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Zhen Y, Yang Y, Zheng Y, Wang X, Liu L, Zheng Z, Zheng H, Tang S. The heritability and structural correlates of resting-state fMRI complexity. Neuroimage 2024:120657. [PMID: 38810892 DOI: 10.1016/j.neuroimage.2024.120657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Revised: 05/24/2024] [Accepted: 05/26/2024] [Indexed: 05/31/2024] Open
Abstract
The complexity of fMRI signals quantifies temporal dynamics of spontaneous neural activity, which has been increasingly recognized as providing important insights into cognitive functions and psychiatric disorders. However, its heritability and structural underpinnings are not well understood. Here, we utilize multi-scale sample entropy to extract resting-state fMRI complexity in a large healthy adult sample from the Human Connectome Project. We show that fMRI complexity at multiple time scales is heritable in broad brain regions. Heritability estimates are modest and regionally variable. We relate fMRI complexity to brain structure including surface area, cortical myelination, cortical thickness, subcortical volumes, and total brain volume. We find that surface area is negatively correlated with fine-scale complexity and positively correlated with coarse-scale complexity in most cortical regions, especially the association cortex. Most of these correlations are related to common genetic and environmental effects. We also find positive correlations between cortical myelination and fMRI complexity at fine scales and negative correlations at coarse scales in the prefrontal cortex, lateral temporal lobe, precuneus, lateral parietal cortex, and cingulate cortex, with these correlations mainly attributed to common environmental effects. We detect few significant associations between fMRI complexity and cortical thickness. Despite the non-significant association with total brain volume, fMRI complexity exhibits significant correlations with subcortical volumes in the hippocampus, cerebellum, putamen, and pallidum at certain scales. Collectively, our work establishes the genetic basis and structural correlates of resting-state fMRI complexity across multiple scales, supporting its potential application as an endophenotype for psychiatric disorders.
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Affiliation(s)
- Yi Zhen
- School of Mathematical Sciences, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China
| | - Yaqian Yang
- School of Mathematical Sciences, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China
| | - Yi Zheng
- School of Mathematical Sciences, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China
| | - Xin Wang
- Institute of Artificial Intelligence, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China; Zhongguancun Laboratory, Beijing 100094, China; Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China; PengCheng Laboratory, Shenzhen 518055, China
| | - Longzhao Liu
- Institute of Artificial Intelligence, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China; Zhongguancun Laboratory, Beijing 100094, China; Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China; PengCheng Laboratory, Shenzhen 518055, China
| | - Zhiming Zheng
- Institute of Artificial Intelligence, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China; Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai 264003, China; Zhongguancun Laboratory, Beijing 100094, China; Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China; PengCheng Laboratory, Shenzhen 518055, China; State Key Lab of Software Development Environment, Beihang University, Beijing 100191, China
| | - Hongwei Zheng
- Beijing Academy of Blockchain and Edge Computing, Beijing 100085, China.
| | - Shaoting Tang
- Institute of Artificial Intelligence, Beihang University, Beijing 100191, China; Key laboratory of Mathematics, Informatics and Behavioral Semantics, Beihang University, Beijing 100191, China; Institute of Medical Artificial Intelligence, Binzhou Medical University, Yantai 264003, China; Zhongguancun Laboratory, Beijing 100094, China; Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China; PengCheng Laboratory, Shenzhen 518055, China; State Key Lab of Software Development Environment, Beihang University, Beijing 100191, China.
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Hirsch F, Bumanglag Â, Zhang Y, Wohlschlaeger A. Diverging functional connectivity timescales: Capturing distinct aspects of cognitive performance in early psychosis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.07.24306932. [PMID: 38766002 PMCID: PMC11100938 DOI: 10.1101/2024.05.07.24306932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Background Psychosis spectrum disorders (PSDs) are marked by cognitive impairments, the neurobiological correlates of which remain poorly understood. Here, we investigate the entropy of time-varying functional connectivity (TVFC) patterns from resting-state fMRI (rfMRI) as potential biomarker for cognitive performance in PSDs. By combining our results with multimodal reference data, we hope to generate new insights into the mechanisms underlying cognitive dysfunction in PSDs. We hypothesized that low-entropy TVFC patterns (LEN) would be more behaviorally informative than high-entropy TVFC patterns (HEN), especially for tasks that require extensive integration across diverse cognitive subdomains. Methods rfMRI and behavioral data from 97 patients in the early phases of psychosis and 53 controls were analyzed. Positron-Emission Tomography (PET) and magnetoencephalography (MEG) data were taken from a public repository (Hansen et al., 2022). Multivariate analyses were conducted to examine relationships between TVFC patterns at multiple spatial scales and cognitive performance in patients. Results Compared to HEN, LEN explained significantly more cognitive variance on average in PSD patients, driven by superior encoding of information on psychometrically more integrated tasks. HEN better captured information in specific subdomains of executive functioning. Nodal HEN-LEN transitions were spatially aligned with neurobiological gradients reflecting monoaminergic transporter densities and MEG beta power. Exploratory analyses revealed a close statistical relationship between LEN and positive PSD symptoms. Conclusion Our entropy-based analysis of TVFC patterns dissociates distinct aspects of cognition in PSDs. By linking topographies of neurotransmission and oscillatory dynamics with cognitive performance, it enhances our understanding of the mechanisms underlying cognitive deficits in PSDs. CRediT Authorship Contribution Statement Fabian Hirsch: Conceptualization, Methodology, Software, Formal analysis, Writing - Original Draft, Writing - Review & Editing, Visualization; Ângelo Bumanglag: Methodology, Software, Formal analysis, Writing - Review & Editing; Yifei Zhang: Methodology, Software, Formal analysis, Writing - Review & Editing; Afra Wohlschlaeger: Methodology, Writing - Review & Editing, Supervision, Project administration.
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Vaidya N, Marquand AF, Nees F, Siehl S, Schumann G. The impact of psychosocial adversity on brain and behaviour: an overview of existing knowledge and directions for future research. Mol Psychiatry 2024:10.1038/s41380-024-02556-y. [PMID: 38658773 DOI: 10.1038/s41380-024-02556-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 04/03/2024] [Accepted: 04/08/2024] [Indexed: 04/26/2024]
Abstract
Environmental experiences play a critical role in shaping the structure and function of the brain. Its plasticity in response to different external stimuli has been the focus of research efforts for decades. In this review, we explore the effects of adversity on brain's structure and function and its implications for brain development, adaptation, and the emergence of mental health disorders. We are focusing on adverse events that emerge from the immediate surroundings of an individual, i.e., microenvironment. They include childhood maltreatment, peer victimisation, social isolation, affective loss, domestic conflict, and poverty. We also take into consideration exposure to environmental toxins. Converging evidence suggests that different types of adversity may share common underlying mechanisms while also exhibiting unique pathways. However, they are often studied in isolation, limiting our understanding of their combined effects and the interconnected nature of their impact. The integration of large, deep-phenotyping datasets and collaborative efforts can provide sufficient power to analyse high dimensional environmental profiles and advance the systematic mapping of neuronal mechanisms. This review provides a background for future research, highlighting the importance of understanding the cumulative impact of various adversities, through data-driven approaches and integrative multimodal analysis techniques.
