Laboratory, Department of PsychiatryUniversity of GenevaGenevaSwitzerland
| | - Corrado Sandini
- Institute of Neuromodulation and NeurotechnologyUniversity of TübingenTübingenGermany
| | - Marie Schaer
- Institute of Neuromodulation and NeurotechnologyUniversity of TübingenTübingenGermany
| | - Stephan Eliez
- Institute of Neuromodulation and NeurotechnologyUniversity of TübingenTübingenGermany
| | - Danielle S. Bassett
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Electrical & Systems EngineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Physics & AstronomyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Dimitri Van De Ville
- Medical Image Processing LaboratoryInstitute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
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Park HJ, Kang J. A Computational Framework for Controlling the Self-Restorative Brain Based on the Free Energy and Degeneracy Principles. Front Comput Neurosci 2021; 15:590019. [PMID: 33935674 PMCID: PMC8079648 DOI: 10.3389/fncom.2021.590019] [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: 07/31/2020] [Accepted: 03/18/2021] [Indexed: 11/13/2022] Open
Abstract
The brain is a non-linear dynamical system with a self-restoration process, which protects itself from external damage but is often a bottleneck for clinical treatment. To treat the brain to induce the desired functionality, formulation of a self-restoration process is necessary for optimal brain control. This study proposes a computational model for the brain's self-restoration process following the free-energy and degeneracy principles. Based on this model, a computational framework for brain control is established. We posited that the pre-treatment brain circuit has long been configured in response to the environmental (the other neural populations') demands on the circuit. Since the demands persist even after treatment, the treated circuit's response to the demand may gradually approximate the pre-treatment functionality. In this framework, an energy landscape of regional activities, estimated from resting-state endogenous activities by a pairwise maximum entropy model, is used to represent the pre-treatment functionality. The approximation of the pre-treatment functionality occurs via reconfiguration of interactions among neural populations within the treated circuit. To establish the current framework's construct validity, we conducted various simulations. The simulations suggested that brain control should include the self-restoration process, without which the treatment was not optimal. We also presented simulations for optimizing repetitive treatments and optimal timing of the treatment. These results suggest a plausibility of the current framework in controlling the non-linear dynamical brain with a self-restoration process.
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Affiliation(s)
- Hae-Jeong Park
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South Korea.,Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea.,Department of Psychiatry, Yonsei University College of Medicine, Seoul, South Korea.,Brain Korea 21 Project, Graduate School of Medical Science, Yonsei University College of Medicine, Seoul, South Korea.,Department of Cognitive Science, Yonsei University, Seoul, South Korea
| | - Jiyoung Kang
- Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South Korea.,Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea
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36
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A computational framework for optimal control of a self-adjustive neural system with activity-dependent and homeostatic plasticity. Neuroimage 2021; 230:117805. [PMID: 33524581 DOI: 10.1016/j.neuroimage.2021.117805] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 01/20/2021] [Accepted: 01/24/2021] [Indexed: 12/13/2022] Open
Abstract
The control of the brain system has received increasing attention in the domain of brain science. Most brain control studies have been conducted to explore the brain network's graph-theoretic properties or to produce the desired state based on neural state dynamics, regarding the brain as a passively responding system. However, the self-adjusting nature of neural system after treatment has not been fully considered in the brain control. In the present study, we propose a computational framework for optimal control of the brain with a self-adjustment process in the effective connectivity after treatment. The neural system is modeled to adjust its outgoing effective connectivity as activity-dependent plasticity after treatment, followed by synaptic rescaling of incoming effective connectivity. To control this neural system to induce the desired function, the system's self-adjustment parameter is first estimated, based on which the treatment is optimized. Utilizing this framework, we conducted simulations of optimal control over a functional hippocampal circuitry, estimated using dynamic causal modeling of voltage-sensitive dye imaging from the wild type and mutant mice, responding to consecutive electrical stimuli. Simulation results for optimal control of the abnormal circuit toward a healthy circuit using a single node treatment, neural-type specific treatment as an analogy of medication, and combined treatments of medication and nodal treatment suggest the plausibility of the current framework in controlling the self-adjusting neural system within a restricted treatment setting. We believe the proposed computational framework of the self-adjustment system would help optimal control of the dynamic brain after treatment.
