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Zhang J, Wu K, Dong J, Feng J, Yu L. Modeling the interplay between regional heterogeneity and critical dynamics underlying brain functional networks. Neural Netw 2025; 184:107100. [PMID: 39740389 DOI: 10.1016/j.neunet.2024.107100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 10/03/2024] [Accepted: 12/23/2024] [Indexed: 01/02/2025]
Abstract
The human brain exhibits heterogeneity across regions and network connectivity patterns; However, how these heterogeneities contribute to whole-brain network functions and cognitive capacities remains unclear. In this study, we focus on the regional heterogeneity reflected in local dynamics and study how it contributes to the emergence of functional connectivity patterns, network ignition dynamics of the empirical brains. We find that the level of synchrony among voxelwise neural activities measured from the fMRI data is significantly correlated with the transcriptional variations in excitatory and inhibitory receptor gene expression. Consequently, we construct heterogeneous whole-brain network models with nodal excitability calibrated by the synchronization measure of regional dynamics. We demonstrate that as the extent of heterogeneity increases, the models operating around the critical point between order and disorder generate simulated functional connectivity networks increasingly similar to empirical resting-state or working memory task-evoked function connectivity networks. Furthermore, the heterogeneous models can predict individual differences in resting-state and task-evoked reconfiguration of the functional connectivity, as well as the comparative causal effect of empirical brain networks-that is, how the dynamics of one brain region affect whole-brain synchronization. Overall, this work demonstrates the viability of using regional heterogeneous functional signals to improve the performance of the whole-brain models, and illustrates how regional heterogeneity in human brains interplays with structural connections and critical dynamics to contribute to the emergence of functional connectivity networks.
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Affiliation(s)
- Jijin Zhang
- School of Physical Science and Technology, Lanzhou Center for Theoretical Physics, Key Laboratory of Theoretical Physics of Gansu Province, and Key Laboratory of Quantum Theory and Applications of MoE, Lanzhou University, Lanzhou, Gansu 730000, China
| | - Kejian Wu
- School of Physical Science and Technology, Lanzhou Center for Theoretical Physics, Key Laboratory of Theoretical Physics of Gansu Province, and Key Laboratory of Quantum Theory and Applications of MoE, Lanzhou University, Lanzhou, Gansu 730000, China
| | - Jiaqi Dong
- School of Physical Science and Technology, Lanzhou Center for Theoretical Physics, Key Laboratory of Theoretical Physics of Gansu Province, and Key Laboratory of Quantum Theory and Applications of MoE, Lanzhou University, Lanzhou, Gansu 730000, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, 200433, China; Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK; School of Mathematical Sciences, School of Life Science and the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Lianchun Yu
- School of Physical Science and Technology, Lanzhou Center for Theoretical Physics, Key Laboratory of Theoretical Physics of Gansu Province, and Key Laboratory of Quantum Theory and Applications of MoE, Lanzhou University, Lanzhou, Gansu 730000, China.
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Guan S, Zhang Z, Meng C, Biswal B. Multifractal dynamic changes of spontaneous brain activity in psychiatric disorders: Adult attention deficit-hyperactivity disorder, bipolar disorder, and schizophrenia. J Affect Disord 2025; 373:291-305. [PMID: 39765289 DOI: 10.1016/j.jad.2025.01.007] [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: 08/18/2024] [Revised: 12/30/2024] [Accepted: 01/03/2025] [Indexed: 02/06/2025]
Abstract
It is one of the strategies to study the complexity of spontaneous fluctuation of brain neurons based on resting-state functional magnetic resonance imaging (rs-fMRI), but the multifractal characteristics of spontaneous fluctuation of brain neurons in psychiatric diseases need to be studied. Therefore, this paper will study the multifractal spontaneous brain activity changes in psychiatric disorders using the multifractal detrended fluctuation analysis algorithm based on the UCLA datasets. Specifically: (1) multifractal characteristics in adult attention deficit-hyperactivity disorder (ADHD), bipolar disorder (BP), and schizophrenia (SCHZ); (2) the source of those multifractal characteristics. Results showed that for adult ADHD, BP, and SCHZ, all 6 functional brain regions exhibit multifractal characteristics, and the multifractal spectrum shows a reduction in bell-shaped asymmetry, unlike the intensity of healthy control (HC) asymmetry. Besides, compared with HC, the multifractal sources of all functional brain regions were fat-tail probability distribution and the long-range dependence correlation, but the intensity of fat-tail probability distribution was decreased and the long-range dependence correlation was increased. The results provide a reference for further understanding the complexity of spontaneous fluctuation of neurons in psychiatric disorders.
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Affiliation(s)
- Sihai Guan
- College of Electronic and Information, Southwest Minzu University, Chengdu 610041, China; Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Chengdu 610041, China.
| | - Ziwei Zhang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Chun Meng
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Bharat Biswal
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA.
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3
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Yao R, Shi L, Niu Y, Li H, Fan X, Wang B. Driving brain state transitions via Adaptive Local Energy Control Model. Neuroimage 2025; 306:121023. [PMID: 39800170 DOI: 10.1016/j.neuroimage.2025.121023] [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: 10/31/2024] [Revised: 12/30/2024] [Accepted: 01/10/2025] [Indexed: 01/15/2025] Open
Abstract
The brain, as a complex system, achieves state transitions through interactions among its regions and also performs various functions. An in-depth exploration of brain state transitions is crucial for revealing functional changes in both health and pathological states and realizing precise brain function intervention. Network control theory offers a novel framework for investigating the dynamic characteristics of brain state transitions. Existing studies have primarily focused on analyzing the energy required for brain state transitions, which are driven either by the single brain region or by all brain regions. However, they often neglect the critical question of how the whole brain responds to external control inputs that are driven by control energy from multiple brain regions, which limits their application value in guiding clinical neurostimulation. In this paper, we proposed the Adaptive Local Energy Control Model (ALECM) to explore brain state transitions, which considers the complex interactions of the whole brain along the white matter network when external control inputs are applied to multiple regions. It not only quantifies the energy required for state transitions but also predicts their outcomes based on local control. Our results indicated that patients with Schizophrenia (SZ) and Bipolar Disorder (BD) required more energy to drive the brain state transitions from the pathological state to the healthy baseline state, which is defined as Hetero-state transition. Importantly, we successfully induced Hetero-state transition in the patients' brains by using the ALECM, with subnetworks or specific brain regions serving as local control sets. Eventually, the network similarity between patients and healthy subjects reached baseline levels. These offer evidence that the ALECM can effectively quantify the cost characteristics of brain state transitions, providing a theoretical foundation for accurately predicting the efficacy of electromagnetic perturbation therapies in the future.
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Affiliation(s)
- Rong Yao
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030024, China
| | - Langhua Shi
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030024, China
| | - Yan Niu
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030024, China
| | - HaiFang Li
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030024, China
| | - Xing Fan
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030024, China.
| | - Bin Wang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, 030024, China.
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Fu Z, Sui J, Iraji A, Liu J, Calhoun VD. Cognitive and psychiatric relevance of dynamic functional connectivity states in a large (N > 10,000) children population. Mol Psychiatry 2025; 30:402-413. [PMID: 39085394 PMCID: PMC11746149 DOI: 10.1038/s41380-024-02683-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 07/16/2024] [Accepted: 07/24/2024] [Indexed: 08/02/2024]
Abstract
Children's brains dynamically adapt to the stimuli from the internal state and the external environment, allowing for changes in cognitive and mental behavior. In this work, we performed a large-scale analysis of dynamic functional connectivity (DFC) in children aged 9~11 years, investigating how brain dynamics relate to cognitive performance and mental health at an early age. A hybrid independent component analysis framework was applied to the Adolescent Brain Cognitive Development (ABCD) data containing 10,988 children. We combined a sliding-window approach with k-means clustering to identify five brain states with distinct DFC patterns. Interestingly, the occurrence of a strongly connected state with the most within-network synchrony and the anticorrelations between networks, especially between the sensory networks and between the cerebellum and other networks, was negatively correlated with cognitive performance and positively correlated with dimensional psychopathology in children. Meanwhile, opposite relationships were observed for a DFC state showing integration of sensory networks and antagonism between default-mode and sensorimotor networks but weak segregation of the cerebellum. The mediation analysis further showed that attention problems mediated the effect of DFC states on cognitive performance. This investigation unveils the neurological underpinnings of DFC states, which suggests that tracking the transient dynamic connectivity may help to characterize cognitive and mental problems in children and guide people to provide early intervention to buffer adverse influences.
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Affiliation(s)
- Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
- Department of Computer Science, Georgia State University, Atlanta, GA, USA.
| | - Jing Sui
- IDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Jingyu Liu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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Han JC, Zhang C, Cai YD, Li YT, Shang YX, Chen ZH, Yang G, Song JJ, Su D, Bai K, Sun JT, Liu Y, Liu N, Duan Y, Wang W. Neuroimaging features for cognitive fatigue and its recovery with VR intervention: An EEG microstates analysis. Brain Res Bull 2025; 221:111223. [PMID: 39864596 DOI: 10.1016/j.brainresbull.2025.111223] [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: 10/25/2024] [Revised: 01/15/2025] [Accepted: 01/20/2025] [Indexed: 01/28/2025]
Abstract
INTRODUCTION Cognitive fatigue is mainly caused by enduring mental stress or monotonous work, impairing cognitive and physical performance. Natural scene exposure is a promising intervention for relieving cognitive fatigue, but the efficacy of virtual reality (VR) simulated natural scene exposure is unclear. We aimed to investigate the effect of VR natural scene on cognitive fatigue and further explored its underlying neurophysiological alterations with electroencephalogram (EEG) microstates analysis. METHODS Ten participants performed a 20-minute 1-back task before and after VR intervention while EEG was recorded (pre-task, post-task). Performance was measured with mean accuracy rate (MAR) and mean reaction time (MRT) of the continuous 1-back task. VR simulation of the Canal Town scene was utilized to alleviate cognitive fatigue caused by 1-back tasks. Four resting-state phases were identified: beginning, pre, post, and end phases. RESULTS Post-task had a higher MAR and a lower MRT than pre-task. For pre-task, MAR was negatively correlated with trials, while MRT was positively correlated with trials. Four EEG microstates classes (A-D) were identified, and their temporal parameters (mean duration, time coverage and occurrence) and transition probabilities were calculated. After intervention, mean duration and time coverage of class B decreased, all parameters of class C increased, while all parameters of class D decreased. Transition probabilities between classes B and D decreased but increased between classes A and C. CONCLUSION VR simulation of Canal Town scene is a potentially effective method to alleviate cognitive fatigue. Microstate is an electrophysiological trait characteristic of cognitive fatigue and might be used to indicate the effect of VR intervention.
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Affiliation(s)
- Jia-Cheng Han
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, The Fourth Military Medical University, No. 569 Xinsi Road, Xi'an, Shaanxi 710038, China.
| | - Chi Zhang
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, The Fourth Military Medical University, No. 569 Xinsi Road, Xi'an, Shaanxi 710038, China.
| | - Yan-Dong Cai
- School of Aerospace Engineering, Tsinghua University, Beijing 100084, China; Airborne Avionics Flight Test Institute, Chinese Flight Test Establishment, Xi'an, Shaanxi 710089, China.
| | - Yu-Ting Li
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, The Fourth Military Medical University, No. 569 Xinsi Road, Xi'an, Shaanxi 710038, China.
| | - Yu-Xuan Shang
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, The Fourth Military Medical University, No. 569 Xinsi Road, Xi'an, Shaanxi 710038, China.
| | - Zhu-Hong Chen
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, The Fourth Military Medical University, No. 569 Xinsi Road, Xi'an, Shaanxi 710038, China.
| | - Guan Yang
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, The Fourth Military Medical University, No. 569 Xinsi Road, Xi'an, Shaanxi 710038, China.
| | - Jia-Jie Song
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, The Fourth Military Medical University, No. 569 Xinsi Road, Xi'an, Shaanxi 710038, China.
| | - Dan Su
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, The Fourth Military Medical University, No. 569 Xinsi Road, Xi'an, Shaanxi 710038, China.
| | - Ke Bai
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, The Fourth Military Medical University, No. 569 Xinsi Road, Xi'an, Shaanxi 710038, China.
| | - Jing-Ting Sun
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, The Fourth Military Medical University, No. 569 Xinsi Road, Xi'an, Shaanxi 710038, China.
| | - Yu Liu
- Hangzhou Qu'an Technology Co., Ltd, Hangzhou, Zhejiang 310000, China.
| | - Na Liu
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, The Fourth Military Medical University, No. 569 Xinsi Road, Xi'an, Shaanxi 710038, China; Department of Nursing, The Fourth Military Medical University, Xi'an, Shaanxi 710038, China.
| | - Ya Duan
- School of Aerospace Engineering, Tsinghua University, Beijing 100084, China; Airborne Avionics Flight Test Institute, Chinese Flight Test Establishment, Xi'an, Shaanxi 710089, China.
| | - Wen Wang
- Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, The Fourth Military Medical University, No. 569 Xinsi Road, Xi'an, Shaanxi 710038, China.
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Hancock F, Rosas FE, Luppi AI, Zhang M, Mediano PAM, Cabral J, Deco G, Kringelbach ML, Breakspear M, Kelso JAS, Turkheimer FE. Metastability demystified - the foundational past, the pragmatic present and the promising future. Nat Rev Neurosci 2025; 26:82-100. [PMID: 39663408 DOI: 10.1038/s41583-024-00883-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/01/2024] [Indexed: 12/13/2024]
Abstract
Healthy brain function depends on balancing stable integration between brain areas for effective coordinated functioning, with coexisting segregation that allows subsystems to express their functional specialization. Metastability, a concept from the dynamical systems literature, has been proposed as a key signature that characterizes this balance. Building on this principle, the neuroscience literature has leveraged the phenomenon of metastability to investigate various aspects of brain function in health and disease. However, this body of work often uses the notion of metastability heuristically, and sometimes inaccurately, making it difficult to navigate the vast literature, interpret findings and foster further development of theoretical and experimental methodologies. Here, we provide a comprehensive review of metastability and its applications in neuroscience, covering its scientific and historical foundations and the practical measures used to assess it in empirical data. We also provide a critical analysis of recent theoretical developments, clarifying common misconceptions and paving the road for future developments.
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Affiliation(s)
- Fran Hancock
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Fernando E Rosas
- Department of Informatics, University of Sussex, Brighton, UK.
- Sussex Centre for Consciousness Science, University of Sussex, Brighton, UK.
- Centre for Psychedelic Research, Department of Brain Science, Imperial College London, London, UK.
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK.
- Sussex AI, University of Sussex, Brighton, UK.