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Affiliation(s)
- Nilakshi Vaidya
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Clinical Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany.
| | - Andre F Marquand
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Frauke Nees
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
| | - Sebastian Siehl
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Clinical Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany
- Centre for Population Neuroscience and Stratified Medicine (PONS), Institute for Science and Technology of Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, China
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Zhu K, Chang J, Zhang S, Li Y, Zuo J, Ni H, Xie B, Yao J, Xu Z, Bian S, Yan T, Wu X, Chen S, Jin W, Wang Y, Xu P, Song P, Wu Y, Shen C, Zhu J, Yu Y, Dong F. The enhanced connectivity between the frontoparietal, somatomotor network and thalamus as the most significant network changes of chronic low back pain. Neuroimage 2024; 290:120558. [PMID: 38437909 DOI: 10.1016/j.neuroimage.2024.120558] [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: 10/28/2023] [Revised: 02/22/2024] [Accepted: 02/27/2024] [Indexed: 03/06/2024] Open
Abstract
The prolonged duration of chronic low back pain (cLBP) inevitably leads to changes in the cognitive, attentional, sensory and emotional processing brain regions. Currently, it remains unclear how these alterations are manifested in the interplay between brain functional and structural networks. This study aimed to predict the Oswestry Disability Index (ODI) in cLBP patients using multimodal brain magnetic resonance imaging (MRI) data and identified the most significant features within the multimodal networks to aid in distinguishing patients from healthy controls (HCs). We constructed dynamic functional connectivity (dFC) and structural connectivity (SC) networks for all participants (n = 112) and employed the Connectome-based Predictive Modeling (CPM) approach to predict ODI scores, utilizing various feature selection thresholds to identify the most significant network change features in dFC and SC outcomes. Subsequently, we utilized these significant features for optimal classifier selection and the integration of multimodal features. The results revealed enhanced connectivity among the frontoparietal network (FPN), somatomotor network (SMN) and thalamus in cLBP patients compared to HCs. The thalamus transmits pain-related sensations and emotions to the cortical areas through the dorsolateral prefrontal cortex (dlPFC) and primary somatosensory cortex (SI), leading to alterations in whole-brain network functionality and structure. Regarding the model selection for the classifier, we found that Support Vector Machine (SVM) best fit these significant network features. The combined model based on dFC and SC features significantly improved classification performance between cLBP patients and HCs (AUC=0.9772). Finally, the results from an external validation set support our hypotheses and provide insights into the potential applicability of the model in real-world scenarios. Our discovery of enhanced connectivity between the thalamus and both the dlPFC (FPN) and SI (SMN) provides a valuable supplement to prior research on cLBP.
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Affiliation(s)
- Kun Zhu
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Jianchao Chang
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Siya Zhang
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; School of Basic Medical Sciences, Anhui Medical University, Hefei, PR China
| | - Yan Li
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Junxun Zuo
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Haoyu Ni
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Bingyong Xie
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Jiyuan Yao
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Zhibin Xu
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Sicheng Bian
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Tingfei Yan
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Xianyong Wu
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Orthopedics, Anqing First People's Hospital of Anhui Medical University, Anqing, PR China
| | - Senlin Chen
- Department of Orthopedics, Dongcheng branch of The First Affiliated Hospital of Anhui Medical University (Feidong People's Hospital), Hefei, PR China
| | - Weiming Jin
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Ying Wang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Peng Xu
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Peiwen Song
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Yuanyuan Wu
- Department of Medical Imaging, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Cailiang Shen
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China
| | - Fulong Dong
- Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; School of Basic Medical Sciences, Anhui Medical University, Hefei, PR China.
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Joliot M, Cremona S, Tzourio C, Etard O. Modulate the impact of the drowsiness on the resting state functional connectivity. Sci Rep 2024; 14:8652. [PMID: 38622265 PMCID: PMC11018752 DOI: 10.1038/s41598-024-59476-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 04/11/2024] [Indexed: 04/17/2024] Open
Abstract
This research explores different methodologies to modulate the effects of drowsiness on functional connectivity (FC) during resting-state functional magnetic resonance imaging (RS-fMRI). The study utilized a cohort of students (MRi-Share) and classified individuals into drowsy, alert, and mixed/undetermined states based on observed respiratory oscillations. We analyzed the FC group difference between drowsy and alert individuals after five different processing methods: the reference method, two based on physiological and a global signal regression of the BOLD time series signal, and two based on Gaussian standardizations of the FC distribution. According to the reference method, drowsy individuals exhibit higher cortico-cortical FC than alert individuals. First, we demonstrated that each method reduced the differences between drowsy and alert states. The second result is that the global signal regression was quantitively the most effective, minimizing significant FC differences to only 3.3% of the total FCs. However, one should consider the risks of overcorrection often associated with this methodology. Therefore, choosing a less aggressive form of regression, such as the physiological method or Gaussian-based approaches, might be a more cautious approach. Third and last, using the Gaussian-based methods, cortico-subcortical and intra-default mode network (DMN) FCs were significantly greater in alert than drowsy subjects. These findings bear resemblance to the anticipated patterns during the onset of sleep, where the cortex isolates itself to assist in transitioning into deeper slow wave sleep phases, simultaneously disconnecting the DMN.
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Affiliation(s)
- Marc Joliot
- GIN, IMN UMR5293, CEA, CNRS, Université de Bordeaux, Bordeaux, France.