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Lydon-Staley DM, Cornblath EJ, Blevins AS, Bassett DS. Modeling brain, symptom, and behavior in the winds of change. Neuropsychopharmacology 2021; 46:20-32. [PMID: 32859996 PMCID: PMC7689481 DOI: 10.1038/s41386-020-00805-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/19/2020] [Accepted: 07/22/2020] [Indexed: 02/08/2023]
Abstract
Neuropsychopharmacology addresses pressing questions in the study of three intertwined complex systems: the brain, human behavior, and symptoms of illness. The field seeks to understand the perturbations that impinge upon those systems, either driving greater health or illness. In the pursuit of this aim, investigators often perform analyses that make certain assumptions about the nature of the systems that are being perturbed. Those assumptions can be encoded in powerful computational models that serve to bridge the wide gulf between a descriptive analysis and a formal theory of a system's response. Here we review a set of three such models along a continuum of complexity, moving from a local treatment to a network treatment: one commonly applied form of the general linear model, impulse response models, and network control models. For each, we describe the model's basic form, review its use in the field, and provide a frank assessment of its relative strengths and weaknesses. The discussion naturally motivates future efforts to interlink data analysis, computational modeling, and formal theory. Our goal is to inspire practitioners to consider the assumptions implicit in their analytical approach, align those assumptions to the complexity of the systems under study, and take advantage of exciting recent advances in modeling the relations between perturbations and system function.
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Affiliation(s)
- David M Lydon-Staley
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Eli J Cornblath
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ann Sizemore Blevins
- 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 Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Neurology, Perelman School of Medicine, 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 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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38
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Bitar D, Walton LM, Schbley B, Mohamed ME, Adel M. Differences in dual task paradigms and executive function ability for recreational athletes in United Arab Emirates. J Phys Ther Sci 2020; 32:698-705. [PMID: 33281283 PMCID: PMC7708006 DOI: 10.1589/jpts.32.698] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 08/04/2020] [Indexed: 11/24/2022] Open
Abstract
[Purpose] The purpose of this study to measure four components of executive function:
(1) cognitive flexibility, (2) inhibition, (3) working memory and (4) processing speed,
along with the ability to dual task in recreational athletes. [Participants and Methods]
This was a cross-sectional study of (n=102) male and female participants, between the ages
of 18–40 years of age across different levels and types of sport related physical
activity. The International Physical Activity Questionnaire (IPAQ), short version, Dual
Task Abilities (DTA) were measured utilizing a quantitative, dual task, gait test and
Executive Function (EF) was measured through Stroop Color Word Test and Trail Making Test.
[Results] Differences in EF and Dual Task-Interference (DTI) in recreational athletes did
not show a significant difference between varying types of sport and level of sport
related activity, with reported values high across all groups. Males reported better dual
task interference abilities than females, though there were no significant differences in
executive function between males and females. Executive function performance was the
highest among the age group (18–24 years) population, but there were no significant
differences between those in the higher age groups (25–34 years) and (35–40 years).
[Conclusion] Overall, those participating in the study exhibited high prevalence of strong
EF ability, regardless of sport activity type or level. This may suggest that type and
level of sport activity may not be important when considering executive function
performance maintenance for recreational athletes.
