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Sun S, Yan C, Qu S, Luo G, Liu X, Tian F, Dong Q, Li X, Hu B. Resting-state dynamic functional connectivity in major depressive disorder: A systematic review. Prog Neuropsychopharmacol Biol Psychiatry 2024; 135:111076. [PMID: 38972502 DOI: 10.1016/j.pnpbp.2024.111076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 06/02/2024] [Accepted: 06/26/2024] [Indexed: 07/09/2024]
Abstract
As a novel measure, dynamic functional connectivity (dFC) provides insight into the dynamic nature of brain networks and their interactions in resting-state, surpassing traditional static functional connectivity in pathological conditions such as depression. Since a comprehensive review is still lacking, we then reviewed forty-five eligible papers to explore pathological mechanisms of major depressive disorder (MDD) from perspectives including abnormal brain regions and functional networks, brain state, topological properties, relevant recognition, along with longitudinal studies. Though inconsistencies could be found, common findings are: (1) From different perspectives based on dFC, default-mode network (DMN) with its subregions exhibited a close relation to the pathological mechanism of MDD. (2) With a corrupted integrity within large-scale functional networks and imbalance between them, longer fraction time in a relatively weakly-connected state may be a possible property of MDD concerning its relation with DMN. Abnormal transition frequencies between states were correlated to the severity of MDD. (3) Including dynamic properties in topological network metrics enhanced recognition effect. In all, this review summarized its use for clinical diagnosis and treatment, elucidating the non-stationary of MDD patients' aberrant brain activity in the absence of stimuli and bringing new views into its underlying neuro mechanism.
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Affiliation(s)
- Shuting Sun
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China; Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Chang Yan
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Shanshan Qu
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Gang Luo
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Xuesong Liu
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Fuze Tian
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China; Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Qunxi Dong
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China
| | - Xiaowei Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China
| | - Bin Hu
- Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Beijing Institute of Technology, Ministry of Education, China; Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, China.
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Li S, Zhang Z, Jiang A, Ma X, Wang M, Ni H, Yang B, Zheng Y, Wang L, Dong GH. Repetitive transcranial magnetic stimulation reshaped the dynamic reconfiguration of the executive and reward networks in individuals with tobacco use disorder. J Affect Disord 2024; 365:427-436. [PMID: 39197549 DOI: 10.1016/j.jad.2024.08.120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 07/17/2024] [Accepted: 08/23/2024] [Indexed: 09/01/2024]
Abstract
BACKGROUND Studies have demonstrated the potential of repetitive transcranial magnetic stimulation (rTMS) to decrease smoking cravings in individuals with tobacco use disorder (TUD). However, the neural features underlying the effects of rTMS treatment, especially the dynamic attributes of brain networks associated with the treatment, remain unclear. METHODS Using dynamic functional connectivity analysis, this study first explored the differences in dynamic functional network features between 60 subjects with TUD and 64 nonsmoking healthy controls (HCs). Then, the left dorsolateral prefrontal cortex (DLPFC) was targeted for a five-day course of rTMS treatment in the 60 subjects with TUD (active rTMS in 42 subjects and sham treatment in 18 subjects). We explored the effect of rTMS on the dynamic network features associated with rTMS by comparing the actively treated group and the sham group. RESULTS Compared to nonsmokers, TUD subjects exhibited an increased integration coefficient between the frontoparietal network (FPN) and the basal ganglia network (BGN) and a reduced integration coefficient between the medial frontal network (MFN) and the FPN. Analysis of variance revealed that rTMS treatment reduced the integration coefficient between the FPN and BGN and improved the recruitment coefficient of the FPN. LIMITATIONS This study involved a limited sample of young male smokers, and the findings may not generalize to older smokers or female smokers with an extensive history of smoking. CONCLUSION rTMS treatment of the left DLPFC exhibited significant effectiveness in restructuring the neural circuits associated with TUD while significantly mitigating smoking cravings.
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Affiliation(s)
- Shuang Li
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province, PR China; Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, PR China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - ZhengJie Zhang
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, PR China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - Anhang Jiang
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, PR China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - Xuefeng Ma
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, PR China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - Min Wang
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, PR China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - Haosen Ni
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, PR China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - Bo Yang
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, PR China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - Yanbin Zheng
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, PR China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China
| | - Lingxiao Wang
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, PR China; Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, PR China
| | - Guang-Heng Dong
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province, PR China.
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Meng L, Shi Y, Zhao H, Wang D, Zhu X, Ming D. The inertial-based gait normalcy index of dual task cost during turning quantifies gait automaticity improvement in early-stage Parkinson's rehabilitation. J Neuroeng Rehabil 2024; 21:166. [PMID: 39300485 DOI: 10.1186/s12984-024-01456-0] [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: 03/26/2024] [Accepted: 09/02/2024] [Indexed: 09/22/2024] Open
Abstract
BACKGROUND The loss of gait automaticity is a key cause of motor deficits in Parkinson's disease (PD) patients, even at the early stage of the disease. Action observation training (AOT) shows promise in enhancing gait automaticity. However, effective assessment methods are lacking. We aimed to propose a novel gait normalcy index based on dual task cost (NIDTC) and evaluate its validity and responsiveness for early-stage PD rehabilitation. METHODS Thirty early-stage PD patients were recruited and randomly assigned to the AOT or active control (CON) group. The proposed NIDTC during straight walking and turning tasks and clinical scale scores were measured before and after 12 weeks of rehabilitation. The correlations between the NIDTCs and clinical scores were analyzed with Pearson correlation coefficient analysis to evaluate the construct validity. The rehabilitative changes were assessed using repeated-measures ANOVA, while the responsiveness of NIDTC was further compared by t tests. RESULTS The turning-based NIDTC was significantly correlated with multiple clinical scales. Significant group-time interactions were observed for the turning-based NIDTC (F = 4.669, p = 0.042), BBS (F = 6.050, p = 0.022) and PDQ-39 (F = 7.772, p = 0.011) tests. The turning-based NIDTC reflected different rehabilitation effects between the AOT and CON groups, with the largest effect size (p = 0.020, Cohen's d = 0.933). CONCLUSION The turning-based NIDTC exhibited the highest responsiveness for identifying gait automaticity improvement by providing a comprehensive representation of motor ability during dual tasks. It has great potential as a valid measure for early-stage PD diagnosis and rehabilitation assessment. Trial registration Chinese Clinical Trial Registry: ChiCTR2300067657.
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Affiliation(s)
- Lin Meng
- Academy of Medical Engineering and Translational Medicine, Medical School, Faculty of Medicine, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin, 300072, China.
- Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, No. 26 Ziyuan Road, Xiqing District, Tianjin, 300392, China.
| | - Yu Shi
- Academy of Medical Engineering and Translational Medicine, Medical School, Faculty of Medicine, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin, 300072, China
| | - Hongbo Zhao
- Academy of Medical Engineering and Translational Medicine, Medical School, Faculty of Medicine, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin, 300072, China
| | - Deyu Wang
- Academy of Medical Engineering and Translational Medicine, Medical School, Faculty of Medicine, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin, 300072, China
| | - Xiaodong Zhu
- Department of Neurology, Tianjin Medical University General Hospital, No. 154 Anshan Road, Heping District, Tianjin, 300052, China.
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Medical School, Faculty of Medicine, Tianjin University, No. 92 Weijin Road, Nankai District, Tianjin, 300072, China
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Kim J, Park S, Yoo K, Kim S. Double dissociation of visuomotor interaction mediated by visual feedback during continuous de novo motor learning. Commun Biol 2024; 7:1117. [PMID: 39261584 PMCID: PMC11391080 DOI: 10.1038/s42003-024-06808-z] [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: 11/29/2023] [Accepted: 08/29/2024] [Indexed: 09/13/2024] Open
Abstract
While the sensorimotor cortices are central neural substrates for motor control and learning, how the interaction between their subregions with visual cortices contributes to acquiring de novo visuomotor skills is poorly understood. We design a continuous visuomotor task in fMRI where participants control a cursor using their fingers while learning an arbitrary finger-to-cursor mapping. To investigate visuomotor interaction in the de novo motor task, we manipulate visual feedback of a cursor such that they learn to control using fingers under two alternating conditions: online cursor feedback is available or unavailable except when a target is reached. As a result, we find double dissociation of fMRI activity in subregions of the sensorimotor and visual cortices. Specifically, motor and late visual cortices are more active with online cursor feedback, and somatosensory and early visual cortices are more active without online cursor feedback. We also find a significant reduction in functional connectivity between somatosensory cortices and early visual cortices, which is highly correlated with performance improvement. These findings support the distinct interaction between subregions of sensorimotor cortices and visual cortices, while the connectivity analysis highlights the critical role of somatosensory cortices during de novo motor learning.
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Affiliation(s)
- Junghyun Kim
- Department of Data Science, Hanyang University, Seoul, Republic of Korea
| | - Sungbeen Park
- Department of Artificial Intelligence, Hanyang University, Seoul, Republic of Korea
| | - Kwangsun Yoo
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University (SKKU), Seoul, Republic of Korea
- AI Research Center, Data Science Research Institute, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea
| | - Sungshin Kim
- Department of Data Science, Hanyang University, Seoul, Republic of Korea.
- Department of Artificial Intelligence, Hanyang University, Seoul, Republic of Korea.
- Department of Healthcare Digital Engineering, Hanyang University, Seoul, Republic of Korea.
- Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science (IBS), Suwon, Republic of Korea.
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Tooley UA, Latham A, Kenley JK, Alexopoulos D, Smyser TA, Nielsen AN, Gorham L, Warner BB, Shimony JS, Neil JJ, Luby JL, Barch DM, Rogers CE, Smyser CD. Prenatal environment is associated with the pace of cortical network development over the first three years of life. Nat Commun 2024; 15:7932. [PMID: 39256419 PMCID: PMC11387486 DOI: 10.1038/s41467-024-52242-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 08/30/2024] [Indexed: 09/12/2024] Open
Abstract
Environmental influences on brain structure and function during early development have been well-characterized, but whether early environments are associated with the pace of brain development is not clear. In pre-registered analyses, we use flexible non-linear models to test the theory that prenatal disadvantage is associated with differences in trajectories of intrinsic brain network development from birth to three years (n = 261). Prenatal disadvantage was assessed using a latent factor of socioeconomic disadvantage that included measures of mother's income-to-needs ratio, educational attainment, area deprivation index, insurance status, and nutrition. We find that prenatal disadvantage is associated with developmental increases in cortical network segregation, with neonates and toddlers with greater exposure to prenatal disadvantage showing a steeper increase in cortical network segregation with age, consistent with accelerated network development. Associations between prenatal disadvantage and cortical network segregation occur at the local scale and conform to a sensorimotor-association hierarchy of cortical organization. Disadvantage-associated differences in cortical network segregation are associated with language abilities at two years, such that lower segregation is associated with improved language abilities. These results shed light on associations between the early environment and trajectories of cortical development.
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Affiliation(s)
- Ursula A Tooley
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA.
| | - Aidan Latham
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Jeanette K Kenley
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Tara A Smyser
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Ashley N Nielsen
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Lisa Gorham
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Barbara B Warner
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, USA
| | - Joshua S Shimony
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Jeffrey J Neil
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Joan L Luby
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Deanna M Barch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Cynthia E Rogers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, USA
| | - Christopher D Smyser
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Pediatrics, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
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Shafiei SB, Shadpour S, Mohler JL, Kauffman EC, Holden M, Gutierrez C. Classification of subtask types and skill levels in robot-assisted surgery using EEG, eye-tracking, and machine learning. Surg Endosc 2024; 38:5137-5147. [PMID: 39039296 PMCID: PMC11362185 DOI: 10.1007/s00464-024-11049-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Accepted: 07/06/2024] [Indexed: 07/24/2024]
Abstract
BACKGROUND Objective and standardized evaluation of surgical skills in robot-assisted surgery (RAS) holds critical importance for both surgical education and patient safety. This study introduces machine learning (ML) techniques using features derived from electroencephalogram (EEG) and eye-tracking data to identify surgical subtasks and classify skill levels. METHOD The efficacy of this approach was assessed using a comprehensive dataset encompassing nine distinct classes, each representing a unique combination of three surgical subtasks executed by surgeons while performing operations on pigs. Four ML models, logistic regression, random forest, gradient boosting, and extreme gradient boosting (XGB) were used for multi-class classification. To develop the models, 20% of data samples were randomly allocated to a test set, with the remaining 80% used for training and validation. Hyperparameters were optimized through grid search, using fivefold stratified cross-validation repeated five times. Model reliability was ensured by performing train-test split over 30 iterations, with average measurements reported. RESULTS The findings revealed that the proposed approach outperformed existing methods for classifying RAS subtasks and skills; the XGB and random forest models yielded high accuracy rates (88.49% and 88.56%, respectively) that were not significantly different (two-sample t-test; P-value = 0.9). CONCLUSION These results underscore the potential of ML models to augment the objectivity and precision of RAS subtask and skill evaluation. Future research should consider exploring ways to optimize these models, particularly focusing on the classes identified as challenging in this study. Ultimately, this study marks a significant step towards a more refined, objective, and standardized approach to RAS training and competency assessment.