| | - Sandrine Cremona
- GIN, IMN UMR5293, CEA, CNRS, Université de Bordeaux, Bordeaux, France
| | | | - Olivier Etard
- Normandie Université, UNICAEN, INSERM, COMETE U1075, CYCERON, CHU Caen, 14000, Caen, France
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D’Andrea A, Croce P, O’Byrne J, Jerbi K, Pascarella A, Raffone A, Pizzella V, Marzetti L. Mindfulness meditation styles differently modulate source-level MEG microstate dynamics and complexity. Front Neurosci 2024; 18:1295615. [PMID: 38370436 PMCID: PMC10869546 DOI: 10.3389/fnins.2024.1295615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 01/15/2024] [Indexed: 02/20/2024] Open
Abstract
Background The investigation of mindfulness meditation practice, classically divided into focused attention meditation (FAM), and open monitoring meditation (OMM) styles, has seen a long tradition of theoretical, affective, neurophysiological and clinical studies. In particular, the high temporal resolution of magnetoencephalography (MEG) or electroencephalography (EEG) has been exploited to fill the gap between the personal experience of meditation practice and its neural correlates. Mounting evidence, in fact, shows that human brain activity is highly dynamic, transiting between different brain states (microstates). In this study, we aimed at exploring MEG microstates at source-level during FAM, OMM and in the resting state, as well as the complexity and criticality of dynamic transitions between microstates. Methods Ten right-handed Theravada Buddhist monks with a meditative expertise of minimum 2,265 h participated in the experiment. MEG data were acquired during a randomized block design task (6 min FAM, 6 min OMM, with each meditative block preceded and followed by 3 min resting state). Source reconstruction was performed using eLORETA on individual cortical space, and then parcellated according to the Human Connect Project atlas. Microstate analysis was then applied to parcel level signals in order to derive microstate topographies and indices. In addition, from microstate sequences, the Hurst exponent and the Lempel-Ziv complexity (LZC) were computed. Results Our results show that the coverage and occurrence of specific microstates are modulated either by being in a meditative state or by performing a specific meditation style. Hurst exponent values in both meditation conditions are reduced with respect to the value observed during rest, LZC shows significant differences between OMM, FAM, and REST, with a progressive increase from REST to FAM to OMM. Discussion Importantly, we report changes in brain criticality indices during meditation and between meditation styles, in line with a state-like effect of meditation on cognitive performance. In line with previous reports, we suggest that the change in cognitive state experienced in meditation is paralleled by a shift with respect to critical points in brain dynamics.
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Affiliation(s)
- Antea D’Andrea
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Pierpaolo Croce
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Jordan O’Byrne
- Department of Psychology, University of Montreal, Montreal, QC, Canada
| | - Karim Jerbi
- Department of Psychology, University of Montreal, Montreal, QC, Canada
| | - Annalisa Pascarella
- Institute for the Applications of Calculus “M. Picone”, National Research Council, Rome, Lazio, Italy
| | - Antonino Raffone
- Department of Psychology, Sapienza University of Rome, Rome, Lazio, Italy
| | - Vittorio Pizzella
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
- Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Abruzzo, Italy
| | - Laura Marzetti
- Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Abruzzo, Italy
- Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Abruzzo, Italy
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Deng D, Sun H, Wang Y, Guo X, Yuan Y, Wang J, Qiu L. Structural and functional abnormalities in first-episode drug-naïve pediatric idiopathic generalized epilepsy. Cereb Cortex 2024; 34:bhae021. [PMID: 38314605 DOI: 10.1093/cercor/bhae021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/09/2024] [Accepted: 01/10/2024] [Indexed: 02/06/2024] Open
Abstract
The aim of this study was to investigate brain structure and corresponding static and dynamic functional connectivity (sFC & dFC) abnormalities in untreated, first-episode pediatric idiopathic generalized epilepsy (IGE), with the goal of better understanding the underlying pathological mechanisms of IGE. Thirty-one children with IGE and 31 age-matched healthy controls (HC) were recruited. Structural magnetic resonance imaging (sMRI) data were acquired, and voxel-based morphometry (VBM) analysis were performed to reveal abnormal gray matter volume (GMV). Moreover, sFC and dFC analyses were conducted using the brain areas exhibiting abnormal GMV as seed regions to explore abnormal functional couplings. Compared to HC, the IGE group exhibited increased GMV in left middle cingulate cortex (MCC) and right parahippocampus (ParaHipp). In addition, the analyses of dFC and sFC with MCC and ParaHipp as seeds revealed more extensive functional connectivity (FC) changes in dFC. Notably, the structurally and functionally abnormal brain areas were primarily localized in the default mode network (DMN). However, our study did not find any significant associations between these altered neuroimaging measurements and clinical outcomes. This study uncovered microstructural changes as well as corresponding sFC and dFC changes in patients with new-onset, untreated pediatric IGE. The affected brain regions were primarily located within the DMN, highlighting the DMN's crucial role in the development of pediatric IGE.
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Affiliation(s)
- Dingmei Deng
- West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 18, South Section 3, First Ring Road, Wuhou District, Chengdu 610041, China
- Medical Imaging Center, The Second People's Hospital of Yibin, 96# Beida Street, Cuiping District, Yibin 644000, China
- Clinical Research and Translational Center, Second People's Hospital of Yibin City-West China Yibin Hospital, Sichuan University, 96# Beida Street, Cuiping District, Yibin 644000, China
| | - Hui Sun
- College of Electrical Engineering, Sichuan University, No. 24, South Section 1, First Ring Road, Wuhou District, Chengdu 610065, China
| | - Yuting Wang
- Medical Imaging Center, The Second People's Hospital of Yibin, 96# Beida Street, Cuiping District, Yibin 644000, China
- Clinical Research and Translational Center, Second People's Hospital of Yibin City-West China Yibin Hospital, Sichuan University, 96# Beida Street, Cuiping District, Yibin 644000, China
| | - Xin Guo
- Medical Imaging Center, The Second People's Hospital of Yibin, 96# Beida Street, Cuiping District, Yibin 644000, China
- Clinical Research and Translational Center, Second People's Hospital of Yibin City-West China Yibin Hospital, Sichuan University, 96# Beida Street, Cuiping District, Yibin 644000, China
| | - Yizhi Yuan
- Medical Imaging Center, The Second People's Hospital of Yibin, 96# Beida Street, Cuiping District, Yibin 644000, China
- Clinical Research and Translational Center, Second People's Hospital of Yibin City-West China Yibin Hospital, Sichuan University, 96# Beida Street, Cuiping District, Yibin 644000, China
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, No.7, Zhiyuan Road, Chenggong District, Kunming 650500, China
- Yunnan Key Laboratory of Primate Biomedical Research, No.7, Zhiyuan Road, Chenggong District, Kunming 650500, China
| | - Lihua Qiu
- Medical Imaging Center, The Second People's Hospital of Yibin, 96# Beida Street, Cuiping District, Yibin 644000, China
- Clinical Research and Translational Center, Second People's Hospital of Yibin City-West China Yibin Hospital, Sichuan University, 96# Beida Street, Cuiping District, Yibin 644000, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, No. 24, South Section 1, First Ring Road, Wuhou District, Chengdu City, Sichuan Province, Chengdu 610065, China
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8
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Gohil C, Huang R, Roberts E, van Es MWJ, Quinn AJ, Vidaurre D, Woolrich MW. osl-dynamics, a toolbox for modeling fast dynamic brain activity. eLife 2024; 12:RP91949. [PMID: 38285016 PMCID: PMC10945565 DOI: 10.7554/elife.91949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2024] Open
Abstract
Neural activity contains rich spatiotemporal structure that corresponds to cognition. This includes oscillatory bursting and dynamic activity that span across networks of brain regions, all of which can occur on timescales of tens of milliseconds. While these processes can be accessed through brain recordings and imaging, modeling them presents methodological challenges due to their fast and transient nature. Furthermore, the exact timing and duration of interesting cognitive events are often a priori unknown. Here, we present the OHBA Software Library Dynamics Toolbox (osl-dynamics), a Python-based package that can identify and describe recurrent dynamics in functional neuroimaging data on timescales as fast as tens of milliseconds. At its core are machine learning generative models that are able to adapt to the data and learn the timing, as well as the spatial and spectral characteristics, of brain activity with few assumptions. osl-dynamics incorporates state-of-the-art approaches that can be, and have been, used to elucidate brain dynamics in a wide range of data types, including magneto/electroencephalography, functional magnetic resonance imaging, invasive local field potential recordings, and electrocorticography. It also provides novel summary measures of brain dynamics that can be used to inform our understanding of cognition, behavior, and disease. We hope osl-dynamics will further our understanding of brain function, through its ability to enhance the modeling of fast dynamic processes.