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Affiliation(s)
- Deema Bitar
- Department of Physiotherapy, University of Sharjah: Sharjah, Non-US 00000, United Arab Emirates
| | - Lori Maria Walton
- Department of Physiotherapy, University of Sharjah: Sharjah, Non-US 00000, United Arab Emirates
| | | | | | | | - Maha Ehab Mohamed
- Department of Physiotherapy, University of Sharjah: Sharjah, Non-US 00000, United Arab Emirates
| | - Mennatallah Adel
- Department of Physiotherapy, University of Sharjah: Sharjah, Non-US 00000, United Arab Emirates
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39
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Ju H, Kim JZ, Beggs JM, Bassett DS. Network structure of cascading neural systems predicts stimulus propagation and recovery. J Neural Eng 2020; 17:056045. [PMID: 33036007 PMCID: PMC11191848 DOI: 10.1088/1741-2552/abbff1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Many neural systems display spontaneous, spatiotemporal patterns of neural activity that are crucial for information processing. While these cascading patterns presumably arise from the underlying network of synaptic connections between neurons, the precise contribution of the network's local and global connectivity to these patterns and information processing remains largely unknown. APPROACH Here, we demonstrate how network structure supports information processing through network dynamics in empirical and simulated spiking neurons using mathematical tools from linear systems theory, network control theory, and information theory. MAIN RESULTS In particular, we show that activity, and the information that it contains, travels through cycles in real and simulated networks. SIGNIFICANCE Broadly, our results demonstrate how cascading neural networks could contribute to cognitive faculties that require lasting activation of neuronal patterns, such as working memory or attention.
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Affiliation(s)
- Harang Ju
- Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - Jason Z Kim
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - John M Beggs
- Department of Physics, Indiana University, Bloomington, IN 47405, United States of America
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, United States of America
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40
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Patankar SP, Kim JZ, Pasqualetti F, Bassett DS. Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks. Netw Neurosci 2020; 4:1091-1121. [PMID: 33195950 PMCID: PMC7655114 DOI: 10.1162/netn_a_00157] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Accepted: 07/15/2020] [Indexed: 01/03/2023] Open
Abstract
The human brain displays rich communication dynamics that are thought to be particularly well-reflected in its marked community structure. Yet, the precise relationship between community structure in structural brain networks and the communication dynamics that can emerge therefrom is not well understood. In addition to offering insight into the structure-function relationship of networked systems, such an understanding is a critical step toward the ability to manipulate the brain's large-scale dynamical activity in a targeted manner. We investigate the role of community structure in the controllability of structural brain networks. At the region level, we find that certain network measures of community structure are sometimes statistically correlated with measures of linear controllability. However, we then demonstrate that this relationship depends on the distribution of network edge weights. We highlight the complexity of the relationship between community structure and controllability by performing numerical simulations using canonical graph models with varying mesoscale architectures and edge weight distributions. Finally, we demonstrate that weighted subgraph centrality, a measure rooted in the graph spectrum, and which captures higher order graph architecture, is a stronger and more consistent predictor of controllability. Our study contributes to an understanding of how the brain's diverse mesoscale structure supports transient communication dynamics.
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Affiliation(s)
| | - Jason Z. Kim
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, CA USA
| | - Danielle S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA USA
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
- Santa Fe Institute, Santa Fe, NM USA
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41
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Srivastava P, Nozari E, Kim JZ, Ju H, Zhou D, Becker C, Pasqualetti F, Pappas GJ, Bassett DS. Models of communication and control for brain networks: distinctions, convergence, and future outlook. Netw Neurosci 2020; 4:1122-1159. [PMID: 33195951 PMCID: PMC7655113 DOI: 10.1162/netn_a_00158] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 07/21/2020] [Indexed: 12/13/2022] Open
Abstract
Recent advances in computational models of signal propagation and routing in the human brain have underscored the critical role of white-matter structure. A complementary approach has utilized the framework of network control theory to better understand how white matter constrains the manner in which a region or set of regions can direct or control the activity of other regions. Despite the potential for both of these approaches to enhance our understanding of the role of network structure in brain function, little work has sought to understand the relations between them. Here, we seek to explicitly bridge computational models of communication and principles of network control in a conceptual review of the current literature. By drawing comparisons between communication and control models in terms of the level of abstraction, the dynamical complexity, the dependence on network attributes, and the interplay of multiple spatiotemporal scales, we highlight the convergence of and distinctions between the two frameworks. Based on the understanding of the intertwined nature of communication and control in human brain networks, this work provides an integrative perspective for the field and outlines exciting directions for future work.