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Affiliation(s)
- Somayeh B Shafiei
- The Intelligent Cancer Care Laboratory, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA.
| | - Saeed Shadpour
- Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - James L Mohler
- Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA
| | - Eric C Kauffman
- Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA
| | - Matthew Holden
- School of Computer Science, Carleton University, 1125 Colonel By Drive, Ottawa, ON, K1S 5B6, Canada
| | - Camille Gutierrez
- Obstetrics and Gynecology Residency Program, Sisters of Charity Health System, Buffalo, NY, 14214, USA
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Kazinka R, Roediger D, Xuan L, Yu L, Mueller BA, Camchong J, Opitz A, MacDonald A, Lim KO. tDCS-enhanced cognitive training improves attention and alters connectivity in control and somatomotor networks: A triple blind study. Neuroimage 2024; 298:120792. [PMID: 39147294 PMCID: PMC11425656 DOI: 10.1016/j.neuroimage.2024.120792] [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: 07/02/2024] [Revised: 08/08/2024] [Accepted: 08/13/2024] [Indexed: 08/17/2024] Open
Abstract
BACKGROUND Executive dysfunction such as inattention or forgetfulness can lead to disruptions in a person's daily functioning and quality of life. OBJECTIVE/HYPOTHESIS This triple-blinded randomized clinical trial assessed the efficacy of bifrontal (over the forehead) transcranial direct current stimulation (tDCS) concurrent with cognitive training to improve cognitive performance in a healthy sample. METHODS Fifty-eight participants were randomly assigned to one of three stimulation conditions (2 mA left anode-right cathode, 2 mA right anode-left cathode, or sham), which was administered with cognitive training tasks 3x/week over 12 weeks with assessments at baseline, midpoint (6 weeks), and post-training (12 weeks). We assessed cognitive performance, functional connectivity, and the influence of individual differences in training advancement. RESULTS Forty participants completed training. We found that at midpoint and post, all groups improved significantly on overall cognitive performance. The left anode group's attention & vigilance score improved significantly at post, but the other two groups did not. Greater attention training advancement predicted attention improvement by post, most notably in the left anode group. Finally, within-network connectivity decreased in the control network and increased in the somatomotor network across all groups. CONCLUSIONS These results suggest that, given cognitive training, the left anode montage is more effective at improving attention than the right anode montage and sham. Future research may focus on the application of the left anode montage during cognitive training to assess its effectiveness in improving cognition in neuropsychiatric disorders.
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Affiliation(s)
- Rebecca Kazinka
- University of Minnesota, Department of Psychiatry and Behavioral Sciences, United States; University of Minnesota, Department of Biomedical Engineering, United States
| | - Donovan Roediger
- University of Minnesota, Department of Psychiatry and Behavioral Sciences, United States
| | - Lei Xuan
- University of Minnesota, Department of Psychiatry and Behavioral Sciences, United States
| | - Lingyan Yu
- University of Minnesota, Department of Psychology, United States
| | - Bryon A Mueller
- University of Minnesota, Department of Psychiatry and Behavioral Sciences, United States
| | - Jazmin Camchong
- University of Minnesota, Department of Psychiatry and Behavioral Sciences, United States
| | - Alexander Opitz
- University of Minnesota, Department of Biomedical Engineering, United States
| | - Angus MacDonald
- University of Minnesota, Department of Psychology, United States
| | - Kelvin O Lim
- University of Minnesota, Department of Psychiatry and Behavioral Sciences, United States
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Farahani FV, Nebel MB, Wager TD, Lindquist MA. Effects of connectivity hyperalignment (CHA) on estimated brain network properties: from coarse-scale to fine-scale. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.27.609817. [PMID: 39253413 PMCID: PMC11383013 DOI: 10.1101/2024.08.27.609817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
Recent gains in functional magnetic resonance imaging (fMRI) studies have been driven by increasingly sophisticated statistical and computational techniques and the ability to capture brain data at finer spatial and temporal resolution. These advances allow researchers to develop population-level models of the functional brain representations underlying behavior, performance, clinical status, and prognosis. However, even following conventional preprocessing pipelines, considerable inter-individual disparities in functional localization persist, posing a hurdle to performing compelling population-level inference. Persistent misalignment in functional topography after registration and spatial normalization will reduce power in developing predictive models and biomarkers, reduce the specificity of estimated brain responses and patterns, and provide misleading results on local neural representations and individual differences. This study aims to determine how connectivity hyperalignment (CHA)-an analytic approach for handling functional misalignment-can change estimated functional brain network topologies at various spatial scales from the coarsest set of parcels down to the vertex-level scale. The findings highlight the role of CHA in improving inter-subject similarities, while retaining individual-specific information and idiosyncrasies at finer spatial granularities. This highlights the potential for fine-grained connectivity analysis using this approach to reveal previously unexplored facets of brain structure and function.
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Affiliation(s)
- Farzad V Farahani
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Tor D Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Martin A Lindquist
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
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Nestor K, Rasero J, Betzel R, Gianaros PJ, Verstynen T. Cortical network reconfiguration aligns with shifts of basal ganglia and cerebellar influence. ARXIV 2024:arXiv:2408.07977v1. [PMID: 39184535 PMCID: PMC11343224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Mammalian functional architecture flexibly adapts, transitioning from integration where information is distributed across the cortex, to segregation where information is focal in densely connected communities of brain regions. This flexibility in cortical brain networks is hypothesized to be driven by control signals originating from subcortical pathways, with the basal ganglia shifting the cortex towards integrated processing states and the cerebellum towards segregated states. In a sample of healthy human participants (N=242), we used fMRI to measure temporal variation in global brain networks while participants performed two tasks with similar cognitive demands (Stroop and Multi-Source Inference Task (MSIT)). Using the modularity index, we determined cortical networks shifted from integration (low modularity) at rest to high modularity during easier i.e. congruent (segregation). Increased task difficulty (incongruent) resulted in lower modularity in comparison to the easier counterpart indicating more integration of the cortical network. Influence of basal ganglia and cerebellum was measured using eigenvector centrality. Results correlated with decreases and increases in cortical modularity respectively, with only the basal ganglia influence preceding cortical integration. Our results support the theory the basal ganglia shifts cortical networks to integrated states due to environmental demand. Cerebellar influence correlates with shifts to segregated cortical states, though may not play a causal role.
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Affiliation(s)
- Kimberly Nestor
- Department of Psychology, Carnegie Mellon University, Pittsburgh PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh PA, USA
- Carnegie Mellon Neuroscience Institute, Pittsburgh PA, USA
| | - Javier Rasero
- Department of Psychology, Carnegie Mellon University, Pittsburgh PA, USA
- School of Data Science, University of Virginia, Charlottesville VA, USA
| | - Richard Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN, USA
- Cognitive Science Program, Indiana University, Bloomington IN, USA
- Indiana University, Network Science Institute, Bloomington IN, USA
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455
| | - Peter J. Gianaros
- Center for the Neural Basis of Cognition, Pittsburgh PA, USA
- Department of Psychology, University of Pittsburgh, Pittsburgh PA, USA
| | - Timothy Verstynen
- Department of Psychology, Carnegie Mellon University, Pittsburgh PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh PA, USA
- Carnegie Mellon Neuroscience Institute, Pittsburgh PA, USA
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh PA, USA
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10
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Tanner J, Faskowitz J, Kennedy DP, Betzel RF. Dynamic adaptation to novelty in the brain is related to arousal and intelligence. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.02.606380. [PMID: 39149315 PMCID: PMC11326181 DOI: 10.1101/2024.08.02.606380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
How does the human brain respond to novelty? Here, we address this question using fMRI data wherein human participants watch the same movie scene four times. On the first viewing, this movie scene is novel, and on later viewings it is not. We find that brain activity is lower-dimensional in response to novelty. At a finer scale, we find that this reduction in the dimensionality of brain activity is the result of increased coupling in specific brain systems, most specifically within and between the control and dorsal attention systems. Additionally, we found that novelty induced an increase in between-subject synchronization of brain activity in the same brain systems. We also find evidence that adaptation to novelty, herein operationalized as the difference between baseline coupling and novelty-response coupling, is related to fluid intelligence. Finally, using separately collected out-of-sample data, we find that the above results may be linked to psychological arousal.
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Affiliation(s)
- Jacob Tanner
- Luddy School of Informatics, Computing, and Engineering
- Cognitive Science Program
| | | | - Daniel P. Kennedy
- Cognitive Science Program
- Department of Psychological and Brain Sciences
- Program in Neuroscience, Indiana University, Bloomington, IN 47405
| | - Richard F. Betzel
- Luddy School of Informatics, Computing, and Engineering
- Cognitive Science Program
- Department of Psychological and Brain Sciences
- Program in Neuroscience, Indiana University, Bloomington, IN 47405
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11
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Gu S, Mattar MG, Tang H, Pan G. Emergence and reconfiguration of modular structure for artificial neural networks during continual familiarity detection. SCIENCE ADVANCES 2024; 10:eadm8430. [PMID: 39058783 PMCID: PMC11277393 DOI: 10.1126/sciadv.adm8430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 06/21/2024] [Indexed: 07/28/2024]
Abstract
Advances in artificial intelligence enable neural networks to learn a wide variety of tasks, yet our understanding of the learning dynamics of these networks remains limited. Here, we study the temporal dynamics during learning of Hebbian feedforward neural networks in tasks of continual familiarity detection. Drawing inspiration from network neuroscience, we examine the network's dynamic reconfiguration, focusing on how network modules evolve throughout learning. Through a comprehensive assessment involving metrics like network accuracy, modular flexibility, and distribution entropy across diverse learning modes, our approach reveals various previously unknown patterns of network reconfiguration. We find that the emergence of network modularity is a salient predictor of performance and that modularization strengthens with increasing flexibility throughout learning. These insights not only elucidate the nuanced interplay of network modularity, accuracy, and learning dynamics but also bridge our understanding of learning in artificial and biological agents.
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Affiliation(s)
- Shi Gu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
- Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China
| | - Marcelo G. Mattar
- Department of Psychology, New York University, New York, NY 10003, USA
| | - Huajin Tang
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
- State Key Laboratory of Brain Machine Intelligence, Zhejiang University, Hangzhou, China
| | - Gang Pan
- College of Computer Science and Technology, Zhejiang University, Hangzhou, China
- State Key Laboratory of Brain Machine Intelligence, Zhejiang University, Hangzhou, China
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12
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Geng H, Xu P, Aleman A, Qin S, Luo YJ. Dynamic Organization of Large-scale Functional Brain Networks Supports Interactions Between Emotion and Executive Control. Neurosci Bull 2024; 40:981-991. [PMID: 38261252 PMCID: PMC11250766 DOI: 10.1007/s12264-023-01168-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 10/05/2023] [Indexed: 01/24/2024] Open
Abstract
Emotion and executive control are often conceptualized as two distinct modes of human brain functioning. Little, however, is known about how the dynamic organization of large-scale functional brain networks that support flexible emotion processing and executive control, especially their interactions. The amygdala and prefrontal systems have long been thought to play crucial roles in these processes. Recent advances in human neuroimaging studies have begun to delineate functional organization principles among the large-scale brain networks underlying emotion, executive control, and their interactions. Here, we propose a dynamic brain network model to account for interactive competition between emotion and executive control by reviewing recent resting-state and task-related neuroimaging studies using network-based approaches. In this model, dynamic interactions among the executive control network, the salience network, the default mode network, and sensorimotor networks enable dynamic processes of emotion and support flexible executive control of multiple processes; neural oscillations across multiple frequency bands and the locus coeruleus-norepinephrine pathway serve as communicational mechanisms underlying dynamic synergy among large-scale functional brain networks. This model has important implications for understanding how the dynamic organization of complex brain systems and networks empowers flexible cognitive and affective functions.
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Affiliation(s)
- Haiyang Geng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Tianqiao and Chrissy, Chen Institute for Translational Research, Shanghai, 200040, China
| | - Pengfei Xu
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (BNU), Faculty of Psychology, Beijing Normal University, Beijing, 100875, China
- Center for Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen, 518107, China
| | - Andre Aleman
- University of Groningen, Department of Biomedical Sciences of Cells and Systems, Section Cognitive Neuroscience, University Medical Center Groningen, Groningen, The Netherlands
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
| | - Yue-Jia Luo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- Institute for Neuropsychological Rehabilitation, University of Health and Rehabilitation Sciences, Qingdao, 266113, China.
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Magnetic Resonance Imaging, Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, 518060, China.
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13
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Edgar AK, Chong LX, Wood-Bradley R, Armitage JA, Narayanan A, Macfarlane S. The role of extended reality in optometry education: a narrative review. Clin Exp Optom 2024:1-10. [PMID: 38944747 DOI: 10.1080/08164622.2024.2366366] [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: 03/29/2024] [Accepted: 06/05/2024] [Indexed: 07/01/2024] Open
Abstract
The evolution of digitally based pedagogies, such as extended reality (XR) - a group of simulated learning environments that include virtual simulation, virtual reality, and augmented reality - has prompted optometry educators to seek evidence to guide the implementation of these teaching and learning activities within their curricula. Looking more broadly across the medical and allied health fields, there is a wealth of evidence to guide the incorporation of XR, as it is increasingly being integrated into the curricula of other select health professions disciplines. Educators from these disciplines continue to explore and embed XR in practice. This narrative review summarises the findings and appraises the literature on the use of XR in optometry education. It identifies the learning domains in which XR has been implemented in optometry education and proposes areas for further investigation. The review questions the technology-focused approach that has driven the literature within the review and calls for richer pedagogical foundations with suggestions for future research agendas. As such, this narrative review provides optometry educators with new ways of understanding XR and its relationship with the curriculum.