- Centre for Complexity Science, Department of Brain Science, Imperial College London, London, UK.
| | - Andrea I Luppi
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
- St John's College, University of Cambridge, Cambridge, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Mengsen Zhang
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Pedro A M Mediano
- Department of Computing, Imperial College London, London, UK
- Division of Psychology and Language Sciences, University College London, London, UK
| | - Joana Cabral
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
- Life and Health Sciences Research Institute School of Medicine, University of Minho, Braga, Portugal
| | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Institución Catalana de la Recerca i Estudis Avancats (ICREA), Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- School of Psychological Sciences, Monash University Clayton, Melbourne, Victoria, Australia
| | - Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Michael Breakspear
- School of Psychological Sciences, College of Engineering, Science and the Environment, University of Newcastle, Newcastle, New South Wales, Australia
| | - J A Scott Kelso
- Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL, USA
- Intelligent Systems Research Centre, Ulster University, Derry~Londonderry, Northern Ireland
- The Bath Institute for the Augmented Human, University of Bath, Bath, UK
| | - Federico E Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- The Institute for Human and Synthetic Minds, King's College London, London, UK
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7
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Jedynak M, Troisi Lopez E, Romano A, Jirsa V, David O, Sorrentino P. Intermodal Consistency of Whole-Brain Connectivity and Signal Propagation Delays. Hum Brain Mapp 2025; 46:e70093. [PMID: 39917852 PMCID: PMC11803410 DOI: 10.1002/hbm.70093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 09/28/2024] [Accepted: 11/19/2024] [Indexed: 02/11/2025] Open
Abstract
Measuring propagation of perturbations across the human brain and their transmission delays is critical for network neuroscience, but it is a challenging problem that still requires advancement. Here, we compare results from a recently introduced, noninvasive technique of functional delays estimation from source-reconstructed electro/magnetoencephalography, to the corresponding findings from a large dataset of cortico-cortical evoked potentials estimated from intracerebral stimulations of patients suffering from pharmaco-resistant epilepsies. The two methods yield significantly similar probabilistic connectivity maps and signal propagation delays, in both cases characterized with Pearson correlations greater than 0.5 (when grouping by stimulated parcel is applied for delays). This similarity suggests a correspondence between the mechanisms underpinning the propagation of spontaneously generated scale-free perturbations (i.e., neuronal avalanches observed in resting state activity studied using magnetoencephalography) and the spreading of cortico-cortical evoked potentials. This manuscript provides evidence for the accuracy of the estimate of functional delays obtained noninvasively from reconstructed sources. Conversely, our findings show that estimates obtained from externally induced perturbations in patients capture physiological activities in healthy subjects. In conclusion, this manuscript constitutes a mutual validation between two modalities, broadening their scope of applicability and interpretation. Importantly, the capability to measure delays noninvasively (as per MEG) paves the way for the inclusion of functional delays in personalized large-scale brain models as well as in diagnostic and prognostic algorithms.
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Affiliation(s)
- Maciej Jedynak
- Aix Marseille University, INSERM, INS, Institut de Neurosciences des SystèmesMarseilleFrance
| | - Emahnuel Troisi Lopez
- Institute of Applied Sciences and Intelligent Systems of National Research CouncilPozzuoliItaly
| | - Antonella Romano
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly
| | - Viktor Jirsa
- Aix Marseille University, INSERM, INS, Institut de Neurosciences des SystèmesMarseilleFrance
| | - Olivier David
- Aix Marseille University, INSERM, INS, Institut de Neurosciences des SystèmesMarseilleFrance
- Department of NeurosurgeryFondation Lenval Pediatric HospitalNiceFrance
| | - Pierpaolo Sorrentino
- Aix Marseille University, INSERM, INS, Institut de Neurosciences des SystèmesMarseilleFrance
- Department of Biomedical SciencesUniversity of SassariSassariItaly
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Li X, Wu S, Yang W, Jiang C, Zhao Z, Wang H, Lin F. Abnormal resting-state brain network dynamics in toddlers with autism spectrum disorder. Eur Child Adolesc Psychiatry 2025:10.1007/s00787-025-02651-7. [PMID: 39853404 DOI: 10.1007/s00787-025-02651-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2024] [Accepted: 01/16/2025] [Indexed: 01/26/2025]
Abstract
Emerging evidence suggests aberrant functional connectivity (FC) of brain networks in children, adolescents, and adults with autism spectrum disorder (ASD). However, little is known about alterations of dynamic FC in toddlers with ASD. The aim of this study was to investigate the characteristics of brain network dynamics in ASD toddlers. We analyzed resting-state functional magnetic resonance imaging data of 41 ASD and 23 typically-developing (TD) toddlers aged 15-45 months using leading eigenvector dynamics analysis. The between-group differences in the temporal properties of state were evaluated, followed by correlation analysis of these differences with autistic factors in ASD. Compared with TD subjects, ASD toddlers had a lower occurrence rate and mean dwell time in the global state. The mean dwell time of this state was negatively correlated with the body and object use of the autism behavior checklist (ABC) score. The ASD group presented a higher occurrence probability in the state related to the visual network, and the dwell time of this state was positively correlated with the relationship building of the ABC score. Moreover, the occurrence probability of the state related to the dorsal attention network and default mode network was positively correlated with the body and object use of the ABC score. Our findings indicate that there are differences in the developmental patterns of large-scale brain functional networks in ASD toddlers, which may contribute to the early identification of ASD and provide meaningful insights into its potential neural mechanisms.
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Affiliation(s)
- Xiuxian Li
- National Center for Magnetic Resonance in Wuhan, State Key Laboratory of Magnetic Resonance Spectroscopy and Imaging, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Shengjuan Wu
- Department of Child Health Care, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430070, China
| | - Wenzhong Yang
- Department of Radiology, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430070, China
| | - Chengcheng Jiang
- Department of Radiology, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430070, China
| | - Zhiwei Zhao
- Department of Child Health Care, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430070, China
| | - Hong Wang
- Department of Child Health Care, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430070, China.
| | - Fuhun Lin
- National Center for Magnetic Resonance in Wuhan, State Key Laboratory of Magnetic Resonance Spectroscopy and Imaging, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, 430071, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
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9
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Li Y, Zhang J, Li X, Zhang Z. Uncovering narrative aging: an underlying neural mechanism compensated through spatial constructional ability. Commun Biol 2025; 8:104. [PMID: 39837995 PMCID: PMC11751312 DOI: 10.1038/s42003-025-07501-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Accepted: 01/08/2025] [Indexed: 01/23/2025] Open
Abstract
"The narrative" is a complex cognitive process that has sparked a debate on whether its features age through maintenance or decline. To address this question, we attempted to uncover the narrative aging and its underlying neural characteristics with a cross-validation based cognitive neuro-decoding statistical framework. This framework used a total of 740 healthy older participants with completed narrative and extensive neuropsychological tests and MRI scans. The results indicated that narrative comprises macro and micro structures, with the macrostructure involving complex cognitive processes more relevant to aging. For the brain functional basis, brain hub nodes contributing to macrostructure were predominantly found in the angular gyrus and medial frontal lobe, while microstructure hub nodes were located in the supramarginal gyrus and middle cingulate cortex. Moreover, networks enriched by macrostructure included the default mode network and fronto-parietal network, indicating a higher functional gradient compared to the microstructure-enriched dorsal attention network. Additionally, an interesting finding showed that macrostructure increases in spatial contribution with age, suggesting a compensatory interaction where brain regions related to spatial-constructional ability have a greater impact on macrostructure. These results, supported by neural-level validation and multimodal structural MRI, provide detailed insights into the compensatory effect in the narrative aging process.
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Affiliation(s)
- Yumeng Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Centre, Beijing Normal University, Beijing, 100875, China
| | - Junying Zhang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700, China
| | - Xin Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Centre, Beijing Normal University, Beijing, 100875, China.
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Centre, Beijing Normal University, Beijing, 100875, China.
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10
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Xu M, Xu Y, Wu S, Li Z. The relationship between behavioral inhibition and resting electroencephalography: A neuroelectrophysiological study. Int J Psychophysiol 2025; 209:112516. [PMID: 39842666 DOI: 10.1016/j.ijpsycho.2025.112516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2024] [Revised: 01/08/2025] [Accepted: 01/18/2025] [Indexed: 01/24/2025]
Abstract
Investigating the neurophysiological indicators of behavioral inhibition is crucial; however, despite numerous studies on the relationship between behavioral inhibition and resting-state electroencephalography (rs-EEG), the findings have yielded inconsistent results. Furthermore, these investigations primarily focused on reactive inhibition while neglecting intentional inhibition. Therefore, this study aimed to reassess the correlation between reactive inhibition and rs-EEG metrics while also exploring the association between intentional inhibition and rs-EEG. Power spectrum analysis and microstate analysis were employed to extract rs-EEG, whereas the Free Two-Choice Oddball task was utilized for assessing both reactive and intentional inhibition among 95 participants. The results revealed no significant correlations between reactive inhibition and rs-EEG metrics. However, intentional inhibition exhibited a negative correlation with relative power in delta and beta bands but a positive correlation with relative power in alpha band. Moreover, intentional inhibition demonstrated a negative correlation with occurrence rate and contribution of microstate A but a positive correlation with duration of microstate D. Additionally, it displayed a negative relationship with the transition probability between microstate A and C but a positive relationship with the transition probability between microstate C and D. The regression analysis revealed that the occurrence rate of microstate A can negatively predict intentional inhibition. Overall, this study advances theoretical understanding as well as empirical research in this field by addressing gaps in rs-EEG evidence for intentional inhibition while providing potential neuropsychological indicators for its assessment.
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Affiliation(s)
- Mengsi Xu
- School of Psychology, Shaanxi Normal University, Xi'an, China.
| | - Yanxi Xu
- School of Psychology, Shaanxi Normal University, Xi'an, China
| | - Shiyan Wu
- School of Psychology, Shaanxi Normal University, Xi'an, China
| | - Zhiai Li
- Department of Applied Psychology, College of Public Administration, Guangdong University of Foreign Studies, Guangzhou, China.
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11
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Dang G, Zhu L, Lian C, Zeng S, Shi X, Pei Z, Lan X, Shi JQ, Yan N, Guo Y, Su X. Are neurasthenia and depression the same disease entity? An electroencephalography study. BMC Psychiatry 2025; 25:44. [PMID: 39825342 PMCID: PMC11742223 DOI: 10.1186/s12888-025-06468-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 01/01/2025] [Indexed: 01/20/2025] Open
Abstract
BACKGROUND The neurasthenia-depression controversy has lasted for several decades. It is challenging to solve the argument by symptoms alone for syndrome-based disease classification. Our aim was to identify objective electroencephalography (EEG) measures that can differentiate neurasthenia from major depressive disorder (MDD). METHODS Both electronic medical information records and EEG records from patients with neurasthenia and MDD were gathered. The demographic and clinical characteristics, EEG power spectral density, and functional connectivity were compared between the neurasthenia and MDD groups. Machine Learning methods such as random forest, logistic regression, support vector machines, and k nearest neighbors were also used for classification between groups to extend the identification that there is a significant different pattern between neurasthenia and MDD. RESULTS We analyzed 305 patients with neurasthenia and 45 patients with MDD. Compared with the MDD group, patients with neurasthenia reported more somatic symptoms and less emotional symptoms (p < 0.05). Moreover, lower theta connectivity was observed in patients with neurasthenia compared to those with MDD (p < 0.01). Among the classification models, random forest performed best with an accuracy of 0.93, area under the receiver operating characteristic curve of 0.97, and area under the precision-recall curve of 0.96. The essential feature contributing to the model was the theta connectivity. LIMITATIONS This is a retrospective study, and medical records may not include all the details of a patient's syndrome. The sample size of the MDD group was smaller than that of the neurasthenia group. CONCLUSION Neurasthenia and MDD are different not only in symptoms but also in brain activities.
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Affiliation(s)
- Ge Dang
- Department of Neurology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, China
| | - Lin Zhu
- Department of Neurology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, China
| | - Chongyuan Lian
- Institute of Neurological and Psychiatric Disorders, Shenzhen Bay Laboratory, Shenzhen, Guangdong, China
| | - Silin Zeng
- Department of Neurology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, China
| | - Xue Shi
- Department of Neurology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, China
| | - Zian Pei
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Xiaoyong Lan
- Institute of Neurological and Psychiatric Disorders, Shenzhen Bay Laboratory, Shenzhen, Guangdong, China
| | - Jian Qing Shi
- Department of Statistics and Data Science, College of Science, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Nan Yan
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen University Town, 1068 Xueyuan Avenue, Shenzhen, Guangdong, 518055, China.
| | - Yi Guo
- Department of Neurology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, China.
- Institute of Neurological and Psychiatric Disorders, Shenzhen Bay Laboratory, Shenzhen, Guangdong, China.
- Department of Neurology, Shenzhen People's Hospital, 1017 Dongmen North Road, Shenzhen, Guangdong, 518000, China.
| | - Xiaolin Su
- Department of Neurology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, China.
- Department of Neurology, Shenzhen People's Hospital, 1017 Dongmen North Road, Shenzhen, Guangdong, 518000, China.
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12
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Xin X, Gu S, Wang C, Gao X. Abnormal brain entropy dynamics in ADHD. J Affect Disord 2025; 369:1099-1107. [PMID: 39442707 DOI: 10.1016/j.jad.2024.10.066] [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: 04/16/2024] [Revised: 09/04/2024] [Accepted: 10/19/2024] [Indexed: 10/25/2024]
Abstract
BACKGROUND Brain entropy (BEN) is a novel measure for irregularity and complexity of brain activities, which has been used to characterize abnormal brain activities in many brain disorders including attention-deficit/hyperactivity disorder (ADHD). While most research assumes BEN is stationary during scan sessions, the brain in resting state is also a highly dynamic system. The BEN dynamics in ADHD has not been explored. METHODS We used a sliding window approach to derive the dynamical brain entropy (dBEN) from resting-state functional magnetic resonance imaging (rfMRI) dataset that includes 98 ADHD patients and 111 healthy controls (HCs). We identified 3 reoccurring BEN states. We tested whether the BEN dynamics differ between ADHD and HC, and whether they are associated with ADHD symptom severity. RESULTS One BEN states, characterized by low overall BEN and low within-state BEN located in SMN (sensorimotor network) and VN (visual network), its FW (fractional window) and MDT (mean dwell time) were increased in ADHD and positively correlated with ADHD severity; another state characterized by high overall BEN and low within-state BEN located in DMN (default mode network) and ECN (executive control network), its FW and MDT were decreased in ADHD and negatively correlated with ADHD severity. LIMITATIONS The window length of dBEN analysis can be further optimized to suit more datasets. The co-variation between dBEN and other dynamical brain metrics was not explored. CONCLUSION Our findings revealed abnormal BEN dynamics in ADHD, providing new insights into clinical diagnosis and neuropathology of ADHD.
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Affiliation(s)
- Xiaoyang Xin
- Preschool College, Luoyang Normal University, Luoyang 471000, China; Center for Psychological Sciences, Zhejiang University, Hangzhou 310027, China
| | - Shuangshuang Gu
- Center for Psychological Sciences, Zhejiang University, Hangzhou 310027, China
| | - Cuiping Wang
- Preschool College, Luoyang Normal University, Luoyang 471000, China
| | - Xiaoqing Gao
- Center for Psychological Sciences, Zhejiang University, Hangzhou 310027, China.