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Affiliation(s)
- Pragya Srivastava
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA
| | - Erfan Nozari
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA USA
| | - Jason Z. Kim
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA
| | - Harang Ju
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Dale Zhou
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Cassiano Becker
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, CA USA
| | - George J. Pappas
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA USA
| | - Danielle S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA USA
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA USA
- Santa Fe Institute, Santa Fe, NM USA
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42
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Bertolero MA, Bassett DS. On the Nature of Explanations Offered by Network Science: A Perspective From and for Practicing Neuroscientists. Top Cogn Sci 2020; 12:1272-1293. [PMID: 32441854 PMCID: PMC7687232 DOI: 10.1111/tops.12504] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 04/16/2020] [Accepted: 04/16/2020] [Indexed: 12/31/2022]
Abstract
Network neuroscience represents the brain as a collection of regions and inter-regional connections. Given its ability to formalize systems-level models, network neuroscience has generated unique explanations of neural function and behavior. The mechanistic status of these explanations and how they can contribute to and fit within the field of neuroscience as a whole has received careful treatment from philosophers. However, these philosophical contributions have not yet reached many neuroscientists. Here we complement formal philosophical efforts by providing an applied perspective from and for neuroscientists. We discuss the mechanistic status of the explanations offered by network neuroscience and how they contribute to, enhance, and interdigitate with other types of explanations in neuroscience. In doing so, we rely on philosophical work concerning the role of causality, scale, and mechanisms in scientific explanations. In particular, we make the distinction between an explanation and the evidence supporting that explanation, and we argue for a scale-free nature of mechanistic explanations. In the course of these discussions, we hope to provide a useful applied framework in which network neuroscience explanations can be exercised across scales and combined with other fields of neuroscience to gain deeper insights into the brain and behavior.
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Affiliation(s)
- Maxwell A Bertolero
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania
- Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania
- Santa Fe Institute
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43
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Wei L, Han X, Yu X, Sun Y, Ding M, Du Y, Jiang W, Zhou Y, Wang H. Brain controllability and morphometry similarity of internet gaming addiction. Methods 2020; 192:93-102. [PMID: 32791337 DOI: 10.1016/j.ymeth.2020.08.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 07/21/2020] [Accepted: 08/08/2020] [Indexed: 12/22/2022] Open
Abstract
Internet gaming addiction (IGD) is a common disease in teenagers which usually reflects the abnormalities in brain function or structure. Several computational models have been applied to investigate the characteristic of IGD brain networks, for instance, the conception of brain controllability. The primary objective of this study was to explore the relationship between brain controllability and IGD related clinical behaviour. A sample of 101 subjects, including 49 IGD patients and 52 normal controls, were recruited to undergo MR T1 and DTI scanning. Specifically, the MR images were used to generate the white matter connectivity matrix and the morphometry similarity network. The morphometry similarity network was then divided into several communities using modular decomposition. After, average controllability, modal controllability and synchronizability were calculated through measuring the adjacency matrix. The results indicated that the IGD group had greater synchronizability and modal controllability compared to that of the control group, and different morphological-based brain communities had different controllability properties. Furthermore, the addiction demonstrated the mediating effects between nodal or modular brain controllability as well as anxiety. In conclusion, brain controllability could be a potential biomarker of IGD.
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Affiliation(s)
- Lei Wei
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Xu Han
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xuchen Yu
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Yawen Sun
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ming Ding
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Yasong Du
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Wenqing Jiang
- Department of Child & Adolescent Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University, Shanghai, China
| | - Yan Zhou
- Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - He Wang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China; Human Phenome Institute, Fudan University, Shanghai, China.
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44
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Murphy AC, Bertolero MA, Papadopoulos L, Lydon-Staley DM, Bassett DS. Multimodal network dynamics underpinning working memory. Nat Commun 2020; 11:3035. [PMID: 32541774 PMCID: PMC7295998 DOI: 10.1038/s41467-020-15541-0] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 03/12/2020] [Indexed: 11/24/2022] Open
Abstract
Complex human cognition arises from the integrated processing of multiple brain systems. However, little is known about how brain systems and their interactions might relate to, or perhaps even explain, human cognitive capacities. Here, we address this gap in knowledge by proposing a mechanistic framework linking frontoparietal system activity, default mode system activity, and the interactions between them, with individual differences in working memory capacity. We show that working memory performance depends on the strength of functional interactions between the frontoparietal and default mode systems. We find that this strength is modulated by the activation of two newly described brain regions, and demonstrate that the functional role of these systems is underpinned by structural white matter. Broadly, our study presents a holistic account of how regional activity, functional connections, and structural linkages together support integrative processing across brain systems in order for the brain to execute a complex cognitive process.