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Affiliation(s)
- Amanda K Edgar
- Deakin Learning Futures, Deakin University, Geelong, Australia
| | - Luke X Chong
- School of Medicine, Deakin University, Geelong, Australia
| | | | | | - Anuradha Narayanan
- Unit of Medical Research Foundation, Elite School of Optometry, Chennai, India
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14
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Shafiei SB, Shadpour S, Mohler JL, Rashidi P, Toussi MS, Liu Q, Shafqat A, Gutierrez C. Prediction of Robotic Anastomosis Competency Evaluation (RACE) metrics during vesico-urethral anastomosis using electroencephalography, eye-tracking, and machine learning. Sci Rep 2024; 14:14611. [PMID: 38918593 PMCID: PMC11199555 DOI: 10.1038/s41598-024-65648-3] [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: 06/26/2023] [Accepted: 06/21/2024] [Indexed: 06/27/2024] Open
Abstract
Residents learn the vesico-urethral anastomosis (VUA), a key step in robot-assisted radical prostatectomy (RARP), early in their training. VUA assessment and training significantly impact patient outcomes and have high educational value. This study aimed to develop objective prediction models for the Robotic Anastomosis Competency Evaluation (RACE) metrics using electroencephalogram (EEG) and eye-tracking data. Data were recorded from 23 participants performing robot-assisted VUA (henceforth 'anastomosis') on plastic models and animal tissue using the da Vinci surgical robot. EEG and eye-tracking features were extracted, and participants' anastomosis subtask performance was assessed by three raters using the RACE tool and operative videos. Random forest regression (RFR) and gradient boosting regression (GBR) models were developed to predict RACE scores using extracted features, while linear mixed models (LMM) identified associations between features and RACE scores. Overall performance scores significantly differed among inexperienced, competent, and experienced skill levels (P value < 0.0001). For plastic anastomoses, R2 values for predicting unseen test scores were: needle positioning (0.79), needle entry (0.74), needle driving and tissue trauma (0.80), suture placement (0.75), and tissue approximation (0.70). For tissue anastomoses, the values were 0.62, 0.76, 0.65, 0.68, and 0.62, respectively. The models could enhance RARP anastomosis training by offering objective performance feedback to trainees.
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Affiliation(s)
- Somayeh B Shafiei
- Intelligent Cancer Care Laboratory, Department of Urology, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, NY, 14263, USA.
| | - Saeed Shadpour
- Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - James L Mohler
- Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA
| | - Parisa Rashidi
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, 32611, USA
| | - Mehdi Seilanian Toussi
- Intelligent Cancer Care Laboratory, Department of Urology, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, NY, 14263, USA
| | - Qian Liu
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Ambreen Shafqat
- Intelligent Cancer Care Laboratory, Department of Urology, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, NY, 14263, USA
| | - Camille Gutierrez
- Obstetrics and Gynecology Residency Program, Sisters of Charity Health System, Buffalo, NY, 14214, USA
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15
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Beck MM, Kristensen FT, Abrahamsen G, Spedden ME, Christensen MS, Lundbye-Jensen J. Distinct mechanisms for online and offline motor skill learning across human development. Dev Sci 2024:e13536. [PMID: 38867436 DOI: 10.1111/desc.13536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 04/25/2024] [Accepted: 05/27/2024] [Indexed: 06/14/2024]
Abstract
The human central nervous system (CNS) undergoes tremendous changes from childhood to adulthood and this may affect how individuals at different stages of development learn new skills. Here, we studied motor skill learning in children, adolescents, and young adults to test the prediction that differences in the maturation of different learning mechanisms lead to distinct temporal patterns of motor learning during practice and overnight. We found that overall learning did not differ between children, adolescents, and young adults. However, we demonstrate that adult-like skill learning is characterized by rapid and large improvements in motor performance during practice (i.e., online) that are susceptible to forgetting and decay over time (i.e., offline). On the other hand, child-like learning exhibits slower and less pronounced improvements in performance during practice, but these improvements are robust against forgetting and lead to gains in performance overnight without further practice. The different temporal dynamics of motor skill learning suggest an engagement of distinct learning mechanisms in the human CNS during development. In conclusion, adult-like skill learning mechanisms favor online improvements in motor performance whereas child-like learning mechanisms favors offline behavioral gains. RESEARCH HIGHLIGHTS: Many essential motor skills, like walking, talking, and writing, are acquired during childhood, and it is colloquially thought that children learn better than adults. We investigated dynamics of motor skill learning in children, adolescents, and young adults. Adults displayed substantial improvements during practice that was susceptible to forgetting over time. Children displayed smaller improvements during practice that were resilient against forgetting. The distinct age-related characteristics of these processes of acquisition and consolidation suggest that skill learning relies on different mechanisms in the immature and mature central nervous system.
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Affiliation(s)
- Mikkel Malling Beck
- Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen N, Denmark
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Hvidovre, Denmark
| | | | - Gitte Abrahamsen
- Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen N, Denmark
| | | | | | - Jesper Lundbye-Jensen
- Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen N, Denmark
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16
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Xiao H, Tang D, Zheng C, Yang Z, Zhao W, Guo S. Atypical dynamic network reconfiguration and genetic mechanisms in patients with major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2024; 132:110957. [PMID: 38365102 DOI: 10.1016/j.pnpbp.2024.110957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 01/23/2024] [Accepted: 01/30/2024] [Indexed: 02/18/2024]
Abstract
BACKGROUND Brain dynamics underlie complex forms of flexible cognition or the ability to shift between different mental modes. However, the precise dynamic reconfiguration based on multi-layer network analysis and the genetic mechanisms of major depressive disorder (MDD) remains unclear. METHODS Resting-state functional magnetic resonance imaging (fMRI) data were acquired from the REST-meta-MDD consortium, including 555 patients with MDD and 536 healthy controls (HC). A time-varying multi-layer network was constructed, and dynamic modular characteristics were used to investigate the network reconfiguration. Additionally, partial least squares regression analysis was performed using transcriptional data provided by the Allen Human Brain Atlas (AHBA) to identify genes associated with atypical dynamic network reconfiguration in MDD. RESULTS In comparison to HC, patients with MDD exhibited lower global and local recruitment coefficients. The local reduction was particularly evident in the salience and subcortical networks. Spatial transcriptome correlation analysis revealed an association between gene expression profiles and atypical dynamic network reconfiguration observed in MDD. Further functional enrichment analyses indicated that positively weighted reconfiguration-related genes were primarily associated with metabolic and biosynthetic pathways. Additionally, negatively enriched genes were predominantly related to programmed cell death-related terms. CONCLUSIONS Our findings offer robust evidence of the atypical dynamic reconfiguration in patients with MDD from a novel perspective. These results offer valuable insights for further exploration into the mechanisms underlying MDD.
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Affiliation(s)
- Hairong Xiao
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, China
| | - Dier Tang
- School of Mathematics, Jilin University, Changchun 130015, China
| | - Chuchu Zheng
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, China
| | - Zeyu Yang
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, China
| | - Wei Zhao
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, China
| | - Shuixia Guo
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, China.
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17
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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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18
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Corredor D, Segobin S, Hinault T, Eustache F, Dayan J, Guillery-Girard B, Naveau M. The multiscale topological organization of the functional brain network in adolescent PTSD. Cereb Cortex 2024; 34:bhae246. [PMID: 38864573 PMCID: PMC11167567 DOI: 10.1093/cercor/bhae246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 05/17/2024] [Accepted: 05/18/2024] [Indexed: 06/13/2024] Open
Abstract
The experience of an extremely aversive event can produce enduring deleterious behavioral, and neural consequences, among which posttraumatic stress disorder (PTSD) is a representative example. Although adolescence is a period of great exposure to potentially traumatic events, the effects of trauma during adolescence remain understudied in clinical neuroscience. In this exploratory work, we aim to study the whole-cortex functional organization of 14 adolescents with PTSD using a data-driven method tailored to our population of interest. To do so, we built on the network neuroscience framework and specifically on multilayer (multisubject) community analysis to study the functional connectivity of the brain. We show, across different topological scales (the number of communities composing the cortex), a hyper-colocalization between regions belonging to occipital and pericentral regions and hypo-colocalization in middle temporal, posterior-anterior medial, and frontal cortices in the adolescent PTSD group compared to a nontrauma exposed group of adolescents. These preliminary results raise the question of an altered large-scale cortical organization in adolescent PTSD, opening an interesting line of research for future investigations.
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Affiliation(s)
- David Corredor
- Centre Cyceron, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, Normandie Université, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, Caen 14000, France
| | - Shailendra Segobin
- Centre Cyceron, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, Normandie Université, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, Caen 14000, France
| | - Thomas Hinault
- Centre Cyceron, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, Normandie Université, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, Caen 14000, France
| | - Francis Eustache
- Centre Cyceron, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, Normandie Université, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, Caen 14000, France
| | - Jacques Dayan
- Centre Cyceron, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, Normandie Université, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, Caen 14000, France
- Pôle Hospitalo-Universitaire de Psychiatrie de l’Enfant et de l’Adolescent, Centre Hospitalier Guillaume Régnier, Université Rennes 1, Rennes 35700, France
| | - Bérengère Guillery-Girard
- Centre Cyceron, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, Normandie Université, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, Caen 14000, France
| | - Mikaël Naveau
- UNICAEN, CNRS, INSERM, CEA, UAR3408 CYCERON, Normandie Université, Caen 14000, France
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19
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Verwey WB. C-SMB 2.0: Integrating over 25 years of motor sequencing research with the Discrete Sequence Production task. Psychon Bull Rev 2024; 31:931-978. [PMID: 37848660 PMCID: PMC11192694 DOI: 10.3758/s13423-023-02377-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/30/2023] [Indexed: 10/19/2023]
Abstract
An exhaustive review is reported of over 25 years of research with the Discrete Sequence Production (DSP) task as reported in well over 100 articles. In line with the increasing call for theory development, this culminates into proposing the second version of the Cognitive framework of Sequential Motor Behavior (C-SMB 2.0), which brings together known models from cognitive psychology, cognitive neuroscience, and motor learning. This processing framework accounts for the many different behavioral results obtained with the DSP task and unveils important properties of the cognitive system. C-SMB 2.0 assumes that a versatile central processor (CP) develops multimodal, central-symbolic representations of short motor segments by repeatedly storing the elements of these segments in short-term memory (STM). Independently, the repeated processing by modality-specific perceptual and motor processors (PPs and MPs) and by the CP when executing sequences gradually associates successively used representations at each processing level. The high dependency of these representations on active context information allows for the rapid serial activation of the sequence elements as well as for the executive control of tasks as a whole. Speculations are eventually offered as to how the various cognitive processes could plausibly find their neural underpinnings within the intricate networks of the brain.
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Affiliation(s)
- Willem B Verwey
- Department of Learning, Data-Analytics and Technology, Section Cognition, Data and Education, Faculty of Behavioral, Management and Social sciences, University of Twente, PO Box 217, 7500 AE, Enschede, the Netherlands.
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Coronel-Oliveros C, Medel V, Orellana S, Rodiño J, Lehue F, Cruzat J, Tagliazucchi E, Brzezicka A, Orio P, Kowalczyk-Grębska N, Ibáñez A. Gaming expertise induces meso‑scale brain plasticity and efficiency mechanisms as revealed by whole-brain modeling. Neuroimage 2024; 293:120633. [PMID: 38704057 DOI: 10.1016/j.neuroimage.2024.120633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 04/17/2024] [Accepted: 04/30/2024] [Indexed: 05/06/2024] Open
Abstract
Video games are a valuable tool for studying the effects of training and neural plasticity on the brain. However, the underlying mechanisms related to plasticity-associated brain structural changes and their impact on brain dynamics are unknown. Here, we used a semi-empirical whole-brain model to study structural neural plasticity mechanisms linked to video game expertise. We hypothesized that video game expertise is associated with neural plasticity-mediated changes in structural connectivity that manifest at the meso‑scale level, resulting in a more segregated functional network topology. To test this hypothesis, we combined structural connectivity data of StarCraft II video game players (VGPs, n = 31) and non-players (NVGPs, n = 31), with generic fMRI data from the Human Connectome Project and computational models, to generate simulated fMRI recordings. Graph theory analysis on simulated data was performed during both resting-state conditions and external stimulation. VGPs' simulated functional connectivity was characterized by a meso‑scale integration, with increased local connectivity in frontal, parietal, and occipital brain regions. The same analyses at the level of structural connectivity showed no differences between VGPs and NVGPs. Regions that increased their connectivity strength in VGPs are known to be involved in cognitive processes crucial for task performance such as attention, reasoning, and inference. In-silico stimulation suggested that differences in FC between VGPs and NVGPs emerge in noisy contexts, specifically when the noisy level of stimulation is increased. This indicates that the connectomes of VGPs may facilitate the filtering of noise from stimuli. These structural alterations drive the meso‑scale functional changes observed in individuals with gaming expertise. Overall, our work sheds light on the mechanisms underlying structural neural plasticity triggered by video game experiences.