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Affiliation(s)
- Chetan Gohil
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
| | - Rukuang Huang
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
| | - Evan Roberts
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
| | - Mats WJ van Es
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
| | - Andrew J Quinn
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
- Centre for Human Brain Health, School of Psychology, University of BirminghamBirminghamUnited Kingdom
| | - Diego Vidaurre
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
- Center for Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus UniversityAarhusDenmark
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of OxfordOxfordUnited Kingdom
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Brice Azangue A, Megam Ngouonkadi EB, Kabong Nono M, Fotsin HB, Sone Ekonde M, Yemele D. Stability and synchronization in neural network with delayed synaptic connections. CHAOS (WOODBURY, N.Y.) 2024; 34:013117. [PMID: 38215223 DOI: 10.1063/5.0175408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 12/04/2023] [Indexed: 01/14/2024]
Abstract
In this paper, we investigate the stability of the synchronous state in a complex network using the master stability function technique. We use the extended Hindmarsh-Rose neuronal model including time delayed electrical, chemical, and hybrid couplings. We find the corresponding master stability equation that describes the whole dynamics for each coupling mode. From the maximum Lyapunov exponent, we deduce the stability state for each coupling mode. We observe that for electrical coupling, there exists a mixing between stable and unstable states. For a good setting of some system parameters, the position and the size of unstable areas can be modified. For chemical coupling, we observe difficulties in having a stable area in the complex plane. For hybrid coupling, we observe a stable behavior in the whole system compared to the case where these couplings are considered separately. The obtained results for each coupling mode help to analyze the stability state of some network topologies by using the corresponding eigenvalues. We observe that using electrical coupling can involve a full or partial stability of the system. In the case of chemical coupling, unstable states are observed whereas in the case of hybrid interactions a full stability of the network is obtained. Temporal analysis of the global synchronization is also done for each coupling mode, and the results show that when the network is stable, the synchronization is globally observed, while in the case when it is unstable, its nodes are not globally synchronized.
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Affiliation(s)
- A Brice Azangue
- Research Unit of Condensed Matter, Electronics and Signal Processing, Department of Physics, Faculty of Science, University of Dschang, P.O. Box 067 Dschang, Cameroon
| | - E B Megam Ngouonkadi
- Research Unit of Condensed Matter, Electronics and Signal Processing, Department of Physics, Faculty of Science, University of Dschang, P.O. Box 067 Dschang, Cameroon
- Department of Electrical and Electronic Engineering, College of Technology (COT), University of Buea, P.O. Box 63 Buea, Cameroon
| | - M Kabong Nono
- Research Unit of Condensed Matter, Electronics and Signal Processing, Department of Physics, Faculty of Science, University of Dschang, P.O. Box 067 Dschang, Cameroon
| | - H B Fotsin
- Research Unit of Condensed Matter, Electronics and Signal Processing, Department of Physics, Faculty of Science, University of Dschang, P.O. Box 067 Dschang, Cameroon
| | - M Sone Ekonde
- Department of Electrical and Electronic Engineering, College of Technology (COT), University of Buea, P.O. Box 63 Buea, Cameroon
| | - D Yemele
- Research Unit of Mechanics and Modeling of Physical Systems, Department of Physics, Faculty of Sciences, University of Dschang, P.O. Box 067 Dschang, Cameroon
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10
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Wang F, Liu Z, Ford SD, Deng M, Zhang W, Yang J, Palaniyappan L. Aberrant Brain Dynamics in Schizophrenia During Working Memory Task: Evidence From a Replication Functional MRI Study. Schizophr Bull 2024; 50:96-106. [PMID: 37018464 PMCID: PMC10754176 DOI: 10.1093/schbul/sbad032] [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] [Indexed: 04/07/2023]
Abstract
BACKGROUND AND HYPOTHESIS The integration of information that typifies working memory (WM) operation requires a flexible, dynamic functional relationship among brain regions. In schizophrenia, though WM capacity is prominently impaired at higher loads, the mechanistic underpinnings are unclear. As a result, we lack convincing cognitive remediation of load-dependent deficits. We hypothesize that reduced WM capacity arises from a disruption in dynamic functional connectivity when patients face cognitive demands. STUDY DESIGN We calculate the dynamic voxel-wise degree centrality (dDC) across the functional connectome in 142 patients with schizophrenia and 88 healthy controls (HCs) facing different WM loads during an n-back task. We tested associations of the altered variability in dDC and clinical symptoms and identified intermediate connectivity configurations (clustered states) across time during WM operation. These analyses were repeated in another independent dataset of 169 subjects (102 with schizophrenia). STUDY RESULTS Compared with HCs, patients showed an increased dDC variability of supplementary motor area (SMA) for the "2back vs. 0back" contrast. This instability at the SMA seen in patients correlated with increased positive symptoms and followed a limited "U-shape" pattern at rest-condition and 2 loads. In the clustering analysis, patients showed reduced centrality in the SMA, superior temporal gyrus, and putamen. These results were replicated in a constrained search in the second independent dataset. CONCLUSIONS Schizophrenia is characterized by a load-dependent reduction of stable centrality in SMA; this relates to the severity of positive symptoms, especially disorganized behaviour. Restoring SMA stability in the presence of cognitive demands may have a therapeutic effect in schizophrenia.