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Affiliation(s)
- Andrew C Murphy
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Maxwell A Bertolero
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Lia Papadopoulos
- Department of Physics and Astronomy, School of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - David M Lydon-Staley
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Physics and Astronomy, School of Arts & Sciences, 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 Electrical & Systems Engineering, School of Engineering & Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Santa Fe Institute, Santa Fe, NM, 87501, USA.
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45
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Tang E, Ju H, Baum GL, Roalf DR, Satterthwaite TD, Pasqualetti F, Bassett DS. Control of brain network dynamics across diverse scales of space and time. Phys Rev E 2020; 101:062301. [PMID: 32688528 PMCID: PMC8728948 DOI: 10.1103/physreve.101.062301] [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] [Received: 01/18/2019] [Accepted: 03/12/2020] [Indexed: 12/30/2022]
Abstract
The human brain is composed of distinct regions that are each associated with particular functions and distinct propensities for the control of neural dynamics. However, the relation between these functions and control profiles is poorly understood, as is the variation in this relation across diverse scales of space and time. Here we probe the relation between control and dynamics in brain networks constructed from diffusion tensor imaging data in a large community sample of young adults. Specifically, we probe the control properties of each brain region and investigate their relationship with dynamics across various spatial scales using the Laplacian eigenspectrum. In addition, through analysis of regional modal controllability and partitioning of modes, we determine whether the associated dynamics are fast or slow, as well as whether they are alternating or monotone. We find that brain regions that facilitate the control of energetically easy transitions are associated with activity on short length scales and slow timescales. Conversely, brain regions that facilitate control of difficult transitions are associated with activity on long length scales and fast timescales. Built on linear dynamical models, our results offer parsimonious explanations for the activity propagation and network control profiles supported by regions of differing neuroanatomical structure.
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Affiliation(s)
- Evelyn Tang
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Pennsylvania 19104, USA
- Max Planck Institute for Dynamics and Self-Organization, Göttingen 37079, Germany
| | - Harang Ju
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Pennsylvania 19104, USA
- Neuroscience Graduate Program, Perelman School of Medicine, University of Pennsylvania, Pennsylvania 19104, USA
| | - Graham L Baum
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Pennsylvania 19104, USA
- Neuroscience Graduate Program, Perelman School of Medicine, University of Pennsylvania, Pennsylvania 19104, USA
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Pennsylvania 19104, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Pennsylvania 19104, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, Riverside, California 92521, USA
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Pennsylvania 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Pennsylvania 19104, USA
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Pennsylvania 19104, USA
- Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Pennsylvania 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Pennsylvania 19104, USA
- Santa Fe Institute, Santa Fe, New Mexico 87501, USA
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46
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Karrer TM, Kim JZ, Stiso J, Kahn AE, Pasqualetti F, Habel U, Bassett DS. A practical guide to methodological considerations in the controllability of structural brain networks. J Neural Eng 2020; 17:026031. [PMID: 31968320 PMCID: PMC7734595 DOI: 10.1088/1741-2552/ab6e8b] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Predicting how the brain can be driven to specific states by means of internal or external control requires a fundamental understanding of the relationship between neural connectivity and activity. Network control theory is a powerful tool from the physical and engineering sciences that can provide insights regarding that relationship; it formalizes the study of how the dynamics of a complex system can arise from its underlying structure of interconnected units. APPROACH Given the recent use of network control theory in neuroscience, it is now timely to offer a practical guide to methodological considerations in the controllability of structural brain networks. Here we provide a systematic overview of the framework, examine the impact of modeling choices on frequently studied control metrics, and suggest potentially useful theoretical extensions. We ground our discussions, numerical demonstrations, and theoretical advances in a dataset of high-resolution diffusion imaging with 730 diffusion directions acquired over approximately 1 h of scanning from ten healthy young adults. MAIN RESULTS Following a didactic introduction of the theory, we probe how a selection of modeling choices affects four common statistics: average controllability, modal controllability, minimum control energy, and optimal control energy. Next, we extend the current state-of-the-art in two ways: first, by developing an alternative measure of structural connectivity that accounts for radial propagation of activity through abutting tissue, and second, by defining a complementary metric quantifying the complexity of the energy landscape of a system. We close with specific modeling recommendations and a discussion of methodological constraints. SIGNIFICANCE Our hope is that this accessible account will inspire the neuroimaging community to more fully exploit the potential of network control theory in tackling pressing questions in cognitive, developmental, and clinical neuroscience.