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Affiliation(s)
- Carlos Coronel-Oliveros
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Diagonal Las Torres, Peñalolén, Santiago 2640, Chile; Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California US and Trinity College Dublin, Ireland; Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Harrington, Playa Ancha, Valparaíso 287, Chile
| | - Vicente Medel
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Diagonal Las Torres, Peñalolén, Santiago 2640, Chile; Brain and Mind Centre, The University of Sydney, 94 Mallett St, Camperdown, NSW 2050, Australia; Department of Neuroscience, Universidad de Chile, Independencia 1027, Independencia, Santiago, Chile
| | - Sebastián Orellana
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Harrington, Playa Ancha, Valparaíso 287, Chile
| | - Julio Rodiño
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Harrington, Playa Ancha, Valparaíso 287, Chile; Brain Dynamics Laboratory, Facultad de Ingeniería, Universidad de Valparaíso, General Cruz 222, Valparaíso, Chile
| | - Fernando Lehue
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Harrington, Playa Ancha, Valparaíso 287, Chile
| | - Josephine Cruzat
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Diagonal Las Torres, Peñalolén, Santiago 2640, Chile
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Diagonal Las Torres, Peñalolén, Santiago 2640, Chile; Buenos Aires Physics Institute and Physics Department, University of Buenos Aires, Intendente Güiraldes 2160 - Ciudad Universitaria, Buenos Aires, Argentina
| | - Aneta Brzezicka
- Faculty of Psychology, SWPS University of Social Sciences and Humanities, Chodakowska 19/31, Warsaw, 03-815, Poland
| | - Patricio Orio
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Harrington, Playa Ancha, Valparaíso 287, Chile; Instituto de Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Gran Bretaña 1091, Playa Ancha, Valparaíso, Chile.
| | - Natalia Kowalczyk-Grębska
- Faculty of Psychology, SWPS University of Social Sciences and Humanities, Chodakowska 19/31, Warsaw, 03-815, Poland.
| | - Agustín Ibáñez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Diagonal Las Torres, Peñalolén, Santiago 2640, Chile; Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California US and Trinity College Dublin, Ireland; Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Vito Dumas 284, Provincia de Buenos Aires, Argentina; Trinity College Institute of Neuroscience, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland.
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21
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Maddaluno O, Della Penna S, Pizzuti A, Spezialetti M, Corbetta M, de Pasquale F, Betti V. Encoding Manual Dexterity through Modulation of Intrinsic α Band Connectivity. J Neurosci 2024; 44:e1766232024. [PMID: 38538141 PMCID: PMC11097277 DOI: 10.1523/jneurosci.1766-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 01/21/2024] [Accepted: 02/20/2024] [Indexed: 05/18/2024] Open
Abstract
The human hand possesses both consolidated motor skills and remarkable flexibility in adapting to ongoing task demands. However, the underlying mechanisms by which the brain balances stability and flexibility remain unknown. In the absence of external input or behavior, spontaneous (intrinsic) brain connectivity is thought to represent a prior of stored memories. In this study, we investigated how manual dexterity modulates spontaneous functional connectivity in the motor cortex during hand movement. Using magnetoencephalography, in 47 human participants (both sexes), we examined connectivity modulations in the α and β frequency bands at rest and during two motor tasks (i.e., finger tapping or toe squeezing). The flexibility and stability of such modulations allowed us to identify two groups of participants with different levels of performance (high and low performers) on the nine-hole peg test, a test of manual dexterity. In the α band, participants with higher manual dexterity showed distributed decreases of connectivity, specifically in the motor cortex, increased segregation, and reduced nodal centrality. Participants with lower manual dexterity showed an opposite pattern. Notably, these patterns from the brain to behavior are mirrored by results from behavior to the brain. Indeed, when participants were divided using the median split of the dexterity score, we found the same connectivity patterns. In summary, this experiment shows that a long-term motor skill-manual dexterity-influences the way the motor systems respond during movements.
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Affiliation(s)
- Ottavia Maddaluno
- Department of Psychology, Sapienza University of Rome, Rome 00185, Italy
- IRCCS Santa Lucia Foundation, Rome 00179, Italy
| | - Stefania Della Penna
- Department of Neuroscience, Imaging and Clinical Sciences and ITAB - Institute of Advanced Biomedical Technologies, "G. d'Annunzio" University of Chieti and Pescara, Chieti 66013, Italy
| | - Alessandra Pizzuti
- Department of Psychology, Sapienza University of Rome, Rome 00185, Italy
- IRCCS Santa Lucia Foundation, Rome 00179, Italy
| | - Matteo Spezialetti
- Department of Psychology, Sapienza University of Rome, Rome 00185, Italy
- IRCCS Santa Lucia Foundation, Rome 00179, Italy
| | - Maurizio Corbetta
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua 35131, Italy
- Veneto Institute of Molecular Medicine (VIMM), Padova 35129, Italy
| | | | - Viviana Betti
- Department of Psychology, Sapienza University of Rome, Rome 00185, Italy
- IRCCS Santa Lucia Foundation, Rome 00179, Italy
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22
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Li J, Cao Y, Huang M, Li Z, Qin Z, Lang J. The alterations of functional brain networks and its relationship with sport decision-making and training duration in soccer players across different skill levels. Neurosci Lett 2024; 831:137788. [PMID: 38642882 DOI: 10.1016/j.neulet.2024.137788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 04/03/2024] [Accepted: 04/17/2024] [Indexed: 04/22/2024]
Abstract
Studies have indicated that skilled soccer players possess superior decision-making abilities compared to their less-skilled counterparts. However, the underlying neural mechanism for this phenomenon remains incompletely understood. In our investigation, we explored distinctions in the topology of functional brain networks between skilled and less-skilled soccer players. Employing mediating analysis, we scrutinized the relationships among functional brain network parameters, training duration, and decision-making accuracy. Our findings revealed that skilled soccer players demonstrated significantly higher decision-making accuracy compared to their less-skilled counterparts. Skilled players also exhibited increased values in the cluster coefficient, characteristic path length and local efficiency but lower global efficiency. Moreover, we observed enhanced functional brain connectivity within the occipital and cingulo-opercular networks, as well as between the fronto-parietal and cingulo-opercular networks in skilled soccer players. Cluster coefficient and functional connectivity between fronto-parietal and cingulo-opercular networks had positive mediating effects on the association between training duration and sport decision-making accuracy. In conclusion, our study provides initial evidence for distinctions in functional brain network parameters between soccer players with varying skill levels and their relationship with sport decision-making accuracy.
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Affiliation(s)
- Ju Li
- College of P.E. and Sports, Beijing Normal University, Beijing 100875, China.
| | - Yaping Cao
- College of P.E. and Sports, Beijing Normal University, Beijing 100875, China
| | - Minghao Huang
- College of P.E. and Sports, Northwest Normal University, Gansu 730070, China.
| | - Zhongcheng Li
- College of P.E. and Sports, Beijing Normal University, Beijing 100875, China.
| | - Zhe Qin
- College of P.E. and Sports, Northwest Normal University, Gansu 730070, China
| | - Jian Lang
- College of P.E. and Sports, Beijing Normal University, Beijing 100875, China.
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23
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Banca P, Herrojo Ruiz M, Gonzalez-Zalba MF, Biria M, Marzuki AA, Piercy T, Sule A, Fineberg NA, Robbins TW. Action sequence learning, habits, and automaticity in obsessive-compulsive disorder. eLife 2024; 12:RP87346. [PMID: 38722306 PMCID: PMC11081634 DOI: 10.7554/elife.87346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2024] Open
Abstract
This study investigates the goal/habit imbalance theory of compulsion in obsessive-compulsive disorder (OCD), which postulates enhanced habit formation, increased automaticity, and impaired goal/habit arbitration. It directly tests these hypotheses using newly developed behavioral tasks. First, OCD patients and healthy participants were trained daily for a month using a smartphone app to perform chunked action sequences. Despite similar procedural learning and attainment of habitual performance (measured by an objective automaticity criterion) by both groups, OCD patients self-reported higher subjective habitual tendencies via a recently developed questionnaire. Subsequently, in a re-evaluation task assessing choices between established automatic and novel goal-directed actions, both groups were sensitive to re-evaluation based on monetary feedback. However, OCD patients, especially those with higher compulsive symptoms and habitual tendencies, showed a clear preference for trained/habitual sequences when choices were based on physical effort, possibly due to their higher attributed intrinsic value. These patients also used the habit-training app more extensively and reported symptom relief post-study. The tendency to attribute higher intrinsic value to familiar actions may be a potential mechanism leading to compulsions and an important addition to the goal/habit imbalance hypothesis in OCD. We also highlight the potential of smartphone app training as a habit reversal therapeutic tool.
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Affiliation(s)
- Paula Banca
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
- Behavioural and Clinical Neuroscience Institute, University of CambridgeCambridgeUnited Kingdom
| | - Maria Herrojo Ruiz
- Department of Psychology, Goldsmiths University of LondonLondonUnited Kingdom
| | | | - Marjan Biria
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
- Behavioural and Clinical Neuroscience Institute, University of CambridgeCambridgeUnited Kingdom
| | - Aleya A Marzuki
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
- Behavioural and Clinical Neuroscience Institute, University of CambridgeCambridgeUnited Kingdom
| | - Thomas Piercy
- Department of Psychiatry, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
| | - Akeem Sule
- Department of Psychiatry, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
| | - Naomi A Fineberg
- Hertfordshire Partnership University NHS Foundation TrustWelwyn Garden CityUnited Kingdom
- University of HertfordshireHatfieldUnited Kingdom
| | - Trevor W Robbins
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
- Behavioural and Clinical Neuroscience Institute, University of CambridgeCambridgeUnited Kingdom
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24
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Wang X, Zwosta K, Hennig J, Böhm I, Ehrlich S, Wolfensteller U, Ruge H. The dynamics of functional brain network segregation in feedback-driven learning. Commun Biol 2024; 7:531. [PMID: 38710773 DOI: 10.1038/s42003-024-06210-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 04/17/2024] [Indexed: 05/08/2024] Open
Abstract
Prior evidence suggests that increasingly efficient task performance in human learning is associated with large scale brain network dynamics. However, the specific nature of this general relationship has remained unclear. Here, we characterize performance improvement during feedback-driven stimulus-response (S-R) learning by learning rate as well as S-R habit strength and test whether and how these two behavioral measures are associated with a functional brain state transition from a more integrated to a more segregated brain state across learning. Capitalizing on two separate fMRI studies using similar but not identical experimental designs, we demonstrate for both studies that a higher learning rate is associated with a more rapid brain network segregation. By contrast, S-R habit strength is not reliably related to changes in brain network segregation. Overall, our current study results highlight the utility of dynamic functional brain state analysis. From a broader perspective taking into account previous study results, our findings align with a framework that conceptualizes brain network segregation as a general feature of processing efficiency not only in feedback-driven learning as in the present study but also in other types of learning and in other task domains.
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Affiliation(s)
- Xiaoyu Wang
- Faculty of Psychology, Technische Universität Dresden, Dresden, Germany.
| | - Katharina Zwosta
- Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Julius Hennig
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Ilka Böhm
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - Stefan Ehrlich
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Dresden, Germany
- Eating Disorder Treatment and Research Center, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Uta Wolfensteller
- Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
| | - Hannes Ruge
- Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
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25
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Dzialas V, Hoenig MC, Prange S, Bischof GN, Drzezga A, van Eimeren T. Structural underpinnings and long-term effects of resilience in Parkinson's disease. NPJ Parkinsons Dis 2024; 10:94. [PMID: 38697984 PMCID: PMC11066097 DOI: 10.1038/s41531-024-00699-x] [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: 05/31/2023] [Accepted: 04/02/2024] [Indexed: 05/05/2024] Open
Abstract
Resilience in neuroscience generally refers to an individual's capacity to counteract the adverse effects of a neuropathological condition. While resilience mechanisms in Alzheimer's disease are well-investigated, knowledge regarding its quantification, neurobiological underpinnings, network adaptations, and long-term effects in Parkinson's disease is limited. Our study involved 151 Parkinson's patients from the Parkinson's Progression Marker Initiative Database with available Magnetic Resonance Imaging, Dopamine Transporter Single-Photon Emission Computed Tomography scans, and clinical information. We used an improved prediction model linking neuropathology to symptom severity to estimate individual resilience levels. Higher resilience levels were associated with a more active lifestyle, increased grey matter volume in motor-associated regions, a distinct structural connectivity network and maintenance of relative motor functioning for up to a decade. Overall, the results indicate that relative maintenance of motor function in Parkinson's patients may be associated with greater neuronal substrate, allowing higher tolerance against neurodegenerative processes through dynamic network restructuring.
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Affiliation(s)
- Verena Dzialas
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Nuclear Medicine, 50937, Cologne, Germany
- University of Cologne, Faculty of Mathematics and Natural Sciences, 50923, Cologne, Germany
| | - Merle C Hoenig
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Nuclear Medicine, 50937, Cologne, Germany
- Molecular Organization of the Brain, Institute for Neuroscience and Medicine II, Research Center Juelich, 52428, Juelich, Germany
| | - Stéphane Prange
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Nuclear Medicine, 50937, Cologne, Germany
- Université de Lyon, Institut des Sciences Cognitives Marc Jeannerod, CNRS, UMR, 5229, Bron, France
| | - Gérard N Bischof
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Nuclear Medicine, 50937, Cologne, Germany
- Molecular Organization of the Brain, Institute for Neuroscience and Medicine II, Research Center Juelich, 52428, Juelich, Germany
| | - Alexander Drzezga
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Nuclear Medicine, 50937, Cologne, Germany
- Molecular Organization of the Brain, Institute for Neuroscience and Medicine II, Research Center Juelich, 52428, Juelich, Germany
- German Center for Neurodegenerative Diseases, 53127, Bonn, Germany
| | - Thilo van Eimeren
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Nuclear Medicine, 50937, Cologne, Germany.