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13
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Darbin O, Eghbalnia HR, Romeo A, Montgomery EB. Emergence of structures in neuronal network activities. Sci Rep 2025; 15:220. [PMID: 39747412 PMCID: PMC11696529 DOI: 10.1038/s41598-024-83970-8] [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: 07/05/2024] [Accepted: 12/18/2024] [Indexed: 01/04/2025] Open
Abstract
Nonlinear responses of individual neurons are both experimentally established and considered fundamental for the functioning of neuronal circuitry. Consequently, one may envisage the collective dynamics of large networks of neurons exhibiting a large repertoire of nonlinear behaviors. However, an ongoing and central challenge in the modeling of neural dynamics involves the trade-off between tractability and biological realism. This is particularly important in exploring the range of possible dynamics of large networks. Our approach uses Gaussian white noise as a probe, thus capturing the full range of system responses and characteristics by using an approach inspired by the well-established Wiener - Volterra nonlinear system identification approach. We assess model behavior over a range of network architectures and noise stimulation rates and demonstrate non-monotonicity and nonlinearity as a system property. Perhaps surprisingly, our computational model suggests that recurrent systems of nonlinear neurons exhibit a range of complex behaviors that do not readily yield to linear modeling in every setting. Our results suggest that a linear interpretation of experimental data is likely to discount the critical importance of properties emerging from network architecture. The main contribution of this effort is to highlight the importance of the network's architecture operating on the nonlinear properties of individual neurons and the experimental probing approaches of the circuitry.
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Affiliation(s)
- Olivier Darbin
- Department of Neurosurgery, University South Alabama, 307 University Blvd, Mobile, AL, 36688, USA.
| | - Hamid R Eghbalnia
- Department of Molecular Biology and Biophysics, University of Connecticut Health Center, 263 Farmington Avenue, Farmington, CT, 06030, USA
| | - Andrew Romeo
- Department of Neurosurgery, University South Alabama, 307 University Blvd, Mobile, AL, 36688, USA
| | - Erwin B Montgomery
- Department of Medicine (Neurology), McMaster University, Hamilton, ON, L8L 2X2, Canada
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14
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Guendelman M, Vekslar R, Shriki O. A New Perspective in Epileptic Seizure Classification: Applying the Taxonomy of Seizure Dynamotypes to Noninvasive EEG and Examining Dynamical Changes across Sleep Stages. eNeuro 2025; 12:ENEURO.0157-24.2024. [PMID: 39746808 PMCID: PMC11747977 DOI: 10.1523/eneuro.0157-24.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 11/17/2024] [Accepted: 11/21/2024] [Indexed: 01/04/2025] Open
Abstract
Epilepsy, a neurological disorder characterized by recurrent unprovoked seizures, significantly impacts patient quality of life. Current classification methods focus primarily on clinical observations and electroencephalography (EEG) analysis, often overlooking the underlying dynamics driving seizures. This study uses surface EEG data to identify seizure transitions using a dynamical systems-based framework-the taxonomy of seizure dynamotypes-previously examined only in invasive data. We applied principal component and independent component (IC) analysis to surface EEG recordings from 1,177 seizures in 158 patients with focal epilepsy, decomposing the signals into ICs. The ICs were visually labeled for clear seizure transitions and bifurcation morphologies (BifMs), which were then examined using Bayesian multilevel modeling in the context of clinical factors. Our analysis reveals that certain onset bifurcations (saddle node on invariant circle and supercritical Hopf) are more prevalent during wakefulness compared with their reduced rate during nonrapid eye movement (NREM) sleep, particularly NREM3. We discuss the possible implications of our results in the context of modeling approaches and suggest additional avenues to continue this exploration. Furthermore, we demonstrate the feasibility of automating this classification process using machine learning, achieving high performance in identifying seizure-related ICs and classifying interspike interval changes. Our findings suggest that the noise in surface EEG may obscure certain BifMs, and we suggest technical improvements that could enhance detection accuracy. Expanding the dataset and incorporating long-term biological rhythms, such as circadian and multiday cycles, may provide a more comprehensive understanding of seizure dynamics and improve clinical decision-making.
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Affiliation(s)
| | | | - Oren Shriki
- Departments of Cognitive and Brain Sciences
- Computer Science, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel
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15
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Kusch L, Breyton M, Depannemaecker D, Petkoski S, Jirsa VK. Synchronization in spiking neural networks with short and long connections and time delays. CHAOS (WOODBURY, N.Y.) 2025; 35:013161. [PMID: 39883693 DOI: 10.1063/5.0158186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 12/13/2024] [Indexed: 02/01/2025]
Abstract
Synchronization is fundamental for information processing in oscillatory brain networks and is strongly affected by time delays via signal propagation along long fibers. Their effect, however, is less evident in spiking neural networks given the discrete nature of spikes. To bridge the gap between these different modeling approaches, we study the synchronization conditions, dynamics underlying synchronization, and the role of the delay of a two-dimensional network model composed of adaptive exponential integrate-and-fire neurons. Through parameter exploration of neuronal and network properties, we map the synchronization behavior as a function of unidirectional long-range connection and the microscopic network properties and demonstrate that the principal network behaviors comprise standing or traveling waves of activity and depend on noise strength, E/I balance, and voltage adaptation, which are modulated by the delay of the long-range connection. Our results show the interplay of micro- (single neuron properties), meso- (connectivity and composition of the neuronal network), and macroscopic (long-range connectivity) parameters for the emergent spatiotemporal activity of the brain.
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Affiliation(s)
- Lionel Kusch
- Institut de Neurosciences des Systèmes, Aix-Marseille University, INSERM, Marseille 13005, France
| | - Martin Breyton
- Institut de Neurosciences des Systèmes, Aix-Marseille University, INSERM, Marseille 13005, France
- Service de Pharmacologie Clinique et Pharmacovigilance, Assistance Publique des Hôpitaux de Marseille, Marseille 13005, France
| | - Damien Depannemaecker
- Institut de Neurosciences des Systèmes, Aix-Marseille University, INSERM, Marseille 13005, France
| | - Spase Petkoski
- Institut de Neurosciences des Systèmes, Aix-Marseille University, INSERM, Marseille 13005, France
| | - Viktor K Jirsa
- Institut de Neurosciences des Systèmes, Aix-Marseille University, INSERM, Marseille 13005, France
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16
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Fousek J, Rabuffo G, Gudibanda K, Sheheitli H, Petkoski S, Jirsa V. Symmetry breaking organizes the brain's resting state manifold. Sci Rep 2024; 14:31970. [PMID: 39738729 DOI: 10.1038/s41598-024-83542-w] [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: 07/24/2024] [Accepted: 12/16/2024] [Indexed: 01/02/2025] Open
Abstract
Spontaneously fluctuating brain activity patterns that emerge at rest have been linked to the brain's health and cognition. Despite detailed descriptions of the spatio-temporal brain patterns, our understanding of their generative mechanism is still incomplete. Using a combination of computational modeling and dynamical systems analysis we provide a mechanistic description of the formation of a resting state manifold via the network connectivity. We demonstrate that the symmetry breaking by the connectivity creates a characteristic flow on the manifold, which produces the major data features across scales and imaging modalities. These include spontaneous high-amplitude co-activations, neuronal cascades, spectral cortical gradients, multistability, and characteristic functional connectivity dynamics. When aggregated across cortical hierarchies, these match the profiles from empirical data. The understanding of the brain's resting state manifold is fundamental for the construction of task-specific flows and manifolds used in theories of brain function. In addition, it shifts the focus from the single recordings towards the brain's capacity to generate certain dynamics characteristic of health and pathology.
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Affiliation(s)
- Jan Fousek
- INSERM, INS, Institut de Neurosciences des Systèmes, Aix Marseille University, 13005, Marseille, France.
- Central European Institute of Technology (CEITEC), Masaryk University, Brno, Czech Republic.
| | - Giovanni Rabuffo
- INSERM, INS, Institut de Neurosciences des Systèmes, Aix Marseille University, 13005, Marseille, France
| | - Kashyap Gudibanda
- INSERM, INS, Institut de Neurosciences des Systèmes, Aix Marseille University, 13005, Marseille, France
| | - Hiba Sheheitli
- Department of Neurology, University of Minnesota, Minneapolis, MN, USA
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Spase Petkoski
- INSERM, INS, Institut de Neurosciences des Systèmes, Aix Marseille University, 13005, Marseille, France
| | - Viktor Jirsa
- INSERM, INS, Institut de Neurosciences des Systèmes, Aix Marseille University, 13005, Marseille, France.
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17
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Maximino C. Biocultural Aspects of Mental Distress: Expanding the Biomedical Model Towards an Integrative Biopsychosocial Understanding of Disorder. Integr Psychol Behav Sci 2024; 59:5. [PMID: 39725806 DOI: 10.1007/s12124-024-09869-1] [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: 11/20/2024] [Indexed: 12/28/2024]
Abstract
To produce a theoretical approach about the relations between neuroscience and psychopathology that expands beyond the biomedical model to include a non-reductionist, enactive, and biocultural perspective. An integrative review, drawing from the biocultural approach from Anthropology, is used to produce examples from epigenetics, neuroplasticity, and functional neuroanatomy. A biocultural approach points to a brain that is highly plastic, reinforcing a much more complex model in which biological vulnerabilities and the historical-cultural environment co-construct each other. The examples given seem to point to the pressing need for a critical expansion of reductionist models of psychopathology. Importantly, the cultural-historical environment to which we refer is not a set of neutral social relations to which individuals are homogeneously exposed, such that aspects that are usually studied under the social determinants of health and disease (poverty, discrimination, violence, and other factors that represent sources of control, production, and distribution of material resources, ideology, and power) need to be incorporated in adequate biopsychosocial models of mental distress.
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Affiliation(s)
- Caio Maximino
- Laboratório de Neurociências e Comportamento, Faculdade de Psicologia, Instituto de Estudos em Saúde e Biológicas, Universidade Federal do Sul e Sudeste do Pará, Av. dos Ipês, S/N, Marabá, PA, 68500-000, Brazil.
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18
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Ciaramella F, Cipriano L, Lopez ET, Polverino A, Lucidi F, Sorrentino G, Mandolesi L, Sorrentino P. Brain dynamics and personality: a preliminary study. AIMS Neurosci 2024; 11:490-504. [PMID: 39801796 PMCID: PMC11712230 DOI: 10.3934/neuroscience.2024030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2024] [Revised: 12/02/2024] [Accepted: 12/09/2024] [Indexed: 01/16/2025] Open
Abstract
Personality can be considered a system characterized by complex dynamics that are extremely adaptive depending on continuous interactions with the environment and situations. The present preliminary study explores the dynamic interplay between brain flexibility and personality by taking the dynamic approach to personality into account, thereby drawing from Cloninger's psychobiological model. 46 healthy individuals were recruited, and their brain dynamics were assessed using magnetoencephalography (MEG) during the resting state. We identified brain activation patterns and measured brain flexibility by employing the theory of neuronal avalanches. Subsequent correlation analyses revealed a significant positive association between brain flexibility and cooperativeness, thus highlighting the role of brain reconfiguration tendencies in fostering openness, tolerance, and empathy towards others. Additionally, this preliminary finding suggests a neurobiological basis for adaptive social behaviors. Although the results are preliminary, this study provides initial insights into the intricate relationship between brain dynamics and personality, thus laying the groundwork for further research in this emerging field using a dynamic network analysis of the functional activity of the brain.
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Affiliation(s)
- Francesco Ciaramella
- Department of Motor and Wellness Sciences, University of Naples “Parthenope”, 80133 Naples, Italy
- NeapoliSanit Rehabilitation Center, Ottaviano (NA), 80044 Naples, Italy
| | - Lorenzo Cipriano
- Department of Motor and Wellness Sciences, University of Naples “Parthenope”, 80133 Naples, Italy
| | - Emahnuel Troisi Lopez
- Department of Education and Sport Sciences, Pegaso Telematic University, 80143 Naples, Italy
| | | | - Fabio Lucidi
- Department of Social and Developmental Psychology, Faculty of Medicine and Psychology, University of Roma “Sapienza”, 00185 Rome, Italy
- Forensic Science and Social Governance Disciplinary Innovation Base of Zhongnan University of Economics and Law, 430073, Wuhan, China
| | - Giuseppe Sorrentino
- Department of Motor and Wellness Sciences, University of Naples “Parthenope”, 80133 Naples, Italy
- ICS Maugeri Hermitage Napoli, 80145 Naples, Italy
| | - Laura Mandolesi
- Department Humanities, University of Naples “Federico” II, 80133 Naples, Italy
| | - Pierpaolo Sorrentino
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
- Institut de Neurosciences des Systèmes, Inserm, INS, Aix-Marseille University, 13005 Marseille, France
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19
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Xue L, Qing T, Lv Y, Zhao J. Aging of visual word perception is related to decreased segregation within and beyond the word network in the brain. Front Aging Neurosci 2024; 16:1483449. [PMID: 39703923 PMCID: PMC11655501 DOI: 10.3389/fnagi.2024.1483449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2024] [Accepted: 11/14/2024] [Indexed: 12/21/2024] Open
Abstract
Introduction We investigated the neural correlates of cognitive decline in visual word perception from the perspective of intrinsic brain networks. Methods A total of 19 healthy older adults and 22 young adults were recruited to participate in two functional magnetic resonance imaging (fMRI) sessions (one resting-state session and one for localizer tasks), along with a visual word perceptual processing task. We examined age-related alterations in resting-state functional connectivity (FC) within the word network, as well as between the word network and other networks. We tested their associations with behavioral performance in word and symbol-form processing. Results We found that, compared to young adults, older adults exhibited increased FC between the two word-selective regions in the left and right ventral occipitotemporal cortex (vOT). Additionally, older adults exhibited increased FC between these two word-selective regions and non-word-selective regions. Notably, these FC alterations correlated with individual differences in behavioral performance in visual word perception. Discussion These results suggest that cognitive decline in visual word perception is associated with decreased segregation within and beyond the word network in the aging brain. Our findings support the neural dedifferentiation hypothesis for cognitive decline in visual word processing and improve our understanding of interactive neural specialization theory.
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Affiliation(s)
- Licheng Xue
- School of Early Childhood Education, Hangzhou Polytechnic, Zhejiang, China
| | - Tianying Qing
- Helmholtz Institute, Utrecht University, Utrecht, Netherlands
| | - Yating Lv
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Zhejiang, China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Zhejiang, China
| | - Jing Zhao
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Zhejiang, China
- Jing Hengyi School of Education, Hangzhou Normal University, Zhejiang, China
- Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University, Zhejiang, China
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20
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Leone R, Geysen S, Deco G, Kobeleva X. Beyond Focal Lesions: Dynamical Network Effects of White Matter Hyperintensities. Hum Brain Mapp 2024; 45:e70081. [PMID: 39624946 PMCID: PMC11612665 DOI: 10.1002/hbm.70081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2024] [Revised: 11/03/2024] [Accepted: 11/11/2024] [Indexed: 12/06/2024] Open
Abstract
White matter (WM) tracts shape the brain's dynamical activity and their damage (e.g., white matter hyperintensities, WMH) yields relevant functional alterations, ultimately leading to cognitive symptoms. The mechanisms linking the structural damage caused by WMH to the arising alterations of brain dynamics is currently unknown. To estimate the impact of WMH on brain dynamics, we combine neural-mass whole-brain modeling with a virtual-lesioning (disconnectome) approach informed by empirical data. We account for the heterogeneous effects of WMH either on inter-regional communication (i.e., edges) or on dynamics (i.e., nodes) and create models of their local versus global, and edge versus nodal effects using a large fMRI dataset comprising 188 non-demented individuals (120 cognitively normal, 68 with mild cognitive impairment) with varying degrees of WMH. We show that, although WMH mainly determine local damage to specific WM tracts, these lesions yield relevant global dynamical effects by reducing the overall synchronization of the brain through a reduction of global coupling. Alterations of local nodal dynamics through disconnections are less relevant and present only at later stages of WMH damage. Exploratory analyses suggest that education might play a beneficial role in counteracting the reduction in global coupling associated with WMH. This study provides generative models linking the structural damage caused by WMH to alterations in brain dynamics. These models might be used to evaluate the detrimental effects of WMH on brain dynamics in a subject-specific manner. Furthermore, it validates the use of whole-brain modeling for hypothesis-testing of structure-function relationships in diseased states characterized by empirical disconnections.