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Affiliation(s)
- Feiwen Wang
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China
| | - Zhening Liu
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China
| | - Sabrina D Ford
- Robarts Research Institute, Western University, London, ON, Canada
| | - Mengjie Deng
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China
| | - Wen Zhang
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China
| | - Jie Yang
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China
| | - Lena Palaniyappan
- Robarts Research Institute, Western University, London, ON, Canada
- Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
- Department of Medical Biophysics, Western University, London, ON, Canada
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11
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Paltoglou G, Stefanaki C, Chrousos GP. Functional MRI Techniques Suggesting that the Stress System Interacts with Three Large Scale Core Brain Networks to Help Coordinate the Adaptive Response: A Systematic Review. Curr Neuropharmacol 2024; 22:976-989. [PMID: 37533249 PMCID: PMC10845086 DOI: 10.2174/1570159x21666230801151718] [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: 01/09/2023] [Revised: 03/16/2023] [Accepted: 04/06/2023] [Indexed: 08/04/2023] Open
Abstract
OBJECTIVE Synthesis of functional MRI (fMRI) and functional connectivity (FC) analysis data on human stress system (SS) function, as it relates to the dynamic function of the Salience (SN), Default Mode (DMN) and Central Executive (CEN) networks. METHODS Systematic search of Medline, Scopus, Clinical Trials.gov, and Google Scholar databases of studies published prior to September 2022 resulted in 28 full-text articles included for qualitative synthesis. RESULTS Acute stress changes the states of intra-/inter- neural network FCs and activities from those of resting, low arousal state in the SN, DMN and CEN, during which intra- and inter-network FCs and activities of all three networks are low. SS activation is positively linked to the activity of the SN and negatively to that of the DMN, while, in parallel, it is associated with an initial decrease and a subsequent increase of the intra- network FC and activity of the CEN. The FC between the DMN and the CEN increases, while those between the SN and the CEN decrease, allowing time for frontal lobe strategy input and "proper" CEN activity and task decision. SN activation is linked to sensory hypersensitivity, "impaired" memory, and a switch from serial to parallel processing, while trait mindfulness is associated with FC changes promoting CEN activity and producing a "task-ready state". CONCLUSION SS activation is tightly connected to that of the SN, with stress hormones likely potentiating the intra-network FC of the latter, attenuating that of the DMN, and causing a biphasic suppression- to-activation response of the CEN, all adaptive changes favoring proper decisions and survival.
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Affiliation(s)
- George Paltoglou
- University Research Institute of Maternal and Child Health and Precision Medicine, Medical School, National and Kapodistrian University of Athens, “Aghia Sophia” Children's Hospital, Athens 11527, Greece
- Second Department of Pediatrics, School of Medicine, National and Kapodistrian University of Athens, “A. & P. Kyriakou” Children's Hospital, Athens 11527, Greece
- UNESCO Chair on Adolescent Health Care, Medical School, National and Kapodistrian University of Athens, Athens 11527, Greece
| | - Charikleia Stefanaki
- University Research Institute of Maternal and Child Health and Precision Medicine, Medical School, National and Kapodistrian University of Athens, “Aghia Sophia” Children's Hospital, Athens 11527, Greece
- UNESCO Chair on Adolescent Health Care, Medical School, National and Kapodistrian University of Athens, Athens 11527, Greece
| | - George P. Chrousos
- University Research Institute of Maternal and Child Health and Precision Medicine, Medical School, National and Kapodistrian University of Athens, “Aghia Sophia” Children's Hospital, Athens 11527, Greece
- UNESCO Chair on Adolescent Health Care, Medical School, National and Kapodistrian University of Athens, Athens 11527, Greece
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12
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Long K, Zhang X, Wang N, Lei H. Heart Rate Variability during Online Video Game Playing in Habitual Gamers: Effects of Internet Addiction Scale, Ranking Score and Gaming Performance. Brain Sci 2023; 14:29. [PMID: 38248244 PMCID: PMC10813724 DOI: 10.3390/brainsci14010029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 12/14/2023] [Accepted: 12/23/2023] [Indexed: 01/23/2024] Open
Abstract
Previous studies have demonstrated that individuals with internet gaming disorder (IGD) display abnormal autonomic activities at rest and during gameplay. Here, we examined whether and how in-game autonomic activity is modulated by human characteristics and behavioral performance of the player. We measured heart rate variability (HRV) in 42 male university student habitual gamers (HGs) when they played a round of League of Legends game online. Short-term HRV indices measured in early, middle and late phases of the game were compared between the players at high risk of developing IGD and those at low risk, as assessed by the revised Chen Internet addiction scale (CIAS-R). Multiple linear regression (MLR) was used to identify significant predictors of HRV measured over the whole gameplay period (WG), among CIAS-R, ranking score, hours of weekly playing and selected in-game performance parameters. The high-risk players showed a significantly higher low-frequency power/high-frequency power ratio (LF/HF) relative to the low-risk players, regardless of game phase. MLR analysis revealed that LF/HF measured in WG was predicted by, and only by, CIAS-R. The HRV indicators of sympathetic activity were found to be predicted only by the number of slain in WG (NSlain), and the indicators of parasympathetic activity were predicted by both CIAS-R and NSlain. Collectively, the results demonstrated that risk of developing IGD is associated with dysregulated autonomic balance during gameplay, and in-game autonomic activities are modulated by complex interactions among personal attributes and in-game behavioral performance of the player, as well as situational factors embedded in game mechanics.
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Affiliation(s)
- Kehong Long
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China; (K.L.); (X.Z.); (N.W.)
- University of Chinese Academy of Sciences, Beijing 100190, China
| | - Xuzhe Zhang
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China; (K.L.); (X.Z.); (N.W.)
- University of Chinese Academy of Sciences, Beijing 100190, China
| | - Ningxin Wang
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China; (K.L.); (X.Z.); (N.W.)
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hao Lei
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China; (K.L.); (X.Z.); (N.W.)
- University of Chinese Academy of Sciences, Beijing 100190, China
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
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13
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Nelson MC, Royer J, Lu WD, Leppert IR, Campbell JSW, Schiavi S, Jin H, Tavakol S, Vos de Wael R, Rodriguez-Cruces R, Pike GB, Bernhardt BC, Daducci A, Misic B, Tardif CL. The human brain connectome weighted by the myelin content and total intra-axonal cross-sectional area of white matter tracts. Netw Neurosci 2023; 7:1363-1388. [PMID: 38144691 PMCID: PMC10697181 DOI: 10.1162/netn_a_00330] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 07/19/2023] [Indexed: 12/26/2023] Open
Abstract
A central goal in neuroscience is the development of a comprehensive mapping between structural and functional brain features, which facilitates mechanistic interpretation of brain function. However, the interpretability of structure-function brain models remains limited by a lack of biological detail. Here, we characterize human structural brain networks weighted by multiple white matter microstructural features including total intra-axonal cross-sectional area and myelin content. We report edge-weight-dependent spatial distributions, variance, small-worldness, rich club, hubs, as well as relationships with function, edge length, and myelin. Contrasting networks weighted by the total intra-axonal cross-sectional area and myelin content of white matter tracts, we find opposite relationships with functional connectivity, an edge-length-independent inverse relationship with each other, and the lack of a canonical rich club in myelin-weighted networks. When controlling for edge length, networks weighted by either fractional anisotropy, radial diffusivity, or neurite density show no relationship with whole-brain functional connectivity. We conclude that the co-utilization of structural networks weighted by total intra-axonal cross-sectional area and myelin content could improve our understanding of the mechanisms mediating the structure-function brain relationship.