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, 50937, Cologne, Germany.
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26
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Xu Y, Liao X, Lei T, Cao M, Zhao J, Zhang J, Zhao T, Li Q, Jeon T, Ouyang M, Chalak L, Rollins N, Huang H, He Y. Development of neonatal connectome dynamics and its prediction for cognitive and language outcomes at age 2. Cereb Cortex 2024; 34:bhae204. [PMID: 38771241 DOI: 10.1093/cercor/bhae204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 04/23/2024] [Accepted: 05/01/2024] [Indexed: 05/22/2024] Open
Abstract
The functional brain connectome is highly dynamic over time. However, how brain connectome dynamics evolves during the third trimester of pregnancy and is associated with later cognitive growth remains unknown. Here, we use resting-state functional Magnetic Resonance Imaging (MRI) data from 39 newborns aged 32 to 42 postmenstrual weeks to investigate the maturation process of connectome dynamics and its role in predicting neurocognitive outcomes at 2 years of age. Neonatal brain dynamics is assessed using a multilayer network model. Network dynamics decreases globally but increases in both modularity and diversity with development. Regionally, module switching decreases with development primarily in the lateral precentral gyrus, medial temporal lobe, and subcortical areas, with a higher growth rate in primary regions than in association regions. Support vector regression reveals that neonatal connectome dynamics is predictive of individual cognitive and language abilities at 2 years of age. Our findings highlight network-level neural substrates underlying early cognitive development.
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Affiliation(s)
- Yuehua Xu
- School of Systems Science, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Tianyuan Lei
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Miao Cao
- Institution of Science and Technology for Brain-Inspired Intelligence, Fudan University, No. 220 Handan Road, Shanghai 200433, China
| | - Jianlong Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Jiaying Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Qiongling Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Tina Jeon
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States
| | - Minhui Ouyang
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States
- Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Lina Chalak
- Department of Pediatrics, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States
| | - Nancy Rollins
- Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States
| | - Hao Huang
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States
- Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Chinese Institute for Brain Research, No. 26 Kexueyuan Road, Beijing 102206, China
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27
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Wang KP, Yu CL, Shen C, Schack T, Hung TM. A longitudinal study of the effect of visuomotor learning on functional brain connectivity. Psychophysiology 2024; 61:e14510. [PMID: 38159049 DOI: 10.1111/psyp.14510] [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: 03/06/2023] [Revised: 10/07/2023] [Accepted: 10/12/2023] [Indexed: 01/03/2024]
Abstract
Neural adaptation in the frontoparietal and motor cortex-sensorimotor circuits is crucial for acquiring visuomotor skills. However, the specific nature of highly dynamic neural connectivity in these circuits during the acquisition of visuomotor skills remains unclear. To achieve a more comprehensive understanding of the relationship between acquisition of visuomotor skills and neural connectivity, we used electroencephalographic coherence to capture highly dynamic nature of neural connectivity. We recruited 60 male novices who were randomly assigned to either the experimental group (EG) or the control group (CG). Participants in EG were asked to engage in repeated putting practice, but CG did not engage in golf practice. In addition, we analyzed the connectivity by using 8-13 Hz imaginary inter-site phase coherence in the frontoparietal networks (Fz-P3 and Fz-P4) and the motor cortex-sensorimotor networks (Cz-C3 and Cz-C4) during a golf putting task. To gain a deeper understanding of the dynamic nature of learning trajectories, we compared data at three time points: baseline (T1), 50% improvement from baseline (T2), and 100% improvement from baseline (T3). The results primarily focused on EG, an inverted U-shaped coherence curve was observed in the connectivity of the left motor cortex-sensorimotor circuit, whereas an increase in the connectivity of the right frontoparietal circuit from T2 to T3 was revealed. These results imply that the dynamics of cortico-cortical communication, particularly involving the left motor cortex-sensorimotor and frontal-left parietal circuits. In addition, our findings partially support Hikosaka et al.'s model and provide additional insight into the specific role of these circuits in visuomotor learning.
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Affiliation(s)
- Kuo-Pin Wang
- Center for Cognitive Interaction Technology, Bielefeld University, Bielefeld, Germany
- Neurocognition and Action, Biomechanics Research Group, Faculty of Psychology and Sports Science, Bielefeld University, Bielefeld, Germany
| | - Chien-Lin Yu
- Department of Physical Education and Sport Sciences, National Taiwan Normal University, Taipei, Taiwan
| | - Cheng Shen
- Department of Physical Education and Sport Sciences, National Taiwan Normal University, Taipei, Taiwan
| | - Thomas Schack
- Center for Cognitive Interaction Technology, Bielefeld University, Bielefeld, Germany
- Neurocognition and Action, Biomechanics Research Group, Faculty of Psychology and Sports Science, Bielefeld University, Bielefeld, Germany
| | - Tsung-Min Hung
- Department of Physical Education and Sport Sciences, National Taiwan Normal University, Taipei, Taiwan
- Institute for Research Excellence in Learning Science, National Taiwan Normal University, Taipei, Taiwan
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28
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Tseng HC, Hsieh IH. Effects of absolute pitch on brain activation and functional connectivity during hearing-in-noise perception. Cortex 2024; 174:1-18. [PMID: 38484435 DOI: 10.1016/j.cortex.2024.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/11/2024] [Accepted: 02/06/2024] [Indexed: 04/21/2024]
Abstract
Hearing-in-noise (HIN) ability is crucial in speech and music communication. Recent evidence suggests that absolute pitch (AP), the ability to identify isolated musical notes, is associated with HIN benefits. A theoretical account postulates a link between AP ability and neural network indices of segregation. However, how AP ability modulates the brain activation and functional connectivity underlying HIN perception remains unclear. Here we used functional magnetic resonance imaging to contrast brain responses among a sample (n = 45) comprising 15 AP musicians, 15 non-AP musicians, and 15 non-musicians in perceiving Mandarin speech and melody targets under varying signal-to-noise ratios (SNRs: No-Noise, 0, -9 dB). Results reveal that AP musicians exhibited increased activation in auditory and superior frontal regions across both HIN domains (music and speech), irrespective of noise levels. Notably, substantially higher sensorimotor activation was found in AP musicians when the target was music compared to speech. Furthermore, we examined AP effects on neural connectivity using psychophysiological interaction analysis with the auditory cortex as the seed region. AP musicians showed decreased functional connectivity with the sensorimotor and middle frontal gyrus compared to non-AP musicians. Crucially, AP differentially affected connectivity with parietal and frontal brain regions depending on the HIN domain being music or speech. These findings suggest that AP plays a critical role in HIN perception, manifested by increased activation and functional independence between auditory and sensorimotor regions for perceiving music and speech streams.
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Affiliation(s)
- Hung-Chen Tseng
- Institute of Cognitive Neuroscience, National Central University, Taoyuan City, Taiwan
| | - I-Hui Hsieh
- Institute of Cognitive Neuroscience, National Central University, Taoyuan City, Taiwan; Cognitive Intelligence and Precision Healthcare Center, National Central University, Taoyuan City, Taiwan.
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29
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Girelli F, Rossetti MG, Perlini C, Bellani M. Neural correlates of cognitive behavioral therapy-based interventions for bipolar disorder: A scoping review. J Psychiatr Res 2024; 172:351-359. [PMID: 38447356 DOI: 10.1016/j.jpsychires.2024.02.054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 02/20/2024] [Accepted: 02/24/2024] [Indexed: 03/08/2024]
Abstract
Cognitive Behavioral Therapy (CBT) is among the gold-standard psychotherapeutic interventions for the treatment of psychiatric disorders, including bipolar disorder (BD). While the clinical response of CBT in patients with BD has been widely investigated, its neural correlates remain poorly explored. Therefore, this scoping review aimed to discuss neuroimaging studies on CBT-based interventions in bipolar populations. Particular attention has been paid to similarities and differences between studies to inform future research. The literature search was conducted on PubMed, PsycINFO, and Web of Science databases in June 2023, identifying 307 de-duplicated records. Six studies fulfilled the inclusion criteria and were reviewed. All of them analyzed functional brain activity data. Four studies showed that the clinical response to CBT was associated with changes in the functional activity and/or connectivity of prefrontal and posterior cingulate cortices, temporal parietal junction, amygdala, precuneus, and insula. In two additional studies, a peculiar pattern of baseline activations in the prefrontal cortex, hippocampus, amygdala, and insula predicted post-treatment improvements in depressive symptoms, emotion dysregulation, and psychosocial functioning, although CBT-specific effects were not shown. These results suggest, at the very preliminary level, the potential of CBT-based interventions in modulating neural activity and connectivity of patients with BD, especially in regions ascribed to emotional processing. Nonetheless, the discrepancies between studies concerning aims, design, sample characteristics, and CBT and fMRI protocols do not allow conclusions to be drawn. Further research using multimodal imaging techniques, better-characterized BD samples, and standardized CBT-based interventions is needed.
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Affiliation(s)
- Francesca Girelli
- UOC Psichiatria, Azienda Ospedaliera Universitaria Integrata, Verona, Italy
| | - Maria Gloria Rossetti
- UOC Psichiatria, Azienda Ospedaliera Universitaria Integrata, Verona, Italy; Section of Psychiatry, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Cinzia Perlini
- Section of Clinical Psychology, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy.
| | - Marcella Bellani
- Section of Psychiatry, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
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30
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Miller TD, Kennard C, Gowland PA, Antoniades CA, Rosenthal CR. Differential effects of bilateral hippocampal CA3 damage on the implicit learning and recognition of complex event sequences. Cogn Neurosci 2024; 15:27-55. [PMID: 38384107 PMCID: PMC11147457 DOI: 10.1080/17588928.2024.2315818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 01/25/2024] [Indexed: 02/23/2024]
Abstract
Learning regularities in the environment is a fundament of human cognition, which is supported by a network of brain regions that include the hippocampus. In two experiments, we assessed the effects of selective bilateral damage to human hippocampal subregion CA3, which was associated with autobiographical episodic amnesia extending ~50 years prior to the damage, on the ability to recognize complex, deterministic event sequences presented either in a spatial or a non-spatial configuration. In contrast to findings from related paradigms, modalities, and homologue species, hippocampal damage did not preclude recognition memory for an event sequence studied and tested at four spatial locations, whereas recognition memory for an event sequence presented at a single location was at chance. In two additional experiments, recognition memory for novel single-items was intact, whereas the ability to recognize novel single-items in a different location from that presented at study was at chance. The results are at variance with a general role of the hippocampus in the learning and recognition of complex event sequences based on non-adjacent spatial and temporal dependencies. We discuss the impact of the results on established theoretical accounts of the hippocampal contributions to implicit sequence learning and episodic memory.
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Affiliation(s)
- Thomas D. Miller
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Christopher Kennard
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Penny A. Gowland
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | | | - Clive R. Rosenthal
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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31
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Billot A, Kiran S. Disentangling neuroplasticity mechanisms in post-stroke language recovery. BRAIN AND LANGUAGE 2024; 251:105381. [PMID: 38401381 PMCID: PMC10981555 DOI: 10.1016/j.bandl.2024.105381] [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: 07/20/2023] [Revised: 11/28/2023] [Accepted: 01/12/2024] [Indexed: 02/26/2024]
Abstract
A major objective in post-stroke aphasia research is to gain a deeper understanding of neuroplastic mechanisms that drive language recovery, with the ultimate goal of enhancing treatment outcomes. Subsequent to recent advances in neuroimaging techniques, we now have the ability to examine more closely how neural activity patterns change after a stroke. However, the way these neural activity changes relate to language impairments and language recovery is still debated. The aim of this review is to provide a theoretical framework to better investigate and interpret neuroplasticity mechanisms underlying language recovery in post-stroke aphasia. We detail two sets of neuroplasticity mechanisms observed at the synaptic level that may explain functional neuroimaging findings in post-stroke aphasia recovery at the network level: feedback-based homeostatic plasticity and associative Hebbian plasticity. In conjunction with these plasticity mechanisms, higher-order cognitive control processes dynamically modulate neural activity in other regions to meet communication demands, despite reduced neural resources. This work provides a network-level neurobiological framework for understanding neural changes observed in post-stroke aphasia and can be used to define guidelines for personalized treatment development.
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Affiliation(s)
- Anne Billot
- Center for Brain Recovery, Boston University, Boston, USA; Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Swathi Kiran
- Center for Brain Recovery, Boston University, Boston, USA.