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Affiliation(s)
- Riccardo Leone
- Computational Neurology GroupRuhr University BochumBochumGermany
- Faculty of MedicineUniversity of BonnBonnGermany
- German Center for Neurodegenerative Diseases (DZNE)BonnGermany
| | - Steven Geysen
- German Center for Neurodegenerative Diseases (DZNE)BonnGermany
- Department of NeurologyUniversity Hospital BonnBonnGermany
| | - Gustavo Deco
- Department of Information and Communication Technologies, Center for Brain and Cognition, Computational Neuroscience GroupUniversitat Pompeu FabraBarcelonaSpain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA)BarcelonaSpain
| | - Xenia Kobeleva
- Computational Neurology GroupRuhr University BochumBochumGermany
- German Center for Neurodegenerative Diseases (DZNE)BonnGermany
- Department of NeurologyUniversity Hospital BonnBonnGermany
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21
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Kashou AW, Frees DM, Kang K, Parks CO, Harralson H, Fischer JT, Rosenbaum PE, Baham M, Sheridan C, Bickart KC. Drivers of resting-state fMRI heterogeneity in traumatic brain injury across injury characteristics and imaging methods: a systematic review and semiquantitative analysis. Front Neurol 2024; 15:1487796. [PMID: 39664747 PMCID: PMC11631856 DOI: 10.3389/fneur.2024.1487796] [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: 08/28/2024] [Accepted: 10/23/2024] [Indexed: 12/13/2024] Open
Abstract
Traumatic brain injury (TBI) is common and costly. Although neuroimaging modalities such as resting-state functional MRI (rsfMRI) promise to differentiate injured from healthy brains and prognosticate long-term outcomes, the field suffers from heterogeneous findings. To assess whether this heterogeneity stems from variability in the TBI populations studied or the imaging methods used, and to determine whether a consensus exists in this literature, we performed the first systematic review of studies comparing rsfMRI functional connectivity (FC) in patients with TBI to matched controls for seven canonical brain networks across injury severity, age, chronicity, population type, and various imaging methods. Searching PubMed, Web of Science, Google Scholar, and ScienceDirect, 1,105 manuscripts were identified, 50 fulfilling our criteria. Across these manuscripts, 179 comparisons were reported between a total of 1,397 patients with TBI and 1,179 matched controls. Collapsing across injury characteristics, imaging methods, and networks, there were roughly equal significant to null findings and increased to decreased connectivity differences reported. Whereas most factors did not explain these mixed findings, stratifying across severity and chronicity, separately, showed a trend of increased connectivity at higher severities and greater chronicities of TBI. Among methodological factors, studies were more likely to find connectivity differences when scans were longer than 360 s, custom image processing pipelines were used, and when patients kept their eyes open versus closed during scans. We offer guidelines to address this variability, focusing on aspects of study design and rsfMRI acquisition to move the field toward reproducible results with greater potential for clinical translation.
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Affiliation(s)
- Alexander W. Kashou
- Department of Radiology, Loma Linda University School of Medicine, Loma Linda, CA, United States
- UCLA Steve Tisch BrainSPORT Program, University of California, Los Angeles, Los Angeles, CA, United States
| | - Daniel M. Frees
- UCLA Steve Tisch BrainSPORT Program, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Statistics, Stanford University, Stanford, CA, United States
| | - Kaylee Kang
- UCLA Steve Tisch BrainSPORT Program, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Statistics, Stanford University, Stanford, CA, United States
| | - Christian O. Parks
- UCLA Steve Tisch BrainSPORT Program, University of California, Los Angeles, Los Angeles, CA, United States
| | - Hunter Harralson
- UCLA Steve Tisch BrainSPORT Program, University of California, Los Angeles, Los Angeles, CA, United States
| | - Jesse T. Fischer
- UCLA Steve Tisch BrainSPORT Program, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Kinesiology, Occidental College, Los Angeles, CA, United States
| | - Philip E. Rosenbaum
- UCLA Steve Tisch BrainSPORT Program, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - Michael Baham
- UCLA Steve Tisch BrainSPORT Program, University of California, Los Angeles, Los Angeles, CA, United States
- School of Medicine, University of California, Irvine, Irvine, CA, United States
| | - Christopher Sheridan
- UCLA Steve Tisch BrainSPORT Program, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Kevin C. Bickart
- UCLA Steve Tisch BrainSPORT Program, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
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22
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Yang J, Liu Z, Pan Y, Fan Z, Cheng Y, Wang F, Sun F, Wu G, Ouyang X, Tao H, Yang J, Palaniyappan L. Regional neural functional efficiency across schizophrenia, bipolar disorder, and major depressive disorder: a transdiagnostic resting-state fMRI study. Psychol Med 2024:1-12. [PMID: 39552391 DOI: 10.1017/s0033291724001685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2024]
Abstract
BACKGROUND Major psychiatric disorders (MPDs) are delineated by distinct clinical features. However, overlapping symptoms and transdiagnostic effectiveness of medications have challenged the traditional diagnostic categorisation. We investigate if there are shared and illness-specific disruptions in the regional functional efficiency (RFE) of the brain across these disorders. METHODS We included 364 participants (118 schizophrenia [SCZ], 80 bipolar disorder [BD], 91 major depressive disorder [MDD], and 75 healthy controls [HCs]). Resting-state fMRI was used to caclulate the RFE based on the static amplitude of low-frequency fluctuation, regional homogeneity, and degree centrality and corresponding dynamic measures indicating variability over time. We used principal component analysis to obtain static and dynamic RFE values. We conducted functional and genetic annotation and enrichment analysis based on abnormal RFE profiles. RESULTS SCZ showed higher static RFE in the cortico-striatal regions and excessive variability in the cortico-limbic regions. SCZ and MDD shared lower static RFE with higher dynamic RFE in sensorimotor regions than BD and HCs. We observed association between static RFE abnormalities with reward and sensorimotor functions and dynamic RFE abnormalities with sensorimotor functions. Differential spatial expression of genes related to glutamatergic synapse and calcium/cAMP signaling was more likely in the regions with aberrant RFE. CONCLUSIONS SCZ shares more regions with disrupted functional integrity, especially in sensorimotor regions, with MDD rather than BD. The neural patterns of these transdiagnostic changes appear to be potentially driven by gene expression variations relating to glutamatergic synapses and calcium/cAMP signaling. The aberrant sensorimotor, cortico-striatal, and cortico-limbic integrity may collectively underlie neurobiological mechanisms of MPDs.
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Affiliation(s)
- Jun Yang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Zhening Liu
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Yunzhi Pan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Zebin Fan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Yixin Cheng
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Feiwen Wang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Fuping Sun
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Guowei Wu
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xuan Ouyang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Haojuan Tao
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Jie Yang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Lena Palaniyappan
- Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montreal, Quebec, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
- Schulich School of Medicine and Dentistry, Robarts Research Institute, Western University, London, Ontario, Canada
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23
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Valt C, Tavella A, Berchio C, Seebold D, Sportelli L, Rampino A, Salisbury DF, Bertolino A, Pergola G. MEG Microstates: An Investigation of Underlying Brain Sources and Potential Neurophysiological Processes. Brain Topogr 2024; 37:993-1009. [PMID: 39115626 PMCID: PMC11408537 DOI: 10.1007/s10548-024-01073-z] [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: 03/19/2024] [Accepted: 07/22/2024] [Indexed: 09/18/2024]
Abstract
Microstates are transient scalp configurations of brain activity measured by electroencephalography (EEG). The application of microstate analysis in magnetoencephalography (MEG) data remains challenging. In one MEG dataset (N = 113), we aimed to identify MEG microstates at rest, explore their brain sources, and relate them to changes in brain activity during open-eyes (ROE) or closed-eyes resting state (RCE) and an auditory Mismatch Negativity (MMN) task. In another dataset of simultaneously recorded EEG-MEG data (N = 21), we investigated the association between MEG and EEG microstates. Six MEG microstates (mMS) provided the best clustering of resting-state activity, each linked to different brain sources: mMS 1-2: left/right occipito-parietal; mMS 3: fronto-temporal; mMS 4: centro-medial; mMS 5-6: left/right fronto-parietal. Increases in occipital alpha power in RCE relative to ROE correlated with greater mMS 1-2 time coverage (τbs < 0.20, ps > .002), while the lateralization of deviance detection in MMN was associated with mMS 5-6 time coverage (τbs < 0.16, ps > .012). No temporal correlation was found between EEG and MEG microstates (ps > .05), despite some overlap in brain sources and global explained variance between mMS 2-3 and EEG microstates B-C (rs > 0.60, ps < .002). Hence, the MEG signal can be decomposed into microstates, but mMS brain activity clustering captures phenomena different from EEG microstates. Source reconstruction and task-related modulations link mMS to large-scale networks and localized activities. Thus, mMSs offer insights into brain dynamics and task-specific processes, complementing EEG microstates in studying physiological and dysfunctional brain activity.
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Affiliation(s)
- Christian Valt
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy.
| | - Angelantonio Tavella
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Department of Mental Health, ASL Bari, Bari, Italy
| | - Cristina Berchio
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Dylan Seebold
- Clinical Neurophysiology Research Laboratory, Western Psychiatric Hospital, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Leonardo Sportelli
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Antonio Rampino
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Psychiatric Unit, Bari University Hospital, Bari, Italy
| | - Dean F Salisbury
- Clinical Neurophysiology Research Laboratory, Western Psychiatric Hospital, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Alessandro Bertolino
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
- Psychiatric Unit, Bari University Hospital, Bari, Italy
| | - Giulio Pergola
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy.
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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24
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Dziego CA, Zanesco AP, Bornkessel-Schlesewsky I, Schlesewsky M, Stanley EA, Jha AP. Mindfulness Training in High-Demand Cohorts Alters Resting-State Electroencephalography: An Exploratory Investigation of Individual Alpha Frequency, Aperiodic 1/ f Activity, and Microstates. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100383. [PMID: 39416660 PMCID: PMC11480290 DOI: 10.1016/j.bpsgos.2024.100383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 07/31/2024] [Accepted: 08/05/2024] [Indexed: 10/19/2024] Open
Abstract
Background Mindfulness training (MT) programs have demonstrated utility as cognitive training tools, but there is little consensus on the neurophysiological processes that may underlie its benefits. It has been posited that intrinsic brain activity recorded at rest reflects the functional connectivity of large-scale brain networks and may provide insight into neuroplastic changes that support MT. In the current study, we indexed changes in several resting-state electroencephalography (EEG) parameters to investigate the neurophysiological underpinnings of MT. Methods Resting-state EEG data were collected from active-duty U.S. military personnel (N = 80) at 2 testing sessions: before (time [T] 1) and after (T2) engaging in an 8-week MT or active comparison intervention (positivity training). We examined longitudinal and/or groupwise differences in several EEG parameters through parameterization of power spectra (individual alpha frequency and 1/f activity) and microstate analysis. Results While no significant group × time differences were observed in individual alpha frequency, significant group × time effects were observed in several EEG parameters from T1 to T2. Compared with MT, positivity training was associated with a steepening of the 1/f slope and higher 1/f intercepts together with decreased duration and increased global field power of microstates. Conclusions Taken together, these results suggest that the effects of interventions may be differentiated in resting-state brain activity in a sample of military personnel. Such findings provide insight into the neural underpinnings of MT-related brain changes, but more research is required to elucidate how these may relate to task-related neural and performance changes with MT and whether results generalize to other mindfulness interventions in alternative cohorts and contexts.
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Affiliation(s)
- Chloe A. Dziego
- Cognitive Neuroscience Laboratory, Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, South Australia, Australia
| | | | - Ina Bornkessel-Schlesewsky
- Cognitive Neuroscience Laboratory, Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, South Australia, Australia
| | - Matthias Schlesewsky
- Cognitive Neuroscience Laboratory, Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, South Australia, Australia
| | - Elizabeth A. Stanley
- Edmund A. Walsh School of Foreign Service, Georgetown University, Washington, DC
| | - Amishi P. Jha
- Department of Psychology, University of Miami, Coral Gables, Florida
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25
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Ning M, Rodionov A, Ross JM, Ozdemir RA, Burch M, Lian SJ, Alsop D, Cavallari M, Dickerson BC, Fong TG, Jones RN, Libermann TA, Marcantonio ER, Santarnecchi E, Schmitt EM, Touroutoglou A, Travison TG, Acker L, Reese M, Sun H, Westover B, Berger M, Pascual-Leone A, Inouye SK, Shafi MM. Prediction of Postoperative Delirium in Older Adults from Preoperative Cognition and Occipital Alpha Power from Resting-State Electroencephalogram. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.15.24312053. [PMID: 39185530 PMCID: PMC11343253 DOI: 10.1101/2024.08.15.24312053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Background Postoperative delirium is the most common complication following surgery among older adults, and has been consistently associated with increased mortality and morbidity, cognitive decline, and loss of independence, as well as markedly increased health-care costs. Electroencephalography (EEG) spectral slowing has frequently been observed during episodes of delirium, whereas intraoperative frontal alpha power is associated with postoperative delirium. We sought to identify preoperative predictors that could identify individuals at high risk for postoperative delirium, which could guide clinical decision-making and enable targeted interventions to potentially decrease delirium incidence and postoperative delirium-related complications. Methods In this prospective observational study, we used machine learning to evaluate whether baseline (preoperative) cognitive function and resting-state EEG could be used to identify patients at risk for postoperative delirium. Preoperative resting-state EEGs and the Montreal Cognitive Assessment were collected from 85 patients (age = 73 ± 6.4 years, 12 cases of delirium) undergoing elective surgery. The model with the highest f1-score was subsequently validated in an independent, prospective cohort of 51 older adults (age = 68 ± 5.2 years, 6 cases of delirium) undergoing elective surgery. Results Occipital alpha powers have higher f1-score than frontal alpha powers and EEG spectral slowing in the training cohort. Occipital alpha powers were able to predict postoperative delirium with AUC, specificity and accuracy all >90%, and sensitivity >80%, in the validation cohort. Notably, models incorporating transformed alpha powers and cognitive scores outperformed models incorporating occipital alpha powers alone or cognitive scores alone. Conclusions While requiring prospective validation in larger cohorts, these results suggest that strong prediction of postoperative delirium may be feasible in clinical settings using simple and widely available clinical tools. Additionally, our results suggested that the thalamocortical circuit exhibits different EEG patterns under different stressors, with occipital alpha powers potentially reflecting baseline vulnerabilities.