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Affiliation(s)
- Mark C. Nelson
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Jessica Royer
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Wen Da Lu
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Ilana R. Leppert
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Jennifer S. W. Campbell
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Hyerang Jin
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Shahin Tavakol
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Reinder Vos de Wael
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Raul Rodriguez-Cruces
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - G. Bruce Pike
- Hotchkiss Brain Institute and Departments of Radiology and Clinical Neuroscience, University of Calgary, Calgary, Canada
| | - Boris C. Bernhardt
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | | | - Bratislav Misic
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Christine L. Tardif
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
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14
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Alonso S, Vidaurre D. Toward stability of dynamic FC estimates in neuroimaging and electrophysiology: Solutions and limits. Netw Neurosci 2023; 7:1389-1403. [PMID: 38144684 PMCID: PMC10713011 DOI: 10.1162/netn_a_00331] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 07/27/2023] [Indexed: 12/26/2023] Open
Abstract
Time-varying functional connectivity (FC) methods are used to map the spatiotemporal organization of brain activity. However, their estimation can be unstable, in the sense that different runs of the inference may yield different solutions. But to draw meaningful relations to behavior, estimates must be robust and reproducible. Here, we propose two solutions using the hidden Markov model (HMM) as a descriptive model of time-varying FC. The first, best ranked HMM, involves running the inference multiple times and selecting the best model based on a quantitative measure combining fitness and model complexity. The second, hierarchical-clustered HMM, generates stable cluster state time series by applying hierarchical clustering to the state time series obtained from multiple runs. Experimental results on fMRI and magnetoencephalography data demonstrate that these approaches substantially improve the stability of time-varying FC estimations. Overall, hierarchical-clustered HMM is preferred when the inference variability is high, while the best ranked HMM performs better otherwise.
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Affiliation(s)
- Sonsoles Alonso
- Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark
| | - Diego Vidaurre
- Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark
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15
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Matkovič A, Anticevic A, Murray JD, Repovš G. Static and dynamic fMRI-derived functional connectomes represent largely similar information. Netw Neurosci 2023; 7:1266-1301. [PMID: 38144686 PMCID: PMC10631791 DOI: 10.1162/netn_a_00325] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 06/06/2023] [Indexed: 12/26/2023] Open
Abstract
Functional connectivity (FC) of blood oxygen level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research suggests that dynamic FC explains variability in FC fluctuations and behavior beyond static FC. Our aim was to systematically compare methods on both dimensions. We compared five FC methods: Pearson's/full correlation (static, bivariate), lagged correlation (dynamic, bivariate), partial correlation (static, multivariate), and multivariate AR model with and without self-connections (dynamic, multivariate). We compared these methods by (i) assessing similarities between FC matrices, (ii) by comparing node centrality measures, and (iii) by comparing the patterns of brain-behavior associations. Although FC estimates did not differ as a function of sensitivity to temporal order, we observed differences between the multivariate and bivariate FC methods. The dynamic FC estimates were highly correlated with the static FC estimates, especially when comparing group-level FC matrices. Similarly, there were high correlations between the patterns of brain-behavior associations obtained using the dynamic and static FC methods. We conclude that the dynamic FC estimates represent information largely similar to that of the static FC.
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Affiliation(s)
- Andraž Matkovič
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - John D. Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Grega Repovš
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
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16
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Zhang H, Chen K, Bao J, Wu H. Oxytocin enhances the triangular association among behavior, resting-state, and task-state functional connectivity. Hum Brain Mapp 2023; 44:6074-6089. [PMID: 37771300 PMCID: PMC10619367 DOI: 10.1002/hbm.26498] [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: 02/15/2023] [Revised: 09/12/2023] [Accepted: 09/15/2023] [Indexed: 09/30/2023] Open
Abstract
Considerable advances in the role of oxytocin (OT) effect on behavior and the brain network have been made, but the effect of OT on the association between inter-individual differences in functional connectivity (FC) and behavior is elusive. Here, by using a face-perception task and multiple connectome-based predictive models, we aimed to (1) determine whether OT could enhance the association among behavioral performance, resting-state FC (rsFC), and task-state FC (tsFC) and (2) if so, explore the role of OT in enhancing this triangular association. We found that in the OT group, the prediction performance of using rsFC or tsFC to predict task behavior was higher than that of the PL group. Additionally, the correlation coefficient between rsFC and tsFC was substantially higher in the OT group than in the PL group. The strength of these associations could be partly explained by OT altering the brain's FCs related to social cognition and face perception in both the resting and task states, mainly in brain regions such as the limbic system, prefrontal cortex, temporal poles, and temporoparietal junction. Taken together, these results provide novel evidence and a corresponding mechanism for how neuropeptides cause increased associations among inter-individual differences across different levels (e.g., behavior and large-scale brain networks in both resting and task-state), and may inspire future research on the role of neuropeptides in the cross levels association of both clinical and nonclinical use.
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Affiliation(s)
- Haoming Zhang
- Centre for Cognitive and Brain Sciences and Department of PsychologyUniversity of MacauMacauChina
| | - Kun Chen
- Centre for Cognitive and Brain Sciences and Department of PsychologyUniversity of MacauMacauChina
| | - Jin Bao
- Shenzhen Neher Neural Plasticity Laboratory, Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences (CAS)ShenzhenChina
- Shenzhen‐Hong Kong Institute of Brain Science‐Shenzhen Fundamental Research InstitutionsShenzhenChina
| | - Haiyan Wu
- Centre for Cognitive and Brain Sciences and Department of PsychologyUniversity of MacauMacauChina
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17
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Li Z, Li J, Wang N, Lv Y, Zou Q, Wang J. Single-subject cortical morphological brain networks: Phenotypic associations and neurobiological substrates. Neuroimage 2023; 283:120434. [PMID: 37907157 DOI: 10.1016/j.neuroimage.2023.120434] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/28/2023] [Accepted: 10/28/2023] [Indexed: 11/02/2023] Open
Abstract
Although single-subject morphological brain networks provide an important way for human connectome studies, their roles and origins are poorly understood. Combining cross-sectional and repeated structural magnetic resonance imaging scans from adults, children and twins with behavioral and cognitive measures and brain-wide transcriptomic, cytoarchitectonic and chemoarchitectonic data, this study examined phenotypic associations and neurobiological substrates of single-subject morphological brain networks. We found that single-subject morphological brain networks explained inter-individual variance and predicted individual outcomes in Motor and Cognition domains, and distinguished individuals from each other. The performance can be further improved by integrating different morphological indices for network construction. Low-moderate heritability was observed for single-subject morphological brain networks with the highest heritability for sulcal depth-derived networks and higher heritability for inter-module connections. Furthermore, differential roles of genetic, cytoarchitectonic and chemoarchitectonic factors were observed for single-subject morphological brain networks. Cortical thickness-derived networks were related to the three factors with contributions from genes enriched in membrane and transport related functions, genes preferentially located in supragranular and granular layers, overall thickness in the molecular layer and thickness of wall in the infragranular layers, and metabotropic glutamate receptor 5 and dopamine transporter; fractal dimension-, gyrification index- and sulcal depth-derived networks were only associated with the chemoarchitectonic factor with contributions from different sets of neurotransmitter receptors. Most results were reproducible across different parcellation schemes and datasets. Altogether, this study demonstrates phenotypic associations and neurobiological substrates of single-subject morphological brain networks, which provide intermediate endophenotypes to link molecular and cellular architecture and behavior and cognition.