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32
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Xia H, Li T, Hou Y, Liu Z, Chen A. Age-related decline in cognitive flexibility and inadequate preparation: evidence from task-state network analysis. GeroScience 2024:10.1007/s11357-024-01135-x. [PMID: 38514520 DOI: 10.1007/s11357-024-01135-x] [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: 12/20/2023] [Accepted: 03/12/2024] [Indexed: 03/23/2024] Open
Abstract
Behavioral evidence showed decreased cognitive flexibility in older adults. However, task-based network mechanisms of cognitive flexibility in aging (CFA) remain unclear. Here, we provided the first task-state network evidence that CFA was associated with inadequate preparation for switching trials by revealing age-related changes in functional integration. We examined functional integration in a letter-number switch task that distinguished between the cue and target stages. Both young and older adults showed decreased functional integration from the cue stage to the target stage, indicating that control-related processes were executed as the task progressed. However, compared to young adults, older adults showed less cue-to-target reduction in functional integration, which was primarily driven by higher network integration in the target stage. Moreover, less cue-to-target reductions were correlated with age-related decreases in task performance in the switch task. To sum up, compared to young adults, older adults pre-executed less control-related processes in the cue stage and more control-related processes in the target stage. Therefore, the decline in cognitive flexibility in older adults was associated with inadequate preparation for the impending demands of cognitive switching. This study offered novel insights into network mechanisms underlying CFA. Furthermore, we highlighted that training the function of brain networks, in conjunction with providing more preparation time for older adults, may be beneficial to their cognitive flexibility.
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Affiliation(s)
- Haishuo Xia
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Ting Li
- Faculty of Psychology, Beijing Normal University, Beijing, China
| | - Yongqing Hou
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Zijin Liu
- School of Psychology, Shanghai University of Sport, Shanghai, 200438, China
| | - Antao Chen
- School of Psychology, Shanghai University of Sport, Shanghai, 200438, China.
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33
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Arnsten AFT, Wang M, D’Esposito M. Dynamic Network Connectivity: from monkeys to humans. Front Hum Neurosci 2024; 18:1353043. [PMID: 38384333 PMCID: PMC10879414 DOI: 10.3389/fnhum.2024.1353043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 01/19/2024] [Indexed: 02/23/2024] Open
Abstract
Human brain imaging research using functional MRI (fMRI) has uncovered flexible variations in the functional connectivity between brain regions. While some of this variability likely arises from the pattern of information flow through circuits, it may also be influenced by rapid changes in effective synaptic strength at the molecular level, a phenomenon called Dynamic Network Connectivity (DNC) discovered in non-human primate circuits. These neuromodulatory molecular mechanisms are found in layer III of the macaque dorsolateral prefrontal cortex (dlPFC), the site of the microcircuits shown by Goldman-Rakic to be critical for working memory. This research has shown that the neuromodulators acetylcholine, norepinephrine, and dopamine can rapidly change the strength of synaptic connections in layer III dlPFC by (1) modifying the depolarization state of the post-synaptic density needed for NMDA receptor neurotransmission and (2) altering the open state of nearby potassium channels to rapidly weaken or strengthen synaptic efficacy and the strength of persistent neuronal firing. Many of these actions involve increased cAMP-calcium signaling in dendritic spines, where varying levels can coordinate the arousal state with the cognitive state. The current review examines the hypothesis that some of the dynamic changes in correlative strength between cortical regions observed in human fMRI studies may arise from these molecular underpinnings, as has been seen when pharmacological agents or genetic alterations alter the functional connectivity of the dlPFC consistent with the macaque physiology. These DNC mechanisms provide essential flexibility but may also confer vulnerability to malfunction when dysregulated in cognitive disorders.
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Affiliation(s)
- Amy F. T. Arnsten
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States
| | - Min Wang
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States
| | - Mark D’Esposito
- Department of Psychology, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
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34
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Wang Y, Zhang Y, Xu T, Han X, Ge X, Chen F. Finger motor representation supports the autonomy in arithmetic: neuroimaging evidence from abacus training. Cereb Cortex 2024; 34:bhad524. [PMID: 38186011 DOI: 10.1093/cercor/bhad524] [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: 07/24/2023] [Revised: 12/09/2023] [Accepted: 12/11/2023] [Indexed: 01/09/2024] Open
Abstract
Researches have reported the close association between fingers and arithmetic. However, it remains unclear whether and how finger training can benefit arithmetic. To address this issue, we used the abacus-based mental calculation (AMC), which combines finger training and mental arithmetic learning, to explore the neural correlates underlying finger-related arithmetic training. A total of 147 Chinese children (75 M/72 F, mean age, 6.89 ± 0.46) were recruited and randomly assigned into AMC and control groups at primary school entry. The AMC group received 5 years of AMC training, and arithmetic abilities and resting-state functional magnetic resonance images data were collected from both groups at year 1/3/5. The connectome-based predictive modeling was used to find the arithmetic-related networks of each group. Compared to controls, the AMC's positively arithmetic-related network was less located in the control module, and the inter-module connections between somatomotor-default and somatomotor-control modules shifted to somatomotor-visual and somatomotor-dorsal attention modules. Furthermore, the positive network of the AMC group exhibited a segregated connectivity pattern, with more intra-module connections than the control group. Overall, our results suggested that finger motor representation with motor module involvement facilitated arithmetic-related network segregation, reflecting increased autonomy of AMC, thus reducing the dependency of arithmetic on higher-order cognitive functions.
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Affiliation(s)
- Yanjie Wang
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou 310058, China
| | - Yi Zhang
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou 310058, China
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310058, China
| | - Tianyong Xu
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou 310058, China
| | - Xiao Han
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou 310058, China
| | - Xuelian Ge
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou 310058, China
| | - Feiyan Chen
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou 310058, China
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35
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Yang 杨炀 Y, Li 李君君 J, Zhao 赵恺 K, Tam F, Graham SJ, Xu 徐敏 M, Zhou 周可 K. Lateralized Functional Connectivity of the Sensorimotor Cortex and its Variations During Complex Visuomotor Tasks. J Neurosci 2024; 44:e0723232023. [PMID: 38050101 PMCID: PMC10860583 DOI: 10.1523/jneurosci.0723-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 11/10/2023] [Accepted: 11/19/2023] [Indexed: 12/06/2023] Open
Abstract
Previous studies have shown that the left hemisphere dominates motor function, often observed through homotopic activation measurements. Using a functional connectivity approach, this study investigated the lateralization of the sensorimotor cortex during handwriting and drawing, two complex visuomotor tasks with varying contextual demands. We found that both left- and right-lateralized connectivity in the primary motor cortex (M1), dorsal premotor cortex (PMd), somatosensory cortex, and visual regions were evident in adults (males and females), primarily in an interhemispheric integrative fashion. Critically, these lateralization tendencies remained highly invariant across task contexts, representing a task-invariant neural architecture for encoding fundamental motor programs consistently implemented in different task contexts. Additionally, the PMd exhibited a slight variation in lateralization degree between task contexts, reflecting the ability of the high-order motor system to adapt to varying task demands. However, connectivity-based lateralization of the sensorimotor cortex was not detected in 10-year-old children (males and females), suggesting that the maturation of connectivity-based lateralization requires prolonged development. In summary, this study demonstrates both task-invariant and task-sensitive connectivity lateralization in sensorimotor cortices that support the resilience and adaptability of skilled visuomotor performance. These findings align with the hierarchical organization of the motor system and underscore the significance of the functional connectivity-based approach in studying functional lateralization.
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Affiliation(s)
- Yang Yang 杨炀
- CAS Key Laboratory of Behavioral Science, Center for Brain Science and Learning Difficulties, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Junjun Li 李君君
- CAS Key Laboratory of Behavioral Science, Center for Brain Science and Learning Difficulties, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kai Zhao 赵恺
- Institute of Brain Trauma and Neurology, Pingjin Hospital, Characteristic Medical Center of Chinese People's Armed Police Force, Tianjin 300300, China
| | - Fred Tam
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario M4N 3M5, Canada
| | - Simon J Graham
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario M4N 3M5, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada
| | - Min Xu 徐敏
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Shenzhen University, Shenzhen 518060, China
| | - Ke Zhou 周可
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing 100875, China
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36
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Canal-Garcia A, Veréb D, Mijalkov M, Westman E, Volpe G, Pereira JB. Dynamic multilayer functional connectivity detects preclinical and clinical Alzheimer's disease. Cereb Cortex 2024; 34:bhad542. [PMID: 38212285 PMCID: PMC10839846 DOI: 10.1093/cercor/bhad542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 01/13/2024] Open
Abstract
Increasing evidence suggests that patients with Alzheimer's disease present alterations in functional connectivity but previous results have not always been consistent. One of the reasons that may account for this inconsistency is the lack of consideration of temporal dynamics. To address this limitation, here we studied the dynamic modular organization on resting-state functional magnetic resonance imaging across different stages of Alzheimer's disease using a novel multilayer brain network approach. Participants from preclinical and clinical Alzheimer's disease stages were included. Temporal multilayer networks were used to assess time-varying modular organization. Logistic regression models were employed for disease stage discrimination, and partial least squares analyses examined associations between dynamic measures with cognition and pathology. Temporal multilayer functional measures distinguished all groups, particularly preclinical stages, overcoming the discriminatory power of risk factors such as age, sex, and APOE ϵ4 carriership. Dynamic multilayer functional measures exhibited strong associations with cognition as well as amyloid and tau pathology. Dynamic multilayer functional connectivity shows promise as a functional imaging biomarker for both early- and late-stage Alzheimer's disease diagnosis.
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Affiliation(s)
- Anna Canal-Garcia
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm 17165, Sweden
| | - Dániel Veréb
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm 17165, Sweden
| | - Mite Mijalkov
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm 17165, Sweden
| | - Eric Westman
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm 17165, Sweden
| | - Giovanni Volpe
- Department of Physics, University of Gothenburg, Gothenburg 40530, Sweden
| | - Joana B Pereira
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm 17165, Sweden
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37
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Yeon J, Larson AS, Rahnev D, D'Esposito M. Task learning is subserved by a domain-general brain network. Cereb Cortex 2024; 34:bhae013. [PMID: 38282457 DOI: 10.1093/cercor/bhae013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 01/04/2023] [Accepted: 01/06/2023] [Indexed: 01/30/2024] Open
Abstract
One of the most important human faculties is the ability to acquire not just new memories but the capacity to perform entirely new tasks. However, little is known about the brain mechanisms underlying the learning of novel tasks. Specifically, it is unclear to what extent learning of different tasks depends on domain-general and/or domain-specific brain mechanisms. Here human subjects (n = 45) learned to perform 6 new tasks while undergoing functional MRI. The different tasks required the engagement of perceptual, motor, and various cognitive processes related to attention, expectation, speed-accuracy tradeoff, and metacognition. We found that a bilateral frontoparietal network was more active during the initial compared with the later stages of task learning, and that this effect was stronger for task variants requiring more new learning. Critically, the same frontoparietal network was engaged by all 6 tasks, demonstrating its domain generality. Finally, although task learning decreased the overall activity in the frontoparietal network, it increased the connectivity strength between the different nodes of that network. These results demonstrate the existence of a domain-general brain network whose activity and connectivity reflect learning for a variety of new tasks, and thus may underlie the human capacity for acquiring new abilities.
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Affiliation(s)
- Jiwon Yeon
- School of Psychology, Georgia Institute of Technology, Atlanta, GA 30332, United States
- Department of Psychology, Stanford University, Stanford, CA, 94305, United States
| | - Alina Sue Larson
- Department of Psychology, University of California, Santa Cruz, CA 90564, United States
| | - Dobromir Rahnev
- School of Psychology, Georgia Institute of Technology, Atlanta, GA 30332, United States
| | - Mark D'Esposito
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, 94720, United States
- Department of Psychology, University of California, Berkeley, CA, 94720, United States
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38
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Li L, Chen Z, Zhang L, Zhang M, Liu H, Wu D, Ren P, Zhang Z. Dynamic reconfiguration of brain coactivation states that underlying working memory correlates with cognitive decline in clinically unimpaired older adults. Cereb Cortex 2024; 34:bhad546. [PMID: 38244565 DOI: 10.1093/cercor/bhad546] [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: 11/24/2023] [Revised: 12/25/2023] [Accepted: 12/27/2023] [Indexed: 01/22/2024] Open
Abstract
Impairments in working memory (WM) are evident in both clinically diagnosed patients with mild cognitive decline and older adults at risk, as indicated by lower scores on neuropsychological tests. Examining the WM-related neural signatures in at-risk older adults becomes essential for timely intervention. WM functioning relies on dynamic brain activities, particularly within the frontoparietal system. However, it remains unclear whether the cognitive decline would be reflected in the decreased dynamic reconfiguration of brain coactivation states during WM tasks. We enrolled 47 older adults and assessed their cognitive function using the Montreal Cognitive Assessment. The temporal dynamics of brain coactivations during a WM task were investigated through graph-based time-frame modularity analysis. Four primary recurring states emerged: two task-positive states with positive activity in the frontoparietal system (dorsal attention and central executive); two task-negative states with positive activity in the default mode network accompanied by negative activity in the frontoparietal networks. Heightened WM load was associated with increased flexibility of the frontoparietal networks, but the cognitive decline was correlated with reduced capacity for neuroplastic changes in response to increased task demands. These findings advance our understanding of aberrant brain reconfiguration linked to cognitive decline, potentially aiding early identification of at-risk individuals.