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Affiliation(s)
- Teresa M. Karrer
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Germany
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jason Z. Kim
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jennifer Stiso
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ari E. Kahn
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, CA 92521, USA
| | - Ute Habel
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Germany
- JARA - Translational Brain Medicine, Aachen, Germany
- Institute of Neuroscience and Medicine: JARA-Institute Brain Structure Function Relationship (INM 10), Research Center Jülich, Jülich, Germany
| | - Danielle S. Bassett
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Physics and Astronomy, College of Arts & Sciences, 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 Electrical and Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Santa Fe Institute, Santa Fe, NM 87501, USA
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47
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Lee WH, Rodrigue A, Glahn DC, Bassett DS, Frangou S. Heritability and Cognitive Relevance of Structural Brain Controllability. Cereb Cortex 2019; 30:3044-3054. [PMID: 31838501 PMCID: PMC7197079 DOI: 10.1093/cercor/bhz293] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 09/20/2019] [Accepted: 10/30/2019] [Indexed: 01/09/2023] Open
Abstract
Cognition and behavior are thought to emerge from the connections and interactions among brain regions. The precise nature of these relationships remains elusive. Here we use tools provided by network control theory to determine how the structural connectivity profile of brain regions may shape individual variation in cognition. In a cohort of healthy young adults (n = 1066), we computed two fundamental brain regional control patterns, average and modal controllability, which index the degree of influence of a region over others. We first established that regional brain controllability measures were both reproducible and heritable. Regions with controllability profiles theoretically conducive to facilitating multiple cognitive operations were over-represented in higher-order resting-state networks. Finally, variation in regional controllability accounted for about 50% of interindividual variability in multiple cognitive domains. We conclude that controllability is a biologically plausible property of the structural connectome and provides a mechanistic explanation for how brain structural architecture may influence cognitive functions.
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Affiliation(s)
- Won Hee Lee
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Amanda Rodrigue
- Tommy Fuss Center for Neuropsychiatric Disease Research, Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - David C Glahn
- Tommy Fuss Center for Neuropsychiatric Disease Research, Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Electrical and Systems Engineering, School of Engineering and 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 and Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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Medaglia JD. Clarifying cognitive control and the controllable connectome. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2019; 10:e1471. [PMID: 29971940 PMCID: PMC6642819 DOI: 10.1002/wcs.1471] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Revised: 05/28/2018] [Accepted: 05/29/2018] [Indexed: 01/08/2023]
Abstract
Cognitive control researchers aim to describe the processes that support adaptive cognition to achieve specific goals. Control theorists consider how to influence the state of systems to reach certain user-defined goals. In brain networks, some conceptual and lexical similarities between cognitive control and control theory offer appealing avenues for scientific discovery. However, these opportunities also come with the risk of conceptual confusion. Here, I suggest that each field of inquiry continues to produce novel and distinct insights. Then, I describe opportunities for synergistic research at the intersection of these subdisciplines with a critical stance that reduces the risk of conceptual confusion. Through this exercise, we can observe that both cognitive neuroscience and systems engineering have much to contribute to cognitive control research in human brain networks. This article is categorized under: Neuroscience > Cognition Computer Science > Neural Networks Neuroscience > Clinical Neuroscience.
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Affiliation(s)
- John D Medaglia
- Department of Psychology, Drexel University, Philadelphia, Pennsylvania
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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