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Affiliation(s)
- Matthew Ning
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Andrei Rodionov
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Boston, MA, USA
- BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki, Finland
- Faculty of Educational Sciences, University of Helsinki, University of Helsinki, Finland
| | - Jessica M. Ross
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center, Palo Alto, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford Medical School, Stanford, CA, USA
| | - Recep A. Ozdemir
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Maja Burch
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Shu Jing Lian
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - David Alsop
- Harvard Medical School, Boston, MA, USA
- Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Michele Cavallari
- Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Center for Neurological Imaging, Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Bradford C. Dickerson
- Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Tamara G. Fong
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Boston, MA, USA
| | - Richard N. Jones
- Department of Psychiatry and Human Behavior, Department of Neurology, Warren Alpert Medical School, Brown University, Providence, RI, USA
| | - Towia A. Libermann
- Harvard Medical School, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Edward R. Marcantonio
- Harvard Medical School, Boston, MA, USA
- Divisions of General Medicine and Gerontology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Emiliano Santarnecchi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Precision Neuroscience & Neuromodulation Program (PNN), Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Eva M. Schmitt
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Boston, MA, USA
| | - Alexandra Touroutoglou
- Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Thomas G. Travison
- Harvard Medical School, Boston, MA, USA
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Boston, MA, USA
| | - Leah Acker
- Department of Anesthesiology, Duke University School of Medicine, Durham, NC, USA
- Duke Center for the Study of Aging and Human Development, Duke University School of Medicine, Durham, NC, USA
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
| | - Melody Reese
- Department of Anesthesiology, Duke University School of Medicine, Durham, NC, USA
- Duke Center for the Study of Aging and Human Development, Duke University School of Medicine, Durham, NC, USA
| | - Haoqi Sun
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health at Mass General
| | - Brandon Westover
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health at Mass General
| | - Miles Berger
- Department of Anesthesiology, Duke University School of Medicine, Durham, NC, USA
- Duke Center for the Study of Aging and Human Development, Duke University School of Medicine, Durham, NC, USA
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
- Duke-UNC Alzheimer’s Disease Research Center, Durham, NC, USA
| | - Alvaro Pascual-Leone
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Boston, MA, USA
| | - Sharon K. Inouye
- Harvard Medical School, Boston, MA, USA
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Mouhsin M. Shafi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
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26
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Zhu Z, Tang D, Qin L, Qian Z, Zhuang J, Liu Y. Syncing the brain's networks: dynamic functional connectivity shifts from temporal interference. Front Hum Neurosci 2024; 18:1453638. [PMID: 39534013 PMCID: PMC11554487 DOI: 10.3389/fnhum.2024.1453638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2024] [Accepted: 10/08/2024] [Indexed: 11/16/2024] Open
Abstract
Background Temporal interference (TI) stimulation, an innovative non-invasive brain stimulation approach, has the potential to activate neurons in deep brain regions. However, the dynamic mechanisms underlying its neuromodulatory effects are not fully understood. This study aims to investigate the effects of TI stimulation on dynamic functional connectivity (dFC) in the motor cortex. Methods 40 healthy adults underwent both TI and tDCS in a double-blind, randomized crossover design, with sessions separated by at least 48 h. The total stimulation intensity of TI is 4 mA, with each channel's intensity set at 2 mA and a 20 Hz frequency difference (2 kHz and 2.02 kHz). The tDCS stimulation intensity is 2 mA. Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected before, during, and after stimulation. dFC was calculated using the left primary motor cortex (M1) as the region of interest (ROI) and analyzed using a sliding time-window method. A two-way repeated measures ANOVA (group × time) was conducted to evaluate the effects of TI and tDCS on changes in dFC. Results For CV of dFC, significant main effects of stimulation type (P = 0.004) and time (P < 0.001) were observed. TI showed lower CV of dFC than tDCS in the left postcentral gyrus (P < 0.001). TI-T2 displayed lower CV of dFC than TI-T1 in the left precentral gyrus (P < 0.001). For mean dFC, a significant main effect of time was found (P < 0.001). TI-T2 showed higher mean dFC than tDCS-T2 in the left postcentral gyrus (P = 0.018). Within-group comparisons revealed significant differences between time points in both TI and tDCS groups, primarily in the left precentral and postcentral gyri (all P < 0.001). Results were consistent across different window sizes. Conclusion 20 Hz TI stimulation altered dFC in the primary motor cortex, leading to a significant decreasing variability and increasing mean connectivity strength in dFC. This outcome indicates that the 20 Hz TI frequency interacted with the motor cortex's natural resonance.
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Affiliation(s)
- Zhiqiang Zhu
- School of Kinesiology, Shenzhen University, Shenzhen, China
| | - Dongsheng Tang
- School of Kinesiology, Shenzhen University, Shenzhen, China
| | - Lang Qin
- School of Kinesiology, Shenzhen University, Shenzhen, China
| | - Zhenyu Qian
- School of Kinesiology, Shanghai University of Sport, Shanghai, China
| | - Jie Zhuang
- School of Kinesiology, Shanghai University of Sport, Shanghai, China
| | - Yu Liu
- School of Kinesiology, Shanghai University of Sport, Shanghai, China
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27
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Sharma K, Deco G, Solodkin A. The localization of coma. Cogn Neuropsychol 2024:1-20. [PMID: 39471280 DOI: 10.1080/02643294.2024.2420406] [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: 08/28/2023] [Revised: 10/08/2024] [Accepted: 10/17/2024] [Indexed: 11/01/2024]
Abstract
Coma and disorders of consciousness (DoC) are common manifestations of acute severe brain injuries. Research into their neuroanatomical basis can be traced from Hippocrates to the present day. Lesions causing DoC have traditionally been conceptualized as decreasing "alertness" from damage to the ascending arousal system, and/or, reducing level of "awareness" due to structural or functional impairment of large-scale brain networks. Within this framework, pharmacological and neuromodulatory interventions to promote recovery from DoC have hitherto met with limited success. This is partly due to inter-individual heterogeneity of brain injury patterns, and an incomplete understanding of brain network properties that characterize consciousness. Advances in multiscale computational modelling of brain dynamics have opened a unique opportunity to explore the causal mechanisms of brain activity at the biophysical level. These models can provide a novel approach for selection and optimization of potential interventions by simulation of brain network dynamics individualized for each patient.
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Affiliation(s)
- Kartavya Sharma
- Neurocritical care division, Departments of Neurology & Neurosurgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Peter O'Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Ana Solodkin
- Department of Neuroscience, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
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28
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Liu Z, Xia H, Chen A. Impaired brain ability of older adults to transit and persist to latent states with well-organized structures at wakeful rest. GeroScience 2024:10.1007/s11357-024-01366-y. [PMID: 39361232 DOI: 10.1007/s11357-024-01366-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Accepted: 09/24/2024] [Indexed: 11/16/2024] Open
Abstract
The intrinsic brain functional network organization continuously changes with aging. By integrating spatial and temporal information, the process of how brain networks temporally reconfigure and remain well-organized spatial structure largely reflects the brain function, thereby holds the potential to capture its age-related declines. In this study, we examined the spatiotemporal brain dynamics from resting-state functional Magnetic Resonance Imaging (fMRI) data of healthy young and older adults using a Hidden Markov Model (HMM). Six brain states were generated by HMM, with the young group showing higher fractional occupancy and mean dwell time in states 1, 3, and 4 (SY1, SY2 and SY3), and the older group in states 2, 5, and 6 (SO1, SO2 and SO3). Importantly, comparisons of transition probabilities revealed that the older group showed a reduced brain ability to transition into states dominated by the younger group, as well as a diminished capacity to persist in them. Moreover, graph analysis revealed that these young-specific states exhibited higher modularity and k-coreness. Collectively, these findings suggested that the older group showed impaired brain ability of both transition into and sustain well spatially organized states. This emphasized that the temporal changes in brain state organization, rather than its static mode, could be a key biomarker for detecting age-related functional decline. These insights may pave the way for targeted interventions aimed at mitigating cognitive decline in the aging population.
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Affiliation(s)
- Zijin Liu
- School of Psychology, Research Center for Exercise and Brain Science, Shanghai University of Sport, Shanghai, 200082, China
| | - Haishuo Xia
- Faculty of Psychology, Southwest University, Chongqing, 400700, China
| | - Antao Chen
- Faculty of Psychology, Southwest University, Chongqing, 400700, China.
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Pronold J, van Meegen A, Shimoura RO, Vollenbröker H, Senden M, Hilgetag CC, Bakker R, van Albada SJ. Multi-scale spiking network model of human cerebral cortex. Cereb Cortex 2024; 34:bhae409. [PMID: 39428578 PMCID: PMC11491286 DOI: 10.1093/cercor/bhae409] [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: 11/03/2023] [Revised: 09/15/2024] [Accepted: 09/24/2024] [Indexed: 10/22/2024] Open
Abstract
Although the structure of cortical networks provides the necessary substrate for their neuronal activity, the structure alone does not suffice to understand the activity. Leveraging the increasing availability of human data, we developed a multi-scale, spiking network model of human cortex to investigate the relationship between structure and dynamics. In this model, each area in one hemisphere of the Desikan-Killiany parcellation is represented by a $1\,\mathrm{mm^{2}}$ column with a layered structure. The model aggregates data across multiple modalities, including electron microscopy, electrophysiology, morphological reconstructions, and diffusion tensor imaging, into a coherent framework. It predicts activity on all scales from the single-neuron spiking activity to the area-level functional connectivity. We compared the model activity with human electrophysiological data and human resting-state functional magnetic resonance imaging (fMRI) data. This comparison reveals that the model can reproduce aspects of both spiking statistics and fMRI correlations if the inter-areal connections are sufficiently strong. Furthermore, we study the propagation of a single-spike perturbation and macroscopic fluctuations through the network. The open-source model serves as an integrative platform for further refinements and future in silico studies of human cortical structure, dynamics, and function.
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Affiliation(s)
- Jari Pronold
- Institute for Advanced Simulation (IAS-6), Jülich Research Centre, D-52428 Jülich, Germany
- RWTH Aachen University, D-52062 Aachen, Germany
| | - Alexander van Meegen
- Institute for Advanced Simulation (IAS-6), Jülich Research Centre, D-52428 Jülich, Germany
- Institute of Zoology, University of Cologne, D-50674 Cologne, Germany
| | - Renan O Shimoura
- Institute for Advanced Simulation (IAS-6), Jülich Research Centre, D-52428 Jülich, Germany
| | - Hannah Vollenbröker
- Institute for Advanced Simulation (IAS-6), Jülich Research Centre, D-52428 Jülich, Germany
- Heinrich Heine University Düsseldorf, D-40225 Düsseldorf, Germany
| | - Mario Senden
- Faculty of Psychology and Neuroscience, Department of Cognitive Neuroscience, Maastricht University, NL-6229 ER Maastricht, The Netherlands
- Faculty of Psychology and Neuroscience, Maastricht Brain Imaging Centre, Maastricht University, NL-6229 ER Maastricht, The Netherlands
| | - Claus C Hilgetag
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, D-20246 Hamburg, Germany
| | - Rembrandt Bakker
- Institute for Advanced Simulation (IAS-6), Jülich Research Centre, D-52428 Jülich, Germany
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, NL-6525 EN Nijmegen, The Netherlands
| | - Sacha J van Albada
- Institute for Advanced Simulation (IAS-6), Jülich Research Centre, D-52428 Jülich, Germany
- Institute of Zoology, University of Cologne, D-50674 Cologne, Germany
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30
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Fox NA, Pérez-Edgar K, Morales S, Brito NH, Campbell AM, Cavanagh JF, Gabard-Durnam LJ, Hudac CM, Key AP, Larson-Prior LJ, Pedapati EV, Norton ES, Reetzke R, Roberts TP, Rutter TM, Scott LS, Shuffrey LC, Antúnez M, Boylan MR, Garner BM, Learnard B, McNair S, McSweeney M, Castillo MIN, Norris J, Nyabingi OS, Pini N, Quinn A, Stosur R, Tan E, Troller-Renfree SV, Yoder L. The development and structure of the HEALthy Brain and Child Development (HBCD) Study EEG protocol. Dev Cogn Neurosci 2024; 69:101447. [PMID: 39305603 PMCID: PMC11439552 DOI: 10.1016/j.dcn.2024.101447] [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: 03/06/2024] [Revised: 08/03/2024] [Accepted: 09/06/2024] [Indexed: 09/30/2024] Open
Abstract
The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. Electroencephalography (EEG) is one of two brain imaging modalities central to the HBCD Study. EEG records electrical signals from the scalp that reflect electrical brain activity. In addition, the EEG signal can be synchronized to the presentation of discrete stimuli (auditory or visual) to measure specific cognitive processes with excellent temporal precision (e.g., event-related potentials; ERPs). EEG is particularly helpful for the HBCD Study as it can be used with awake, alert infants, and can be acquired continuously across development. The current paper reviews the HBCD Study's EEG/ERP protocol: (a) the selection and development of the tasks (Video Resting State, Visual Evoked Potential, Auditory Oddball, Face Processing); (b) the implementation of common cross-site acquisition parameters and hardware, site setup, training, and initial piloting; (c) the development of the preprocessing pipelines and creation of derivatives; and (d) the incorporation of equity and inclusion considerations. The paper also provides an overview of the functioning of the EEG Workgroup and the input from members across all steps of protocol development and piloting.
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Affiliation(s)
- Nathan A Fox
- Department of Human Development and Quantitative Methodology, The University of Maryland, College Park, USA.
| | | | - Santiago Morales
- Department of Psychology, University of Southern California, USA
| | | | - Alana M Campbell
- Department of Psychiatry, University of North Carolina at Chapel Hill, USA
| | | | | | - Caitlin M Hudac
- Department of Psychology and Carolina Autism and Neurodevelopment Research Center, University of South Carolina, USA
| | - Alexandra P Key
- Department of Pediatrics and Marcus Autism Center, Emory University School of Medicine, USA
| | - Linda J Larson-Prior
- Department of Neurobiology and Developmental Sciences, University of Arkansas for Medical Sciences, USA
| | - Ernest V Pedapati
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, USA
| | - Elizabeth S Norton
- Roxelyn and Richard Pepper, Department of Communication Sciences and Disorders, Department of Medical Social Sciences, and Institute for Innovations in Developmental Sciences, Northwestern University, USA
| | - Rachel Reetzke
- Center for Autism Services, Science and Innovation, Kennedy Krieger Institute, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, USA
| | | | - Tara M Rutter
- Department of Psychiatry, Oregon Health and Science University, USA
| | - Lisa S Scott
- Department of Psychology, University of Florida, USA
| | - Lauren C Shuffrey
- Department of Child and Adolescent Psychiatry, NYU Grossman School of Medicine, USA
| | - Martín Antúnez
- Department of Human Development and Quantitative Methodology, The University of Maryland, College Park, USA
| | | | | | | | - Savannah McNair
- Department of Human Development and Quantitative Methodology, The University of Maryland, College Park, USA
| | - Marco McSweeney
- Department of Human Development and Quantitative Methodology, The University of Maryland, College Park, USA
| | | | - Jessica Norris
- Department of Human Development and Quantitative Methodology, The University of Maryland, College Park, USA
| | | | - Nicolò Pini
- Department of Psychiatry, Columbia University Irving Medical Center; Division of Developmental Neuroscience, New York State Psychiatric Institute, USA
| | - Alena Quinn
- Department of Psychology, University of South Carolina, USA
| | - Rachel Stosur
- Department of Human Development and Quantitative Methodology, The University of Maryland, College Park, USA
| | - Enda Tan
- Department of Human Development and Quantitative Methodology, The University of Maryland, College Park, USA
| | | | - Lydia Yoder
- Department of Human Development and Quantitative Methodology, The University of Maryland, College Park, USA
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31
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Faghiri A, Yang K, Faria A, Ishizuka K, Sawa A, Adali T, Calhoun V. Frequency modulation increases the specificity of time-resolved connectivity: A resting-state fMRI study. Netw Neurosci 2024; 8:734-761. [PMID: 39355435 PMCID: PMC11349031 DOI: 10.1162/netn_a_00372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 03/06/2024] [Indexed: 10/03/2024] Open
Abstract
Representing data using time-resolved networks is valuable for analyzing functional data of the human brain. One commonly used method for constructing time-resolved networks from data is sliding window Pearson correlation (SWPC). One major limitation of SWPC is that it applies a high-pass filter to the activity time series. Therefore, if we select a short window (desirable to estimate rapid changes in connectivity), we will remove important low-frequency information. Here, we propose an approach based on single sideband modulation (SSB) in communication theory. This allows us to select shorter windows to capture rapid changes in the time-resolved functional network connectivity (trFNC). We use simulation and real resting-state functional magnetic resonance imaging (fMRI) data to demonstrate the superior performance of SSB+SWPC compared to SWPC. We also compare the recurring trFNC patterns between individuals with the first episode of psychosis (FEP) and typical controls (TC) and show that FEPs stay more in states that show weaker connectivity across the whole brain. A result exclusive to SSB+SWPC is that TCs stay more in a state with negative connectivity between subcortical and cortical regions. Based on all the results, we argue that SSB+SWPC is more sensitive for capturing temporal variation in trFNC.