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Affiliation(s)
- Linling Li
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, International Health Science Innovation Center, Shenzhen University Medical School, Shenzhen University, 1066 Xueyuan Avenue, Nanshan District, Shenzhen, 518055, China
| | - Zaili Chen
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, International Health Science Innovation Center, Shenzhen University Medical School, Shenzhen University, 1066 Xueyuan Avenue, Nanshan District, Shenzhen, 518055, China
- Minzu Normal University of Xingyi, No. 1 Xingyi Road, Mulong Street, Xingyi, Guizhou, 562400, China
| | - Li Zhang
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, International Health Science Innovation Center, Shenzhen University Medical School, Shenzhen University, 1066 Xueyuan Avenue, Nanshan District, Shenzhen, 518055, China
| | - Min Zhang
- Department of Computer Science and Technology, Harbin Institute of Technology, HIT Campus of University Town of Shenzhen, NanShan District, Shenzhen, 518055, China
| | - Honghai Liu
- State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, HIT Campus of University Town of Shenzhen, NanShan District, Shenzhen, 518055, China
| | - Donghui Wu
- Department of Geriatric Psychiatry, Shenzhen Mental Health Center/Shenzhen Kangning Hospital, 1080 Cuizhu Road, Luohu District, Shenzhen, 518003, China
| | - Ping Ren
- Department of Geriatric Psychiatry, Shenzhen Mental Health Center/Shenzhen Kangning Hospital, 1080 Cuizhu Road, Luohu District, Shenzhen, 518003, China
| | - Zhiguo Zhang
- Department of Computer Science and Technology, Harbin Institute of Technology, HIT Campus of University Town of Shenzhen, NanShan District, Shenzhen, 518055, China
- Peng Cheng Laboratory, 2 Xingke 1st Street, Nanshan District, Shenzhen, 518055, China
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39
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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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40
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Xu T, Wu Y, Zhang Y, Zuo XN, Chen F, Zhou C. Reshaping the Cortical Connectivity Gradient by Long-Term Cognitive Training During Development. Neurosci Bull 2024; 40:50-64. [PMID: 37715923 PMCID: PMC10774512 DOI: 10.1007/s12264-023-01108-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 06/01/2023] [Indexed: 09/18/2023] Open
Abstract
The organization of the brain follows a topological hierarchy that changes dynamically during development. However, it remains unknown whether and how cognitive training administered over multiple years during development can modify this hierarchical topology. By measuring the brain and behavior of school children who had carried out abacus-based mental calculation (AMC) training for five years (starting from 7 years to 12 years old) in pre-training and post-training, we revealed the reshaping effect of long-term AMC intervention during development on the brain hierarchical topology. We observed the development-induced emergence of the default network, AMC training-promoted shifting, and regional changes in cortical gradients. Moreover, the training-induced gradient changes were located in visual and somatomotor areas in association with the visuospatial/motor-imagery strategy. We found that gradient-based features can predict the math ability within groups. Our findings provide novel insights into the dynamic nature of network recruitment impacted by long-term cognitive training during development.
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Affiliation(s)
- Tianyong Xu
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou, 310027, China
| | - Yunying Wu
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, 310027, China
| | - Yi Zhang
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou, 310027, China
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Feiyan Chen
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou, 310027, China.
- Zhejiang Province Key Laboratory of Quantum Technology and Devices, Zhejiang University, Hangzhou, 310027, China.
| | - Changsong Zhou
- Bio-X Laboratory, School of Physics, Zhejiang University, Hangzhou, 310027, China.
- Zhejiang Province Key Laboratory of Quantum Technology and Devices, Zhejiang University, Hangzhou, 310027, China.
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong, 999077, China.
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41
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Shafiei SB, Shadpour S, Mohler JL, Sasangohar F, Gutierrez C, Seilanian Toussi M, Shafqat A. Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms. J Robot Surg 2023; 17:2963-2971. [PMID: 37864129 PMCID: PMC10678814 DOI: 10.1007/s11701-023-01722-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 08/19/2023] [Indexed: 10/22/2023]
Abstract
The aim of this study was to develop machine learning classification models using electroencephalogram (EEG) and eye-gaze features to predict the level of surgical expertise in robot-assisted surgery (RAS). EEG and eye-gaze data were recorded from 11 participants who performed cystectomy, hysterectomy, and nephrectomy using the da Vinci robot. Skill level was evaluated by an expert RAS surgeon using the modified Global Evaluative Assessment of Robotic Skills (GEARS) tool, and data from three subtasks were extracted to classify skill levels using three classification models-multinomial logistic regression (MLR), random forest (RF), and gradient boosting (GB). The GB algorithm was used with a combination of EEG and eye-gaze data to classify skill levels, and differences between the models were tested using two-sample t tests. The GB model using EEG features showed the best performance for blunt dissection (83% accuracy), retraction (85% accuracy), and burn dissection (81% accuracy). The combination of EEG and eye-gaze features using the GB algorithm improved the accuracy of skill level classification to 88% for blunt dissection, 93% for retraction, and 86% for burn dissection. The implementation of objective skill classification models in clinical settings may enhance the RAS surgical training process by providing objective feedback about performance to surgeons and their teachers.
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Affiliation(s)
- Somayeh B Shafiei
- Intelligent Cancer Care Laboratory, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA.
| | - Saeed Shadpour
- Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - James L Mohler
- Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA
| | - Farzan Sasangohar
- Mike and Sugar Barnes Faculty Fellow II, Wm Michael Barnes and Department of Industrial and Systems Engineering at Texas A&M University, College Station, TX, 77843, USA
| | - Camille Gutierrez
- Obstetrics and Gynecology Residency Program, Sisters of Charity Health System, Buffalo, NY, 14214, USA
| | - Mehdi Seilanian Toussi
- Intelligent Cancer Care Laboratory, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA
| | - Ambreen Shafqat
- Intelligent Cancer Care Laboratory, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA
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42
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Papadaki E, Koustakas T, Werner A, Lindenberger U, Kühn S, Wenger E. Resting-state functional connectivity in an auditory network differs between aspiring professional and amateur musicians and correlates with performance. Brain Struct Funct 2023; 228:2147-2163. [PMID: 37792073 PMCID: PMC10587189 DOI: 10.1007/s00429-023-02711-1] [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: 01/17/2023] [Accepted: 09/10/2023] [Indexed: 10/05/2023]
Abstract
Auditory experience-dependent plasticity is often studied in the domain of musical expertise. Available evidence suggests that years of musical practice are associated with structural and functional changes in auditory cortex and related brain regions. Resting-state functional magnetic resonance imaging (MRI) can be used to investigate neural correlates of musical training and expertise beyond specific task influences. Here, we compared two groups of musicians with varying expertise: 24 aspiring professional musicians preparing for their entrance exam at Universities of Arts versus 17 amateur musicians without any such aspirations but who also performed music on a regular basis. We used an interval recognition task to define task-relevant brain regions and computed functional connectivity and graph-theoretical measures in this network on separately acquired resting-state data. Aspiring professionals performed significantly better on all behavioral indicators including interval recognition and also showed significantly greater network strength and global efficiency than amateur musicians. Critically, both average network strength and global efficiency were correlated with interval recognition task performance assessed in the scanner, and with an additional measure of interval identification ability. These findings demonstrate that task-informed resting-state fMRI can capture connectivity differences that correspond to expertise-related differences in behavior.
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Affiliation(s)
- Eleftheria Papadaki
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany.
- International Max Planck Research School on the Life Course (LIFE), Berlin, Germany.
| | - Theodoros Koustakas
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany
| | - André Werner
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany, London, UK
| | - Simone Kühn
- Lise Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany
- Neuronal Plasticity Working Group, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Elisabeth Wenger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany
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Coronel-Oliveros C, Medel V, Orellana S, Rodiño J, Lehue F, Cruzat J, Tagliazucchi E, Brzezicka A, Orio P, Kowalczyk-Grębska N, Ibáñez A. Gaming expertise induces meso-scale brain plasticity and efficiency mechanisms as revealed by whole-brain modeling Gaming expertise, neuroplasticity and functional dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.21.554072. [PMID: 38077041 PMCID: PMC10705274 DOI: 10.1101/2023.08.21.554072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Video games are a valuable tool for studying the effects of training and neural plasticity on the brain. However, the underlaying mechanisms related to plasticity-induced brain structural changes and their impact in brain dynamics are unknown. Here, we used a semi-empirical whole-brain model to study structural neural plasticity mechanisms linked to video game expertise. We hypothesized that video game expertise is associated with neural plasticity-mediated changes in structural connectivity that manifest at the meso-scale level, resulting in a more segregated functional network topology. To test this hypothesis, we combined structural connectivity data of StarCraft II video game players (VGPs, n = 31) and non-players (NVGPs, n = 31), with generic fMRI data from the Human Connectome Project and computational models, with the aim of generating simulated fMRI recordings. Graph theory analysis on simulated data was performed during both resting-state conditions and external stimulation. VGPs' simulated functional connectivity was characterized by a meso-scale integration, with increased local connectivity in frontal, parietal and occipital brain regions. The same analyses at the level of structural connectivity showed no differences between VGPs and NVGPs. Regions that increased their connectivity strength in VGPs are known to be involved in cognitive processes crucial for task performance such as attention, reasoning, and inference. In-silico stimulation suggested that differences in FC between VGPs and NVGPs emerge in noisy contexts, specifically when the noisy level of stimulation is increased. This indicates that the connectomes of VGPs may facilitate the filtering of noise from stimuli. These structural alterations drive the meso-scale functional changes observed in individuals with gaming expertise. Overall, our work sheds light into the mechanisms underlying structural neural plasticity triggered by video game experiences.
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Affiliation(s)
- Carlos Coronel-Oliveros
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Diagonal Las Torres 2640, Penalolen, Santiago (Chile)
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California US and Trinity College Dublin, Ireland
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Harrington 287, Playa Ancha, Valparaíso (Chile)
| | - Vicente Medel
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Diagonal Las Torres 2640, Penalolen, Santiago (Chile)
- Brain and Mind Centre, The University of Sydney, 94 Mallett St, Camperdown NSW 2050 (Australia)
- Department of Neuroscience, Universidad de Chile, Independencia 1027, Independencia, Santiago (Chile)
| | - Sebastián Orellana
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Harrington 287, Playa Ancha, Valparaíso (Chile)
| | - Julio Rodiño
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Harrington 287, Playa Ancha, Valparaíso (Chile)
- Brain Dynamics Laboratory, Facultad de Ingeniería, Universidad de Valparaíso, General Cruz 222, Valparaíso (Chile)
| | - Fernando Lehue
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Harrington 287, Playa Ancha, Valparaíso (Chile)
| | - Josephine Cruzat
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Diagonal Las Torres 2640, Penalolen, Santiago (Chile)
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Diagonal Las Torres 2640, Penalolen, Santiago (Chile)
- Buenos Aires Physics Institute and Physics Department, University of Buenos Aires, Intendente Güiraldes 2160 - Ciudad Universitaria, Buenos Aires (Argentina)
| | - Aneta Brzezicka
- Faculty of Psychology, SWPS University of Social Sciences and Humanities, Chodakowska 19/31, 03-815 Warsaw (Poland)
| | - Patricio Orio
- Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Harrington 287, Playa Ancha, Valparaíso (Chile)
- Instituto de Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Gran Bretaña 1091, Playa Ancha, Valparaíso (Chile)
| | - Natalia Kowalczyk-Grębska
- Faculty of Psychology, SWPS University of Social Sciences and Humanities, Chodakowska 19/31, 03-815 Warsaw (Poland)
| | - Agustín Ibáñez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Diagonal Las Torres 2640, Penalolen, Santiago (Chile)
- Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California US and Trinity College Dublin, Ireland
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Vito Dumas 284, Provincia de Buenos Aires (Argentina)
- Trinity College Institute of Neuroscience, Trinity College Dublin, Lloyd Building, Dublin 2 (Ireland)
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Ragothaman A, Mancini M, Nutt JG, Wang J, Fair DA, Horak FB, Miranda-Dominguez O. Motor networks, but also non-motor networks predict motor signs in Parkinson's disease. Neuroimage Clin 2023; 40:103541. [PMID: 37972450 PMCID: PMC10685308 DOI: 10.1016/j.nicl.2023.103541] [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: 06/06/2023] [Revised: 10/31/2023] [Accepted: 11/10/2023] [Indexed: 11/19/2023]
Abstract
OBJECTIVE Investigate the brain functional networks associated with motor impairment in people with Parkinson's disease (PD). BACKGROUND PD is primarily characterized by motor dysfunction. Resting-state functional connectivity (RsFC) offers a unique opportunity to non-invasively characterize brain function. In this study, we hypothesized that the motor dysfunction observed in people with PD involves atypical connectivity not only in motor but also in higher-level attention networks. Understanding the interaction between motor and non-motor RsFC that are related to the motor signs could provide insights into PD pathophysiology. METHODS We used data from 88 people with PD (mean age: 68.2(SD:10), 55 M/33F) coming from 2 cohorts. Motor severity was assessed in practical OFF-medication state, using MDS-UPDRS Part-III motor scores (mean: 49 (SD:10)). RsFC was characterized using an atlas of 384 regions that were grouped into 13 functional networks. Associations between RsFC and motor severity were assessed independently for each RsFC using predictive modeling. RESULTS The top 5 % models that predicted the MDS-UPDRS-III motor scores with effect size >0.5 were the connectivity between (1) the somatomotor and Subcortical-Basal-ganglia, (2) somatomotor and Visual and (3) CinguloOpercular (CiO) and language/Ventral attention (Lan/VeA) network pairs. DISCUSSION Our findings suggest that, along with motor networks, visual- and attention-related cortical networks are also associated with the motor symptoms of PD. Non-motor networks may be involved indirectly in motor-coordination. When people with PD have deficits in motor networks, more attention may be needed to carry out formerly automatic motor functions, consistent with compensatory mechanisms in parkinsonian movement disorders.