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Affiliation(s)
- Zhen Li
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Junle Li
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Ningkai Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
| | - Yating Lv
- Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Qihong Zou
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China; Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; Center for Studies of Psychological Application, South China Normal University, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China.
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18
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Papadaki E, Koustakas T, Werner A, Lindenberger U, Kühn S, Wenger E. Resting-state functional connectivity in an auditory network differs between aspiring professional and amateur musicians and correlates with performance. Brain Struct Funct 2023; 228:2147-2163. [PMID: 37792073 PMCID: PMC10587189 DOI: 10.1007/s00429-023-02711-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 09/10/2023] [Indexed: 10/05/2023]
Abstract
Auditory experience-dependent plasticity is often studied in the domain of musical expertise. Available evidence suggests that years of musical practice are associated with structural and functional changes in auditory cortex and related brain regions. Resting-state functional magnetic resonance imaging (MRI) can be used to investigate neural correlates of musical training and expertise beyond specific task influences. Here, we compared two groups of musicians with varying expertise: 24 aspiring professional musicians preparing for their entrance exam at Universities of Arts versus 17 amateur musicians without any such aspirations but who also performed music on a regular basis. We used an interval recognition task to define task-relevant brain regions and computed functional connectivity and graph-theoretical measures in this network on separately acquired resting-state data. Aspiring professionals performed significantly better on all behavioral indicators including interval recognition and also showed significantly greater network strength and global efficiency than amateur musicians. Critically, both average network strength and global efficiency were correlated with interval recognition task performance assessed in the scanner, and with an additional measure of interval identification ability. These findings demonstrate that task-informed resting-state fMRI can capture connectivity differences that correspond to expertise-related differences in behavior.
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Affiliation(s)
- Eleftheria Papadaki
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany.
- International Max Planck Research School on the Life Course (LIFE), Berlin, Germany.
| | - Theodoros Koustakas
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany
| | - André Werner
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany, London, UK
| | - Simone Kühn
- Lise Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany
- Neuronal Plasticity Working Group, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Elisabeth Wenger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany
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Morgenroth E, Vilaclara L, Muszynski M, Gaviria J, Vuilleumier P, Van De Ville D. Probing neurodynamics of experienced emotions-a Hitchhiker's guide to film fMRI. Soc Cogn Affect Neurosci 2023; 18:nsad063. [PMID: 37930850 PMCID: PMC10656947 DOI: 10.1093/scan/nsad063] [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: 03/16/2023] [Revised: 08/04/2023] [Accepted: 11/01/2023] [Indexed: 11/08/2023] Open
Abstract
Film functional magnetic resonance imaging (fMRI) has gained tremendous popularity in many areas of neuroscience. However, affective neuroscience remains somewhat behind in embracing this approach, even though films lend themselves to study how brain function gives rise to complex, dynamic and multivariate emotions. Here, we discuss the unique capabilities of film fMRI for emotion research, while providing a general guide of conducting such research. We first give a brief overview of emotion theories as these inform important design choices. Next, we discuss films as experimental paradigms for emotion elicitation and address the process of annotating them. We then situate film fMRI in the context of other fMRI approaches, and present an overview of results from extant studies so far with regard to advantages of film fMRI. We also give an overview of state-of-the-art analysis techniques including methods that probe neurodynamics. Finally, we convey limitations of using film fMRI to study emotion. In sum, this review offers a practitioners' guide to the emerging field of film fMRI and underscores how it can advance affective neuroscience.
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Affiliation(s)
- Elenor Morgenroth
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva 1202, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva 1202, Switzerland
- Swiss Center for Affective Sciences, University of Geneva, Geneva 1202, Switzerland
| | - Laura Vilaclara
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva 1202, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva 1202, Switzerland
| | - Michal Muszynski
- Department of Basic Neurosciences, University of Geneva, Geneva 1202, Switzerland
| | - Julian Gaviria
- Swiss Center for Affective Sciences, University of Geneva, Geneva 1202, Switzerland
- Department of Basic Neurosciences, University of Geneva, Geneva 1202, Switzerland
- Department of Psychiatry, University of Geneva, Geneva 1202, Switzerland
| | - Patrik Vuilleumier
- Swiss Center for Affective Sciences, University of Geneva, Geneva 1202, Switzerland
- Department of Basic Neurosciences, University of Geneva, Geneva 1202, Switzerland
- CIBM Center for Biomedical Imaging, Geneva 1202, Switzerland
| | - Dimitri Van De Ville
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva 1202, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva 1202, Switzerland
- CIBM Center for Biomedical Imaging, Geneva 1202, Switzerland
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20
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Winter A, Gruber M, Thiel K, Flinkenflügel K, Meinert S, Goltermann J, Winter NR, Borgers T, Stein F, Jansen A, Brosch K, Wroblewski A, Thomas-Odenthal F, Usemann P, Straube B, Alexander N, Jamalabadi H, Nenadić I, Bonnekoh LM, Dohm K, Leehr EJ, Opel N, Grotegerd D, Hahn T, van den Heuvel MP, Kircher T, Repple J, Dannlowski U. Shared and distinct structural brain networks related to childhood maltreatment and social support: connectome-based predictive modeling. Mol Psychiatry 2023; 28:4613-4621. [PMID: 37714950 PMCID: PMC10914611 DOI: 10.1038/s41380-023-02252-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 08/30/2023] [Accepted: 09/05/2023] [Indexed: 09/17/2023]
Abstract
Childhood maltreatment (CM) has been associated with changes in structural brain connectivity even in the absence of mental illness. Social support, an important protective factor in the presence of childhood maltreatment, has been positively linked to white matter integrity. However, the shared effects of current social support and CM and their association with structural connectivity remain to be investigated. They might shed new light on the neurobiological basis of the protective mechanism of social support. Using connectome-based predictive modeling (CPM), we analyzed structural connectomes of N = 904 healthy adults derived from diffusion-weighted imaging. CPM predicts phenotypes from structural connectivity through a cross-validation scheme. Distinct and shared networks of white matter tracts predicting childhood trauma questionnaire scores and the social support questionnaire were identified. Additional analyses were applied to assess the stability of the results. CM and social support were predicted significantly from structural connectome data (all rs ≥ 0.119, all ps ≤ 0.016). Edges predicting CM and social support were inversely correlated, i.e., positively correlated with CM and negatively with social support, and vice versa, with a focus on frontal and temporal regions including the insula and superior temporal lobe. CPM reveals the predictive value of the structural connectome for CM and current social support. Both constructs are inversely associated with connectivity strength in several brain tracts. While this underlines the interconnectedness of these experiences, it suggests social support acts as a protective factor following adverse childhood experiences, compensating for brain network alterations. Future longitudinal studies should focus on putative moderating mechanisms buffering these adverse experiences.