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Affiliation(s)
- Ashkan Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Kun Yang
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Andreia Faria
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Koko Ishizuka
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Akira Sawa
- Departments of Psychiatry, Neuroscience, Biomedical Engineering, Genetic Medicine, and Pharmacology, Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Tülay Adali
- Deptartment of CSEE, University of Maryland, Baltimore County, Baltimore, MD, USA
| | - Vince Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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32
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Fotiadis P, Parkes L, Davis KA, Satterthwaite TD, Shinohara RT, Bassett DS. Structure-function coupling in macroscale human brain networks. Nat Rev Neurosci 2024; 25:688-704. [PMID: 39103609 DOI: 10.1038/s41583-024-00846-6] [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] [Accepted: 07/03/2024] [Indexed: 08/07/2024]
Abstract
Precisely how the anatomical structure of the brain gives rise to a repertoire of complex functions remains incompletely understood. A promising manifestation of this mapping from structure to function is the dependency of the functional activity of a brain region on the underlying white matter architecture. Here, we review the literature examining the macroscale coupling between structural and functional connectivity, and we establish how this structure-function coupling (SFC) can provide more information about the underlying workings of the brain than either feature alone. We begin by defining SFC and describing the computational methods used to quantify it. We then review empirical studies that examine the heterogeneous expression of SFC across different brain regions, among individuals, in the context of the cognitive task being performed, and over time, as well as its role in fostering flexible cognition. Last, we investigate how the coupling between structure and function is affected in neurological and psychiatric conditions, and we report how aberrant SFC is associated with disease duration and disease-specific cognitive impairment. By elucidating how the dynamic relationship between the structure and function of the brain is altered in the presence of neurological and psychiatric conditions, we aim to not only further our understanding of their aetiology but also establish SFC as a new and sensitive marker of disease symptomatology and cognitive performance. Overall, this Review collates the current knowledge regarding the regional interdependency between the macroscale structure and function of the human brain in both neurotypical and neuroatypical individuals.
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Affiliation(s)
- Panagiotis Fotiadis
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Anaesthesiology, University of Michigan, Ann Arbor, MI, USA.
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, USA.
- Santa Fe Institute, Santa Fe, NM, USA.
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33
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Escolà-Gascón Á. Our brains sense the future through a new quantum-like implicit learning mechanism. Brain Res Bull 2024; 216:111048. [PMID: 39128676 DOI: 10.1016/j.brainresbull.2024.111048] [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: 03/10/2024] [Revised: 07/31/2024] [Accepted: 08/08/2024] [Indexed: 08/13/2024]
Abstract
BACKGROUND Imagine if our brains could unconsciously predict future events. This study explores this concept, presenting evidence for an inherent 'foreseeing' ability, termed anomalous cognition (AC). We introduce a new experimentally verifiable approach to explain anomalous information anticipation (AIA), a type of AC, based on an innovative, quantum-like model of implicit learning, grounded in Nonlocal Plasticity Theory (NPT). METHODS Our research involved 203 participants using methods such as continuous flash suppression, random dot motion, and advanced 3D EEG neuroimaging, along with IBM quantum random event generators for precise measurements across 144 trials. These trials tested contingencies between undetectable sensory stimuli and dot movements, focusing on participants' prediction abilities. The design conditions were strictly experimental, violating fundamental classical learning principles, particularly reflex conditioning. If these principles were immutable, their violation would prevent any systematic behavioral changes, resulting in random responses. This violation was implemented through two quantum physics concepts: the mathematical principle of nonlocality and entanglement. RESULTS Despite the sensory stimulus being inaccessible, our results showed a significant prediction between the contingencies and an increase in AIA accuracy, with explained variances between 25 % and 48 %. EEG findings supported this, showing a positive link between brain activity in specific regions and AIA success. Electrochemical activations were detected in the posterior occipital cortex, the intraparietal sulcus, and the medial temporal gyri. AIA hits exceeded the threshold value corresponding to one standard deviation above the expected mean, showing moderate effect sizes in the experimental group (Cohen's d = 0.461). Analyzing the learning curve using the derivation technique, we identified the acceleration point of the wave function, indicating systematic implicit learning. This result showed that from repetition 63 onwards, AIA hits increased significantly. CONCLUSIONS The results suggest that, despite violating fundamental classical learning principles, cognitive processes produced changes in participants' responses susceptible to neuromodulation, considering quantum physics principles of nonlocality and entanglement (both present in NPT). We discuss (a) why the priming effect does not explain the significant results; (b) the potential discovery of a new form of quantum-like implicit learning, which could scientifically resolve phenomena associated with anomalous cognitions (e.g., AIA); and (c) future research directions, including potential applications and clinical impact.
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Affiliation(s)
- Álex Escolà-Gascón
- Department of Quantitative Methods and Statistics, Comillas Pontifical University, established by the Holy See, Vatican City State.
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34
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Castro P, Luppi A, Tagliazucchi E, Perl YS, Naci L, Owen AM, Sitt JD, Destexhe A, Cofré R. Dynamical structure-function correlations provide robust and generalizable signatures of consciousness in humans. Commun Biol 2024; 7:1224. [PMID: 39349600 PMCID: PMC11443142 DOI: 10.1038/s42003-024-06858-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 09/06/2024] [Indexed: 10/04/2024] Open
Abstract
Resting-state functional magnetic resonance imaging evolves through a repertoire of functional connectivity patterns which might reflect ongoing cognition, as well as the contents of conscious awareness. We investigated whether the dynamic exploration of these states can provide robust and generalizable markers for the state of consciousness in human participants, across loss of consciousness induced by general anaesthesia or slow wave sleep. By clustering transient states of functional connectivity, we demonstrated that brain activity during unconsciousness is dominated by a recurrent pattern primarily mediated by structural connectivity and with a reduced capacity to transition to other patterns. Our results provide evidence supporting the pronounced differences between conscious and unconscious brain states in terms of whole-brain dynamics; in particular, the maintenance of rich brain dynamics measured by entropy is a critical aspect of conscious awareness. Collectively, our results may have significant implications for our understanding of consciousness and the neural basis of human awareness, as well as for the discovery of robust signatures of consciousness that are generalizable among different brain conditions.
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Affiliation(s)
- Pablo Castro
- Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Centre National de la Recherche Scientifique (CNRS), Gif-sur-Yvette, France
- Cognitive Neuroimaging Unit, CEA, INSERM, Université Paris-Saclay, NeuroSpin Center, Gif-sur-Yvette, France
| | - Andrea Luppi
- Division of Anaesthesia and Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Enzo Tagliazucchi
- Buenos Aires Physics Institute and Physics Department, University of Buenos Aires, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), CABA, Buenos Aires, Argentina
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Yonatan S Perl
- Buenos Aires Physics Institute and Physics Department, University of Buenos Aires, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), CABA, Buenos Aires, Argentina
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, Inserm, CNRS, Paris, France
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Lorina Naci
- Trinity College Institute of Neuroscience Trinity College Dublin, Dublin, Ireland
- Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Adrian M Owen
- Departments of Physiology and Pharmacology and Psychology, Western University, London, Canada
| | - Jacobo D Sitt
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, Inserm, CNRS, Paris, France
| | - Alain Destexhe
- Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Centre National de la Recherche Scientifique (CNRS), Gif-sur-Yvette, France.
| | - Rodrigo Cofré
- Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Centre National de la Recherche Scientifique (CNRS), Gif-sur-Yvette, France.
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35
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Glick C, Gajawelli N, Sun Y, Badami F, Saggar M, Etkin A. Concurrent single-pulse (sp) TMS/fMRI to reveal the causal connectome in healthy and patient populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.25.614833. [PMID: 39386491 PMCID: PMC11463424 DOI: 10.1101/2024.09.25.614833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/12/2024]
Abstract
Neuroimaging and cognitive neuroscience studies have identified neural circuits linked to anxiety, mood, and trauma-related symptoms and focused on their interaction with the medial prefrontal default mode circuitry. Despite these advances, developing new neuromodulatory treatments based on neurocircuitry remains challenging. It remains unclear which nodes within and controlling these circuits are affected and how their impairment is connected to psychiatric symptoms. Concurrent single-pulse (sp) TMS/fMRI offers a promising approach to probing and mapping the integrity of these circuits. In this study, we present concurrent sp-TMS/fMRI data along with structural MRI scans from 152 participants, including both healthy and depressed individuals. The sp-TMS was administered to 11 different cortical sites, providing a dataset that allows researchers to investigate how brain circuits are modulated by spTMS.
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Affiliation(s)
- Cameron Glick
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Niharika Gajawelli
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Yinming Sun
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | | | - Manish Saggar
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
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36
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Stampacchia S, Asadi S, Tomczyk S, Ribaldi F, Scheffler M, Lövblad KO, Pievani M, Fall AB, Preti MG, Unschuld PG, Van De Ville D, Blanke O, Frisoni GB, Garibotto V, Amico E. Fingerprints of brain disease: connectome identifiability in Alzheimer's disease. Commun Biol 2024; 7:1169. [PMID: 39294332 PMCID: PMC11411139 DOI: 10.1038/s42003-024-06829-8] [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: 01/08/2024] [Accepted: 09/03/2024] [Indexed: 09/20/2024] Open
Abstract
Functional connectivity patterns in the human brain, like the friction ridges of a fingerprint, can uniquely identify individuals. Does this "brain fingerprint" remain distinct even during Alzheimer's disease (AD)? Using fMRI data from healthy and pathologically ageing subjects, we find that individual functional connectivity profiles remain unique and highly heterogeneous during mild cognitive impairment and AD. However, the patterns that make individuals identifiable change with disease progression, revealing a reconfiguration of the brain fingerprint. Notably, connectivity shifts towards functional system connections in AD and lower-order cognitive functions in early disease stages. These findings emphasize the importance of focusing on individual variability rather than group differences in AD studies. Individual functional connectomes could be instrumental in creating personalized models of AD progression, predicting disease course, and optimizing treatments, paving the way for personalized medicine in AD management.
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Affiliation(s)
- Sara Stampacchia
- Neuro-X Institute and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland.
| | - Saina Asadi
- Department of Radiology and Medical Informatics, Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
| | - Szymon Tomczyk
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
| | - Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland
| | - Max Scheffler
- Division of Radiology, Geneva University Hospitals, Geneva, Switzerland
| | - Karl-Olof Lövblad
- Department of Radiology and Medical Informatics, Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
- Neurodiagnostic and Neurointerventional Division, Geneva University Hospitals, Geneva, Switzerland
| | - Michela Pievani
- Lab of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Aïda B Fall
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland
| | - Maria Giulia Preti
- Neuro-X Institute and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Radiology and Medical Informatics, Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Paul G Unschuld
- Division of Geriatric Psychiatry, University Hospitals of Geneva (HUG), 1226, Thônex, Switzerland
- Department of Psychiatry, University of Geneva (UniGE), 1205, Geneva, Switzerland
| | - Dimitri Van De Ville
- Neuro-X Institute and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Department of Radiology and Medical Informatics, Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
| | - Olaf Blanke
- Neuro-X Institute and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Giovanni B Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland
- Geneva Memory Center, Department of Rehabilitation and Geriatrics, Geneva University Hospitals, Geneva, Switzerland
| | - Valentina Garibotto
- Department of Radiology and Medical Informatics, Geneva University Neurocenter, University of Geneva, Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospitals, Geneva, Switzerland
| | - Enrico Amico
- Neuro-X Institute and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland.
- School of Mathematics, University of Birmingham, Birmingham, UK.
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK.
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37
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Liu L, Li Z, Kong D, Huang Y, Wu D, Zhao H, Gao X, Zhang X, Yang M. Neuroimaging markers of aberrant brain activity and treatment response in schizophrenia patients based on brain complexity. Transl Psychiatry 2024; 14:365. [PMID: 39251595 PMCID: PMC11384759 DOI: 10.1038/s41398-024-03067-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 08/21/2024] [Accepted: 08/23/2024] [Indexed: 09/11/2024] Open
Abstract
The complexity of brain activity reflects its ability to process information, adapt to environmental changes, and transition between states. However, it remains unclear how schizophrenia (SZ) affects brain activity complexity, particularly its dynamic changes. This study aimed to investigate the abnormal patterns of brain activity complexity in SZ, their relationship with cognitive deficits, and the impact of antipsychotic medication. Forty-four drug-naive first-episode (DNFE) SZ patients and thirty demographically matched healthy controls (HC) were included. Functional MRI-based sliding window analysis was utilized for the first time to calculate weighted permutation entropy to characterize complex patterns of brain activity in SZ patients before and after 12 weeks of risperidone treatment. Results revealed reduced complexity in the caudate, putamen, and pallidum at baseline in SZ patients compared to HC, with reduced complexity in the left caudate positively correlated with Continuous Performance Test (CPT) and Category Fluency Test scores. After treatment, the complexity of the left caudate increased. Regions with abnormal complexity showed decreased functional connectivity, with complexity positively correlated with connectivity strength. We observed that the dynamic complexity of the brain exhibited the characteristic of spontaneous, recurring "complexity drop", potentially reflecting transient state transitions in the resting brain. Compared to HC, patients exhibited reduced scope, intensity, and duration of complexity drop, all of which improved after treatment. Reduced duration was negatively correlated with CPT scores and positively with clinical symptoms. The results suggest that abnormalities in brain activity complexity and its dynamic changes may underlie cognitive deficits and clinical symptoms in SZ patients. Antipsychotic treatment partially restores these abnormalities, highlighting their potential as indicators of treatment efficacy and biomarkers for personalized therapy.