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Affiliation(s)
| | - Martina Mancini
- Department of Neurology, Oregon Health and Science University, Portland, OR 97239, USA
| | - John G Nutt
- Department of Neurology, Oregon Health and Science University, Portland, OR 97239, USA
| | - Junping Wang
- Department of Radiology, Tianjin Medical University General Hospital, China
| | - Damien A Fair
- Masonic Institute for the Developing Brain (MIDB), University of Minnesota, Minneapolis, MN 55455, USA; Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN 55455, USA; Department of Pediatrics, University of Minnesota Medical School, University of Minnesota, Minneapolis, MN 55455, USA; Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97239, USA
| | - Fay B Horak
- Department of Neurology, Oregon Health and Science University, Portland, OR 97239, USA; Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97239, USA.
| | - Oscar Miranda-Dominguez
- Masonic Institute for the Developing Brain (MIDB), University of Minnesota, Minneapolis, MN 55455, USA; Department of Pediatrics, University of Minnesota Medical School, University of Minnesota, Minneapolis, MN 55455, USA
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45
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Finn ES, Poldrack RA, Shine JM. Functional neuroimaging as a catalyst for integrated neuroscience. Nature 2023; 623:263-273. [PMID: 37938706 DOI: 10.1038/s41586-023-06670-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 09/22/2023] [Indexed: 11/09/2023]
Abstract
Functional magnetic resonance imaging (fMRI) enables non-invasive access to the awake, behaving human brain. By tracking whole-brain signals across a diverse range of cognitive and behavioural states or mapping differences associated with specific traits or clinical conditions, fMRI has advanced our understanding of brain function and its links to both normal and atypical behaviour. Despite this headway, progress in human cognitive neuroscience that uses fMRI has been relatively isolated from rapid advances in other subdomains of neuroscience, which themselves are also somewhat siloed from one another. In this Perspective, we argue that fMRI is well-placed to integrate the diverse subfields of systems, cognitive, computational and clinical neuroscience. We first summarize the strengths and weaknesses of fMRI as an imaging tool, then highlight examples of studies that have successfully used fMRI in each subdomain of neuroscience. We then provide a roadmap for the future advances that will be needed to realize this integrative vision. In this way, we hope to demonstrate how fMRI can help usher in a new era of interdisciplinary coherence in neuroscience.
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Affiliation(s)
- Emily S Finn
- Department of Psychological and Brain Sciences, Dartmouth College, Dartmouth, NH, USA.
| | | | - James M Shine
- School of Medical Sciences, University of Sydney, Sydney, New South Wales, Australia.
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46
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Shafiei SB, Shadpour S, Intes X, Rahul R, Toussi MS, Shafqat A. Performance and learning rate prediction models development in FLS and RAS surgical tasks using electroencephalogram and eye gaze data and machine learning. Surg Endosc 2023; 37:8447-8463. [PMID: 37730852 PMCID: PMC10615961 DOI: 10.1007/s00464-023-10409-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 08/14/2023] [Indexed: 09/22/2023]
Abstract
OBJECTIVE This study explored the use of electroencephalogram (EEG) and eye gaze features, experience-related features, and machine learning to evaluate performance and learning rates in fundamentals of laparoscopic surgery (FLS) and robotic-assisted surgery (RAS). METHODS EEG and eye-tracking data were collected from 25 participants performing three FLS and 22 participants performing two RAS tasks. Generalized linear mixed models, using L1-penalized estimation, were developed to objectify performance evaluation using EEG and eye gaze features, and linear models were developed to objectify learning rate evaluation using these features and performance scores at the first attempt. Experience metrics were added to evaluate their role in learning robotic surgery. The differences in performance across experience levels were tested using analysis of variance. RESULTS EEG and eye gaze features and experience-related features were important for evaluating performance in FLS and RAS tasks with reasonable results. Residents outperformed faculty in FLS peg transfer (p value = 0.04), while faculty and residents both excelled over pre-medical students in the FLS pattern cut (p value = 0.01 and p value < 0.001, respectively). Fellows outperformed pre-medical students in FLS suturing (p value = 0.01). In RAS tasks, both faculty and fellows surpassed pre-medical students (p values for the RAS pattern cut were 0.001 for faculty and 0.003 for fellows, while for RAS tissue dissection, the p value was less than 0.001 for both groups), with residents also showing superior skills in tissue dissection (p value = 0.03). CONCLUSION Findings could be used to develop training interventions for improving surgical skills and have implications for understanding motor learning and designing interventions to enhance learning outcomes.
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Affiliation(s)
- Somayeh B Shafiei
- Intelligent Cancer Care Laboratory, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA.
| | | | - Xavier Intes
- Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY, 12180, USA
| | - Rahul Rahul
- Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY, 12180, USA
| | - Mehdi Seilanian Toussi
- Intelligent Cancer Care Laboratory, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA
| | - Ambreen Shafqat
- Intelligent Cancer Care Laboratory, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA
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47
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Li J, Huang M, Cao Y, Qin Z, Lang J. Long-term Intensive Soccer Training Induced Dynamic Reconfiguration of Brain Network. Neuroscience 2023; 530:133-143. [PMID: 37640136 DOI: 10.1016/j.neuroscience.2023.08.020] [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: 04/26/2023] [Revised: 08/08/2023] [Accepted: 08/10/2023] [Indexed: 08/31/2023]
Abstract
Long-term motor skill learning has been shown to impact the functional plasticity of the brain. Athletes, as a unique population, exhibit remarkable adaptive changes in the static properties of their brain networks. However, studying the differences between expert and novice athletes using a dynamic brain network framework can provide a fresh perspective on how motor skill learning affects the functional organization of the brain. In this study, we investigated the dynamic properties of brain networks in expert and novice soccer players at the whole-brain, network, and region-based levels. Our findings revealed that expert soccer players displayed reduced integration and increased segregation at the whole-brain level. As for network level, experts exhibited increased segregation and reduced flexibility in the visual network, enhanced integration between the visual and ventral attention networks, and decreased integration in the subcortical-sensorimotor and subcortical-cerebellar networks. Additionally, specific brain regions within the visual network exhibited greater recruitment in expert soccer players compared to novices at the nodal level. Furthermore, classification analyses demonstrated the critical role played by the visual network in the classification process. In conclusion, our study provides new insights into the dynamic properties of brain networks in expert and novice soccer players, and suggests that reduced integration and increased segregation in the visual network may be neuroimaging marker that distinguish expert soccer players from novices. Our findings may have implications for the training and development of athletes and advance our understanding of how motor skill learning affects brain functional organization.
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Affiliation(s)
- Ju Li
- College of P.E. and Sports, Beijing Normal University, Beijing 100875, China.
| | - Minghao Huang
- College of P.E. and Sports, Beijing Normal University, Beijing 100875, China.
| | - Yaping Cao
- College of P.E. and Sports, Beijing Normal University, Beijing 100875, China.
| | - Zhe Qin
- College of P.E. and Sports, Northwest Normal University, Gansu 730070, China.
| | - Jian Lang
- College of P.E. and Sports, Beijing Normal University, Beijing 100875, China.
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48
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Shadpour S, Shafqat A, Toy S, Jing Z, Attwood K, Moussavi Z, Shafiei SB. Developing cognitive workload and performance evaluation models using functional brain network analysis. NPJ AGING 2023; 9:22. [PMID: 37803137 PMCID: PMC10558559 DOI: 10.1038/s41514-023-00119-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 08/10/2023] [Indexed: 10/08/2023]
Abstract
Cognition, defined as the ability to learn, remember, sustain attention, make decisions, and solve problems, is essential in daily activities and in learning new skills. The purpose of this study was to develop cognitive workload and performance evaluation models using features that were extracted from Electroencephalogram (EEG) data through functional brain network and spectral analyses. The EEG data were recorded from 124 brain areas of 26 healthy participants conducting two cognitive tasks on a robot simulator. The functional brain network and Power Spectral Density features were extracted from EEG data using coherence and spectral analyses, respectively. Participants reported their perceived cognitive workload using the SURG-TLX questionnaire after each exercise, and the simulator generated actual performance scores. The extracted features, actual performance scores, and subjectively assessed cognitive workload values were used to develop linear models for evaluating performance and cognitive workload. Furthermore, the Pearson correlation was used to find the correlation between participants' age, performance, and cognitive workload. The findings demonstrated that combined EEG features retrieved from spectral analysis and functional brain networks can be used to evaluate cognitive workload and performance. The cognitive workload in conducting only Matchboard level 3, which is more challenging than Matchboard level 2, was correlated with age (0.54, p-value = 0.01). This finding may suggest playing more challenging computer games are more helpful in identifying changes in cognitive workload caused by aging. The findings could open the door for a new era of objective evaluation and monitoring of cognitive workload and performance.
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Affiliation(s)
- Saeed Shadpour
- Department of Animal Biosciences, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
| | - Ambreen Shafqat
- Intelligent Cancer Care Laboratory, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA
| | - Serkan Toy
- Department of Basic Science Education, Virginia Tech Carilion School of Medicine, Roanoke, VA, 24016, USA
| | - Zhe Jing
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA
| | - Kristopher Attwood
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA
| | - Zahra Moussavi
- Department of Electrical and Computer Engineering & Biomedical Engineering Program and Department of Psychiatry, University of Manitoba, Winnipeg, Manitoba, R3T 5V6, Canada
| | - Somayeh B Shafiei
- Intelligent Cancer Care Laboratory, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA.
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Liu Z, Han F, Wang Q. Task-relevant brain dynamics among cognitive subsystems induced by regional stimulation in a whole-brain computational model. Phys Rev E 2023; 108:044402. [PMID: 37978611 DOI: 10.1103/physreve.108.044402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 09/11/2023] [Indexed: 11/19/2023]
Abstract
Cognition involves the global integration of distributed brain regions that are known to work cohesively as cognitive subsystems during brain functioning. Empirical evidence has suggested that spatiotemporal phase relationships between brain regions, measured as synchronization and metastability, may encode important task-relevant information. However, it remains largely unknown how phase relationships aggregate at the level of cognitive subsystems under different cognitive processing. Here, we probe this question by simulating task-relevant brain dynamics through regional stimulation of a whole-brain dynamical network model operating in the resting-state dynamical regime. The model is constructed with structurally embedded Stuart-Laudon oscillators and then fitted with human resting-state functional magnetic resonance imaging data. Based on this framework, we first demonstrate the plausibility of introducing the cognitive system partition into the modeling analysis framework by showing that the clustering of regions across functional networks is better circumscribed by the predefined partition. At the cognitive subsystem level, we focus on how task-relevant phase dynamics are organized in terms of synchronization and metastability. We found that patterns of cognitive synchronization are more task specific, whereas patterns of cognitive metastability are more consistent across different states, suggesting it may encode a more task-general property during cognitive processing, an inherent property conferred by brain organization. This consistent network architecture in cognitive metastability may be related to the distinct functional responses of realistic cognitive systems. We also provide empirical evidence to partially support our computational results. Our paper may provide insights for the mechanisms underlying task-relevant brain dynamics, and establish a model-based link between brain structure, dynamics, and cognition, a fundamental step for computationally aided brain interventions.
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Affiliation(s)
- Zilu Liu
- Department of Dynamics and Control, Beihang University, Beijing 100191, China
| | - Fang Han
- College of Information Science and Technology, Donghua University, Shanghai 200051, China
| | - Qingyun Wang
- Department of Dynamics and Control, Beihang University, Beijing 100191, China
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50
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Uddin LQ, Betzel RF, Cohen JR, Damoiseaux JS, De Brigard F, Eickhoff SB, Fornito A, Gratton C, Gordon EM, Laird AR, Larson-Prior L, McIntosh AR, Nickerson LD, Pessoa L, Pinho AL, Poldrack RA, Razi A, Sadaghiani S, Shine JM, Yendiki A, Yeo BTT, Spreng RN. Controversies and progress on standardization of large-scale brain network nomenclature. Netw Neurosci 2023; 7:864-905. [PMID: 37781138 PMCID: PMC10473266 DOI: 10.1162/netn_a_00323] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 05/10/2023] [Indexed: 10/03/2023] Open
Abstract
Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.
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Affiliation(s)
- Lucina Q. Uddin
- Department of Psychiatry and Biobehavioral Sciences and Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Jessica R. Cohen
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, USA
| | - Jessica S. Damoiseaux
- Institute of Gerontology and Department of Psychology, Wayne State University, Detroit, MI, USA
| | | | - Simon B. Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Evan M. Gordon
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA
| | - Angela R. Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | - Linda Larson-Prior
- Deptartment of Psychiatry and Department of Neurobiology and Developmental Sciences, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - A. Randal McIntosh
- Institute for Neuroscience and Neurotechnology, Simon Fraser University, Vancouver, BC, Canada
| | | | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Ana Luísa Pinho
- Brain and Mind Institute, Western University, London, Ontario, Canada
| | | | - Adeel Razi
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Sepideh Sadaghiani
- Department of Psychology, University of Illinois, Urbana Champaign, IL, USA
| | - James M. Shine
- Brain and Mind Center, University of Sydney, Sydney, Australia
| | - Anastasia Yendiki
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - B. T. Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - R. Nathan Spreng
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
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