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Affiliation(s)
- Liju Liu
- The Fourth People's Hospital of Chengdu, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, PR China
| | - Zezhi Li
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, PR China
| | - Di Kong
- The Fourth People's Hospital of Chengdu, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, PR China
| | - Yanqing Huang
- The Fourth People's Hospital of Chengdu, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, PR China
| | - Diwei Wu
- The Fourth People's Hospital of Chengdu, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, PR China
| | - Huachang Zhao
- The Fourth People's Hospital of Chengdu, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, PR China
| | - Xin Gao
- The Fourth People's Hospital of Chengdu, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, PR China
| | - Xiangyang Zhang
- Affiliated Mental Health Center of Anhui Medical University; Hefei Fourth People's Hospital; Anhui Mental Health Center, Hefei, PR China.
| | - Mi Yang
- The Fourth People's Hospital of Chengdu, The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, PR China.
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38
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Xie B, Ni H, Wang Y, Yao J, Xu Z, Zhu K, Bian S, Song P, Wu Y, Yu Y, Dong F. Dynamic Functional Network Connectivity in Acute Incomplete Cervical Cord Injury Patients and Its Associations With Sensorimotor Dysfunction Measures. World Neurosurg 2024:S1878-8750(24)01529-8. [PMID: 39243971 DOI: 10.1016/j.wneu.2024.08.160] [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: 08/29/2024] [Accepted: 08/30/2024] [Indexed: 09/09/2024]
Abstract
BACKGROUND Dynamic functional network connectivity (dFNC) captures temporal variations in functional connectivity during magnetic resonance imaging acquisition. However, the neural mechanisms driving dFNC alterations in the brain networks of patients with acute incomplete cervical cord injury (AICCI) remain unclear. METHODS This study included 16 AICCI patients and 16 healthy controls. Initially, independent component analysis was employed to extract whole-brain independent components from resting-state functional magnetic resonance imaging data. Subsequently, a sliding time window approach, combined with k-means clustering, was used to estimate dFNC states for each participant. Finally, a correlation analysis was conducted to examine the association between sensorimotor dysfunction scores in AICCI patients and the temporal characteristics of dFNC. RESULTS Independent component analysis was employed to extract 26 whole-brain independent components. Subsequent dynamic analysis identified 4 distinct connectivity states across the entire cohort. Notably, AICCI patients demonstrated a significant preference for State 3 compared to healthy controls, as evidenced by a higher frequency and longer duration spent in this state. Conversely, State 4 exhibited a reduced frequency and shorter dwell time in AICCI patients. Moreover, correlation analysis revealed a positive association between sensorimotor dysfunction and both the mean dwell time and the fraction of time spent in State 3. CONCLUSIONS Patients with AICCI demonstrate abnormal connectivity within dFNC states, and the temporal characteristics of dFNC are associated with sensorimotor dysfunction scores. These findings highlight the potential of dFNC as a sensitive biomarker for detecting network functional changes in AICCI patients, providing valuable insights into the dynamic alterations in brain connectivity related to sensorimotor dysfunction in this population.
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Affiliation(s)
- Bingyong Xie
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Haoyu Ni
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Ying Wang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jiyuan Yao
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Zhibin Xu
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Kun Zhu
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Sicheng Bian
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Peiwen Song
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yuanyuan Wu
- Department of Medical Imaging, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Fulong Dong
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
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39
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Luppi AI, Singleton SP, Hansen JY, Jamison KW, Bzdok D, Kuceyeski A, Betzel RF, Misic B. Contributions of network structure, chemoarchitecture and diagnostic categories to transitions between cognitive topographies. Nat Biomed Eng 2024; 8:1142-1161. [PMID: 39103509 PMCID: PMC11410673 DOI: 10.1038/s41551-024-01242-2] [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: 03/21/2023] [Accepted: 07/02/2024] [Indexed: 08/07/2024]
Abstract
The mechanisms linking the brain's network structure to cognitively relevant activation patterns remain largely unknown. Here, by leveraging principles of network control, we show how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic database. Specifically, we systematically integrated large-scale multimodal neuroimaging data from functional magnetic resonance imaging, diffusion tractography, cortical morphometry and positron emission tomography to simulate how anatomically guided transitions between cognitive states can be reshaped by neurotransmitter engagement or by changes in cortical thickness. Our model incorporates neurotransmitter-receptor density maps (18 receptors and transporters) and maps of cortical thickness pertaining to a wide range of mental health, neurodegenerative, psychiatric and neurodevelopmental diagnostic categories (17,000 patients and 22,000 controls). The results provide a comprehensive look-up table charting how brain network organization and chemoarchitecture interact to manifest different cognitive topographies, and establish a principled foundation for the systematic identification of ways to promote selective transitions between cognitive topographies.
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Affiliation(s)
- Andrea I Luppi
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
| | - S Parker Singleton
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Justine Y Hansen
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Keith W Jamison
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Danilo Bzdok
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- MILA, Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada
| | - Amy Kuceyeski
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Richard F Betzel
- Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Bratislav Misic
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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40
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Long H, Chen Z, Xu X, Zhou Q, Fang Z, Lv M, Yang XH, Xiao J, Sun H, Fan M. Elucidating genetic and molecular basis of altered higher-order brain structure-function coupling in major depressive disorder. Neuroimage 2024; 297:120722. [PMID: 38971483 DOI: 10.1016/j.neuroimage.2024.120722] [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: 03/25/2024] [Revised: 06/24/2024] [Accepted: 07/03/2024] [Indexed: 07/08/2024] Open
Abstract
Previous studies have shown that major depressive disorder (MDD) patients exhibit structural and functional impairments, but few studies have investigated changes in higher-order coupling between structure and function. Here, we systematically investigated the effect of MDD on higher-order coupling between structural connectivity (SC) and functional connectivity (FC). Each brain region was mapped into embedding vector by the node2vec algorithm. We used support vector machine (SVM) with the brain region embedding vector to distinguish MDD patients from health controls (HCs) and identify the most discriminative brain regions. Our study revealed that MDD patients had decreased higher-order coupling in connections between the most discriminative brain regions and local connections in rich-club organization and increased higher-order coupling in connections between the ventral attentional network and limbic network compared with HCs. Interestingly, transcriptome-neuroimaging association analysis demonstrated the correlations between regional rSC-FC coupling variations between MDD patients and HCs and α/β-hydrolase domain-containing 6 (ABHD6), β 1,3-N-acetylglucosaminyltransferase-9(β3GNT9), transmembrane protein 45B (TMEM45B), the correlation between regional dSC-FC coupling variations and retinoic acid early transcript 1E antisense RNA 1(RAET1E-AS1), and the correlations between regional iSC-FC coupling variations and ABHD6, β3GNT9, katanin-like 2 protein (KATNAL2). In addition, correlation analysis with neurotransmitter receptor/transporter maps found that the rSC-FC and iSC-FC coupling variations were both correlated with neuroendocrine transporter (NET) expression, and the dSC-FC coupling variations were correlated with metabotropic glutamate receptor 5 (mGluR5). Further mediation analysis explored the relationship between genes, neurotransmitter receptor/transporter and MDD related higher-order coupling variations. These findings indicate that specific genetic and molecular factors underpin the observed disparities in higher-order SC-FC coupling between MDD patients and HCs. Our study confirmed that higher-order coupling between SC and FC plays an important role in diagnosing MDD. The identification of new biological evidence for MDD etiology holds promise for the development of innovative antidepressant therapies.
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Affiliation(s)
- Haixia Long
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
| | - Zihao Chen
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
| | - Xinli Xu
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
| | - Qianwei Zhou
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
| | - Zhaolin Fang
- Network Information Center, Zhejiang University of Technology, Hangzhou 310023, China
| | - Mingqi Lv
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
| | - Xu-Hua Yang
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
| | - Jie Xiao
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
| | - Hui Sun
- College of Electrical Engineering, Sichuan University, Chengdu 610065, China.
| | - Ming Fan
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou 310018, China.
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Costa C, Pezzetta R, Masina F, Lago S, Gastaldon S, Frangi C, Genon S, Arcara G, Scarpazza C. Comprehensive investigation of predictive processing: A cross- and within-cognitive domains fMRI meta-analytic approach. Hum Brain Mapp 2024; 45:e26817. [PMID: 39169641 PMCID: PMC11339134 DOI: 10.1002/hbm.26817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 07/15/2024] [Accepted: 07/30/2024] [Indexed: 08/23/2024] Open
Abstract
Predictive processing (PP) stands as a predominant theoretical framework in neuroscience. While some efforts have been made to frame PP within a cognitive domain-general network perspective, suggesting the existence of a "prediction network," these studies have primarily focused on specific cognitive domains or functions. The question of whether a domain-general predictive network that encompasses all well-established cognitive domains exists remains unanswered. The present meta-analysis aims to address this gap by testing the hypothesis that PP relies on a large-scale network spanning across cognitive domains, supporting PP as a unified account toward a more integrated approach to neuroscience. The Activation Likelihood Estimation meta-analytic approach was employed, along with Meta-Analytic Connectivity Mapping, conjunction analysis, and behavioral decoding techniques. The analyses focused on prediction incongruency and prediction congruency, two conditions likely reflective of core phenomena of PP. Additionally, the analysis focused on a prediction phenomena-independent dimension, regardless of prediction incongruency and congruency. These analyses were first applied to each cognitive domain considered (cognitive control, attention, motor, language, social cognition). Then, all cognitive domains were collapsed into a single, cross-domain dimension, encompassing a total of 252 experiments. Results pertaining to prediction incongruency rely on a defined network across cognitive domains, while prediction congruency results exhibited less overall activation and slightly more variability across cognitive domains. The converging patterns of activation across prediction phenomena and cognitive domains highlight the role of several brain hubs unfolding within an organized large-scale network (Dynamic Prediction Network), mainly encompassing bilateral insula, frontal gyri, claustrum, parietal lobules, and temporal gyri. Additionally, the crucial role played at a cross-domain, multimodal level by the anterior insula, as evidenced by the conjunction and Meta-Analytic Connectivity Mapping analyses, places it as the major hub of the Dynamic Prediction Network. Results support the hypothesis that PP relies on a domain-general, large-scale network within whose regions PP units are likely to operate, depending on the context and environmental demands. The wide array of regions within the Dynamic Prediction Network seamlessly integrate context- and stimulus-dependent predictive computations, thereby contributing to the adaptive updating of the brain's models of the inner and external world.
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Affiliation(s)
| | | | | | - Sara Lago
- Padova Neuroscience CenterPaduaItaly
- IRCCS Ospedale San CamilloVeniceItaly
| | - Simone Gastaldon
- Padova Neuroscience CenterPaduaItaly
- Dipartimento di Psicologia dello Sviluppo e della SocializzazioneUniversità degli Studi di PadovaPaduaItaly
| | - Camilla Frangi
- Dipartimento di Psicologia GeneraleUniversità degli Studi di PadovaPaduaItaly
| | - Sarah Genon
- Institute for Systems NeuroscienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM‐7)Research Centre JülichJülichGermany
| | | | - Cristina Scarpazza
- IRCCS Ospedale San CamilloVeniceItaly
- Dipartimento di Psicologia GeneraleUniversità degli Studi di PadovaPaduaItaly
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Khanra P, Nakuci J, Muldoon S, Watanabe T, Masuda N. Reliability of energy landscape analysis of resting-state functional MRI data. Eur J Neurosci 2024; 60:4265-4290. [PMID: 38837814 DOI: 10.1111/ejn.16390] [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: 07/05/2023] [Revised: 04/05/2024] [Accepted: 04/25/2024] [Indexed: 06/07/2024]
Abstract
Energy landscape analysis is a data-driven method to analyse multidimensional time series, including functional magnetic resonance imaging (fMRI) data. It has been shown to be a useful characterization of fMRI data in health and disease. It fits an Ising model to the data and captures the dynamics of the data as movement of a noisy ball constrained on the energy landscape derived from the estimated Ising model. In the present study, we examine test-retest reliability of the energy landscape analysis. To this end, we construct a permutation test that assesses whether or not indices characterizing the energy landscape are more consistent across different sets of scanning sessions from the same participant (i.e. within-participant reliability) than across different sets of sessions from different participants (i.e. between-participant reliability). We show that the energy landscape analysis has significantly higher within-participant than between-participant test-retest reliability with respect to four commonly used indices. We also show that a variational Bayesian method, which enables us to estimate energy landscapes tailored to each participant, displays comparable test-retest reliability to that using the conventional likelihood maximization method. The proposed methodology paves the way to perform individual-level energy landscape analysis for given data sets with a statistically controlled reliability.
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Affiliation(s)
- Pitambar Khanra
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, USA
| | - Johan Nakuci
- School of Psychology, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Sarah Muldoon
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, USA
- Institute for Artificial Intelligence and Data Science, State University of New York at Buffalo, Buffalo, New York, USA
| | - Takamitsu Watanabe
- International Research Centre for Neurointelligence, The University of Tokyo, Tokyo, Japan
| | - Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, Buffalo, New York, USA
- Institute for Artificial Intelligence and Data Science, State University of New York at Buffalo, Buffalo, New York, USA
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43
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Voits T, DeLuca V, Hao J, Elin K, Abutalebi J, Duñabeitia JA, Berglund G, Gabrielsen A, Rook J, Thomsen H, Waagen P, Rothman J. Degree of multilingual engagement modulates resting state oscillatory activity across the lifespan. Neurobiol Aging 2024; 140:70-80. [PMID: 38735176 DOI: 10.1016/j.neurobiolaging.2024.04.009] [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: 01/05/2024] [Revised: 03/18/2024] [Accepted: 04/19/2024] [Indexed: 05/14/2024]
Abstract
Multilingualism has been demonstrated to lead to a more favorable trajectory of neurocognitive aging, yet our understanding of its effect on neurocognition across the lifespan remains limited. We collected resting state EEG recordings from a sample of multilingual individuals across a wide age range. Additionally, we obtained data on participant multilingual language use patterns alongside other known lifestyle enrichment factors. Language experience was operationalized via a modified multilingual diversity (MLD) score. Generalized additive modeling was employed to examine the effects and interactions of age and MLD on resting state oscillatory power and coherence. The data suggest an independent modulatory effect of individualized multilingual engagement on age-related differences in whole brain resting state power across alpha and theta bands, and an interaction between age and MLD on resting state coherence in alpha, theta, and low beta. These results provide evidence of multilingual engagement as an independent correlational factor related to differences in resting state EEG power, consistent with the claim that multilingualism can serve as a protective factor in neurocognitive aging.
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Affiliation(s)
- Toms Voits
- Department of Psychology, University of Gothenburg, Gothenburg, Sweden; UiT the Arctic University of Norway, Tromsø, Norway.
| | | | - Jiuzhou Hao
- UiT the Arctic University of Norway, Tromsø, Norway
| | - Kirill Elin
- UiT the Arctic University of Norway, Tromsø, Norway
| | - Jubin Abutalebi
- UiT the Arctic University of Norway, Tromsø, Norway; Centre for Neurolinguistics and Psycholinguistics (CNPL), Vita-Salute San Raffaele University, Milan, Italy
| | - Jon Andoni Duñabeitia
- UiT the Arctic University of Norway, Tromsø, Norway; Universidad Nebrija Research Center in Cognition (CINC), Nebrija University, Madrid, Spain
| | | | | | - Janine Rook
- Department of Applied Linguistics, University of Groningen, Groningen, the Netherlands
| | - Hilde Thomsen
- UiT the Arctic University of Norway, Tromsø, Norway; Université Côte d'Azur, Nice, France
| | | | - Jason Rothman
- UiT the Arctic University of Norway, Tromsø, Norway; Universidad Nebrija Research Center in Cognition (CINC), Nebrija University, Madrid, Spain
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44
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Lum JAG, Barham MP, Hill AT. Pupillometry reveals resting state alpha power correlates with individual differences in adult auditory language comprehension. Cortex 2024; 177:1-14. [PMID: 38821014 DOI: 10.1016/j.cortex.2024.02.019] [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: 11/06/2023] [Revised: 02/09/2024] [Accepted: 02/12/2024] [Indexed: 06/02/2024]
Abstract
Although individual differences in adult language processing are well-documented, the neural basis of this variability remains largely unexplored. The current study addressed this gap in the literature by examining the relationship between resting state alpha activity and individual differences in auditory language comprehension. Alpha oscillations modulate cortical excitability, facilitating efficient information processing in the brain. While resting state alpha oscillations have been tied to individual differences in cognitive performance, their association with auditory language comprehension is less clear. Participants in the study were 80 healthy adults with a mean age of 25.8 years (SD = 7.2 years). Resting state alpha activity was acquired using electroencephalography while participants looked at a benign stimulus for 3 min. Participants then completed a language comprehension task that involved listening to 'syntactically simple' subject-relative clause sentences and 'syntactically complex' object-relative clause sentences. Pupillometry measured real-time processing demand changes, with larger pupil dilation indicating increased processing loads. Replicating past research, comprehending object relative clauses, compared to subject relative clauses, was associated with lower accuracy, slower reaction times, and larger pupil dilation. Resting state alpha power was found to be positively correlated with the pupillometry data. That is, participants with higher resting state alpha activity evidenced larger dilation during sentence comprehension. This effect was more pronounced for the 'complex' object sentences compared to the 'simple' subject sentences. These findings suggest the brain's capacity to generate a robust resting alpha rhythm contributes to variability in processing demands associated with auditory language comprehension, especially when faced with challenging syntactic structures. More generally, the study demonstrates that the intrinsic functional architecture of the brain likely influences individual differences in language comprehension.
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Affiliation(s)
- Jarrad A G Lum
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Australia.
| | - Michael P Barham
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Australia
| | - Aron T Hill
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Australia
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45
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Ghaziri J, Fei P, Tucholka A, Obaid S, Boucher O, Rouleau I, Nguyen DK. Resting-State Functional Connectivity Profile of Insular Subregions. Brain Sci 2024; 14:742. [PMID: 39199437 PMCID: PMC11352390 DOI: 10.3390/brainsci14080742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2024] [Revised: 07/18/2024] [Accepted: 07/24/2024] [Indexed: 09/01/2024] Open
Abstract
The insula is often considered the fifth lobe of the brain and is increasingly recognized as one of the most connected regions in the brain, with widespread connections to cortical and subcortical structures. As a follow-up to our previous tractography work, we investigated the resting-state functional connectivity (rsFC) profiles of insular subregions and assessed their concordance with structural connectivity. We used the CONN toolbox to analyze the rsFC of the same 19 insular regions of interest (ROIs) we used in our prior tractography work and regrouped them into six subregions based on their connectivity pattern similarity. Our analysis of 50 healthy participants confirms the known broad connectivity of the insula and shows novel and specific whole-brain and intra-connectivity patterns of insular subregions. By examining such subregions, our findings provide a more detailed pattern of connectivity than prior studies that may prove useful for comparison between patients.
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Affiliation(s)
- Jimmy Ghaziri
- Département de Psychologie, Université du Québec à Montréal, Montréal, QC H2X 3P2, Canada
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), Montréal, QC H2X 0A9, Canada
| | - Phillip Fei
- Faculté de Médecine et des Sciences de la Santé, Université de Sherbrooke, Chicoutimi, QC J4L 1C9, Canada
| | - Alan Tucholka
- BarcelonaBeta Brain Research Center, Pasqual Maragall Foundation, 08005 Barcelona, Spain
- Pixyl Medical, 38700 Grenoble, France
| | - Sami Obaid
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), Montréal, QC H2X 0A9, Canada
| | - Olivier Boucher
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), Montréal, QC H2X 0A9, Canada
- Service de Neurologie, Centre Hospitalier de l’Université de Montréal (CHUM), Montréal, QC H2X 0C1, Canada
| | - Isabelle Rouleau
- Département de Psychologie, Université du Québec à Montréal, Montréal, QC H2X 3P2, Canada
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), Montréal, QC H2X 0A9, Canada
| | - Dang K. Nguyen
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), Montréal, QC H2X 0A9, Canada
- Service de Neurologie, Centre Hospitalier de l’Université de Montréal (CHUM), Montréal, QC H2X 0C1, Canada
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Khoo SY, Lai WH, On SH, On YY, Adam BM, Law WC, Ng BHS, Fong AYY, Anselm ST. Resting-state electroencephalography (EEG) microstates of healthy individuals following mild sleep deprivation. Sci Rep 2024; 14:16820. [PMID: 39039219 PMCID: PMC11263689 DOI: 10.1038/s41598-024-67902-0] [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: 03/22/2024] [Accepted: 07/17/2024] [Indexed: 07/24/2024] Open
Abstract
Mild sleep deprivation is widespread in many societies worldwide. Electroencephalography (EEG) microstate analysis provides information on spatial and temporal characteristics of resting brain network, serving as an indicator of neurophysiological activities at rest. This study seeks to investigate potential neural markers in EEG following mild sleep deprivation of a single night using EEG microstate analysis. Six-minute resting EEG was conducted on thirty healthy adults within 6 hours of waking in the morning and after at least 18 h of sleep deprivation. Translated and validated Malay language Karolinska Sleepiness Scale was used to assess the participants' degree of sleepiness. Microstate characteristics analysis was conducted on the final 24 subjects based on four standard microstate maps. Microstate C shows a significant increase in mean duration, coverage and occurrence, while microstate D has significantly higher occurrence after sleep deprivation. This study demonstrates notable changes in resting state EEG microstates following mild sleep deprivation. Present findings deepen our understanding of the brain's spatiotemporal dynamics under this condition and suggest the potential utility of neural markers in this domain as components of composite markers for sleep deprivation.
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Affiliation(s)
- Sing Yee Khoo
- Faculty of Medicine and Health Sciences, Universiti Malaysia Sarawak, Jalan Datuk Mohammad Musa, 94300, Kota Samarahan, Sarawak, Malaysia.
- Clinical Research Centre, Institutes for Clinical Research, National Institutes of Health, Sarawak General Hospital, Jalan Hospital, 93586, Kuching, Sarawak, Malaysia.
| | - Wei Hong Lai
- Clinical Research Centre, Institutes for Clinical Research, National Institutes of Health, Sarawak General Hospital, Jalan Hospital, 93586, Kuching, Sarawak, Malaysia
| | - Shin Hui On
- Yong Loo Lin School of Medicine, National University of Singapore, 21 Lower Kent Ridge Road, Singapore, 119077, Singapore
| | - Yue Yuan On
- School of Biological Sciences, Nanyang Technological University, 50 Nanyang Ave, Singapore, 639798, Singapore
| | - Bujang Mohamad Adam
- Clinical Research Centre, Institutes for Clinical Research, National Institutes of Health, Sarawak General Hospital, Jalan Hospital, 93586, Kuching, Sarawak, Malaysia
| | - Wan Chung Law
- Neurology Department, Sarawak General Hospital, Jalan Hospital, Ministry of Health, 93586, Kuching, Sarawak, Malaysia
| | - Benjamin Han Sim Ng
- Neurology Department, Sibu General Hospital, Ministry of Health, KM 5 ½, Jalan Ulu Oya, Pekan Sibu, 96000, Sibu, Sarawak, Malaysia
| | - Alan Yean Yip Fong
- Clinical Research Centre, Institutes for Clinical Research, National Institutes of Health, Sarawak General Hospital, Jalan Hospital, 93586, Kuching, Sarawak, Malaysia
| | - Su Ting Anselm
- Faculty of Medicine and Health Sciences, Universiti Malaysia Sarawak, Jalan Datuk Mohammad Musa, 94300, Kota Samarahan, Sarawak, Malaysia
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Klug S, Murgaš M, Godbersen GM, Hacker M, Lanzenberger R, Hahn A. Synaptic signaling modeled by functional connectivity predicts metabolic demands of the human brain. Neuroimage 2024; 295:120658. [PMID: 38810891 DOI: 10.1016/j.neuroimage.2024.120658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 04/22/2024] [Accepted: 05/27/2024] [Indexed: 05/31/2024] Open
Abstract
PURPOSE The human brain is characterized by interacting large-scale functional networks fueled by glucose metabolism. Since former studies could not sufficiently clarify how these functional connections shape glucose metabolism, we aimed to provide a neurophysiologically-based approach. METHODS 51 healthy volunteers underwent simultaneous PET/MRI to obtain BOLD functional connectivity and [18F]FDG glucose metabolism. These multimodal imaging proxies of fMRI and PET were combined in a whole-brain extension of metabolic connectivity mapping. Specifically, functional connectivity of all brain regions were used as input to explain glucose metabolism of a given target region. This enabled the modeling of postsynaptic energy demands by incoming signals from distinct brain regions. RESULTS Functional connectivity input explained a substantial part of metabolic demands but with pronounced regional variations (34 - 76%). During cognitive task performance this multimodal association revealed a shift to higher network integration compared to resting state. In healthy aging, a dedifferentiation (decreased segregated/modular structure of the brain) of brain networks during rest was observed. Furthermore, by including data from mRNA maps, [11C]UCB-J synaptic density and aerobic glycolysis (oxygen-to-glucose index from PET data), we show that whole-brain functional input reflects non-oxidative, on-demand metabolism of synaptic signaling. The metabolically-derived directionality of functional inputs further marked them as top-down predictions. In addition, the approach uncovered formerly hidden networks with superior efficiency through metabolically informed network partitioning. CONCLUSIONS Applying multimodal imaging, we decipher a crucial part of the metabolic and neurophysiological basis of functional connections in the brain as interregional on-demand synaptic signaling fueled by anaerobic metabolism. The observed task- and age-related effects indicate promising future applications to characterize human brain function and clinical alterations.
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Affiliation(s)
- Sebastian Klug
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Matej Murgaš
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Godber M Godbersen
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria.
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48
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Idesis S, Allegra M, Vohryzek J, Perl YS, Metcalf NV, Griffis JC, Corbetta M, Shulman GL, Deco G. Generative whole-brain dynamics models from healthy subjects predict functional alterations in stroke at the level of individual patients. Brain Commun 2024; 6:fcae237. [PMID: 39077378 PMCID: PMC11285191 DOI: 10.1093/braincomms/fcae237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 05/13/2024] [Accepted: 07/12/2024] [Indexed: 07/31/2024] Open
Abstract
Computational whole-brain models describe the resting activity of each brain region based on a local model, inter-regional functional interactions, and a structural connectome that specifies the strength of inter-regional connections. Strokes damage the healthy structural connectome that forms the backbone of these models and produce large alterations in inter-regional functional interactions. These interactions are typically measured by correlating the time series of the activity between two brain regions in a process, called resting functional connectivity. We show that adding information about the structural disconnections produced by a patient's lesion to a whole-brain model previously trained on structural and functional data from a large cohort of healthy subjects enables the prediction of the resting functional connectivity of the patient and fits the model directly to the patient's data (Pearson correlation = 0.37; mean square error = 0.005). Furthermore, the model dynamics reproduce functional connectivity-based measures that are typically abnormal in stroke patients and measures that specifically isolate these abnormalities. Therefore, although whole-brain models typically involve a large number of free parameters, the results show that, even after fixing those parameters, the model reproduces results from a population very different than that on which the model was trained. In addition to validating the model, these results show that the model mechanistically captures the relationships between the anatomical structure and the functional activity of the human brain.
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Affiliation(s)
- Sebastian Idesis
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Barcelona, Catalonia 08005, Spain
| | - Michele Allegra
- Padova Neuroscience Center (PNC), University of Padova, Padova 35129, Italy
- Department of Physics and Astronomy ‘G. Galilei’, University of Padova, 35131 Padova, Italy
| | - Jakub Vohryzek
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Barcelona, Catalonia 08005, Spain
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, OX3 9BX, Oxford, UK
| | - Yonatan Sanz Perl
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Barcelona, Catalonia 08005, Spain
- Universidad de San Andrés, Centro de Neurociencias Cognitivias, NC1006ACC, Buenos Aires, Argentina
- National Scientific and Technical Research Council, C1425FQB, Buenos Aires, Argentina
- Institut du Cerveau et de la Moelle épinière, ICM, Hôpital Pitié Salpêtrière, 75013 Paris, France
| | - Nicholas V Metcalf
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Joseph C Griffis
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Maurizio Corbetta
- Padova Neuroscience Center (PNC), University of Padova, Padova 35129, Italy
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Neuroscience (DNS), University of Padova, Padova 35128, Italy
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- VIMM, Venetian Institute of Molecular Medicine (VIMM), Biomedical Foundation, Padova 35129, Italy
| | - Gordon L Shulman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Gustavo Deco
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Barcelona, Catalonia 08005, Spain
- Institució Catalana de Recerca I Estudis Avançats (ICREA), Barcelona, Catalonia 08010, Spain
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49
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Tanner J, Faskowitz J, Teixeira AS, Seguin C, Coletta L, Gozzi A, Mišić B, Betzel RF. A multi-modal, asymmetric, weighted, and signed description of anatomical connectivity. Nat Commun 2024; 15:5865. [PMID: 38997282 PMCID: PMC11245624 DOI: 10.1038/s41467-024-50248-6] [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: 08/08/2023] [Accepted: 07/01/2024] [Indexed: 07/14/2024] Open
Abstract
The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are either assigned based on imaging features-e.g. diffusion parameters-or inferred using statistical models. In reality, the ground-truth weights are unknown, motivating the exploration of alternative edge weighting schemes. Here, we explore a multi-modal, regression-based model that endows reconstructed fiber tracts with directed and signed weights. We find that the model fits observed data well, outperforming a suite of null models. The estimated weights are subject-specific and highly reliable, even when fit using relatively few training samples, and the networks maintain a number of desirable features. In summary, we offer a simple framework for weighting connectome data, demonstrating both its ease of implementation while benchmarking its utility for typical connectome analyses, including graph theoretic modeling and brain-behavior associations.
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Affiliation(s)
- Jacob Tanner
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Andreia Sofia Teixeira
- LASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - Caio Seguin
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | | | - Alessandro Gozzi
- Functional Neuroimaging Lab, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems, Rovereto, Italy
| | - Bratislav Mišić
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Richard F Betzel
- Cognitive Science Program, Indiana University, Bloomington, IN, USA.
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA.
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
- Program in Neuroscience, Indiana University, Bloomington, IN, USA.
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50
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Herbert C, Northoff G. Editorial: Analyzing and computing humans - the role of language, culture, brain and health. Front Hum Neurosci 2024; 18:1439729. [PMID: 39015823 PMCID: PMC11250248 DOI: 10.3389/fnhum.2024.1439729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Accepted: 06/06/2024] [Indexed: 07/18/2024] Open
Affiliation(s)
- Cornelia Herbert
- Applied Emotion and Motivation Psychology, Institute of Psychology and Education, Faculty of Engineering, Computer Science and Psychology, Ulm University, Ulm, Germany
| | - Georg Northoff
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada
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