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Affiliation(s)
- Alexandra Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Katharina Thiel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Kira Flinkenflügel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Nils R Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tiana Borgers
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Giessen, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Giessen, Germany
- Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Giessen, Germany
| | - Adrian Wroblewski
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Giessen, Germany
| | - Florian Thomas-Odenthal
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Giessen, Germany
| | - Paula Usemann
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Giessen, Germany
| | - Benjamin Straube
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Giessen, Germany
| | - Nina Alexander
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Giessen, Germany
| | - Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Giessen, Germany
| | - Linda M Bonnekoh
- Department of Child and Adolescent Psychiatry, University Hospital Münster, Münster, Germany
| | - Katharina Dohm
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Elisabeth J Leehr
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Nils Opel
- Department of Psychiatry and Psychotherapy, University of Jena, Jena, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Martijn P van den Heuvel
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Child Psychiatry, Amsterdam University Medical Center, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Giessen, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany.
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21
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Groot JM, Miletic S, Isherwood SJS, Tse DHY, Habli S, Håberg AK, Forstmann BU, Bazin PL, Mittner M. Echoes from Intrinsic Connectivity Networks in the Subcortex. J Neurosci 2023; 43:6609-6618. [PMID: 37562962 PMCID: PMC10538587 DOI: 10.1523/jneurosci.1020-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/11/2023] [Accepted: 07/28/2023] [Indexed: 08/12/2023] Open
Abstract
Decades of research have greatly improved our understanding of intrinsic human brain organization in terms of functional networks and the transmodal hubs within the cortex at which they converge. However, substrates of multinetwork integration in the human subcortex are relatively uncharted. Here, we leveraged recent advances in subcortical atlasing and ultra-high field (7 T) imaging optimized for the subcortex to investigate the functional architecture of 14 individual structures in healthy adult males and females with a fully data-driven approach. We revealed that spontaneous neural activity in subcortical regions can be decomposed into multiple independent subsignals that correlate with, or "echo," the activity in functional networks across the cortex. Distinct subregions of the thalamus, striatum, claustrum, and hippocampus showed a varied pattern of echoes from attention, control, visual, somatomotor, and default mode networks, demonstrating evidence for a heterogeneous organization supportive of functional integration. Multiple network activity furthermore converged within the globus pallidus externa, substantia nigra, and ventral tegmental area but was specific to one subregion, while the amygdala and pedunculopontine nucleus preferentially affiliated with a single network, showing a more homogeneous topography. Subregional connectivity of the globus pallidus interna, subthalamic nucleus, red nucleus, periaqueductal gray, and locus coeruleus did not resemble patterns of cortical network activity. Together, these finding describe potential mechanisms through which the subcortex participates in integrated and segregated information processing and shapes the spontaneous cognitive dynamics during rest.SIGNIFICANCE STATEMENT Despite the impact of subcortical dysfunction on brain health and cognition, large-scale functional mapping of subcortical structures severely lags behind that of the cortex. Recent developments in subcortical atlasing and imaging at ultra-high field provide new avenues for studying the intricate functional architecture of the human subcortex. With a fully data-driven analysis, we reveal subregional connectivity profiles of a large set of noncortical structures, including those rarely studied in fMRI research. The results have implications for understanding how the functional organization of the subcortex facilitates integrative processing through cross-network information convergence, paving the way for future work aimed at improving our knowledge of subcortical contributions to intrinsic brain dynamics and spontaneous cognition.
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Affiliation(s)
- Josephine M Groot
- Department of Psychology, UiT-Arctic University of Norway, Tromsø, 9037, Norway
- Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, 1001 NK, The Netherlands
| | - Steven Miletic
- Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, 1001 NK, The Netherlands
| | - Scott J S Isherwood
- Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, 1001 NK, The Netherlands
| | - Desmond H Y Tse
- Department of Neuropsychology and Psychopharmacology, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Sarah Habli
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, 8900, Norway
| | - Asta K Håberg
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, 8900, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim, 7006, Norway
| | - Birte U Forstmann
- Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, 1001 NK, The Netherlands
| | - Pierre-Louis Bazin
- Department of Psychology, UiT-Arctic University of Norway, Tromsø, 9037, Norway
- Departments of Neurophysics and Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, 04303, Germany
| | - Matthias Mittner
- Department of Psychology, UiT-Arctic University of Norway, Tromsø, 9037, Norway
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22
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Li Y, Zhao M, Cao Y, Gao Y, Wang Y, Yun B, Luo L, Liu W, Zheng C. Static and dynamic resting-state brain activity patterns of table tennis players in 7-Tesla MRI. Front Neurosci 2023; 17:1202932. [PMID: 37521699 PMCID: PMC10375049 DOI: 10.3389/fnins.2023.1202932] [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: 04/09/2023] [Accepted: 06/27/2023] [Indexed: 08/01/2023] Open
Abstract
Table tennis involves quick and accurate motor responses during training and competition. Multiple studies have reported considerably faster visuomotor responses and expertise-related intrinsic brain activity changes among table tennis players compared with matched controls. However, the underlying neural mechanisms remain unclear. Herein, we performed static and dynamic resting-state functional magnetic resonance imaging (rs-fMRI) analyses of 20 table tennis players and 21 control subjects using 7T ultra-high field imaging. We calculated the static and dynamic amplitude of low-frequency fluctuations (ALFF) of the two groups. The results revealed that table tennis players exhibited decreased static ALFF in the left inferior temporal gyrus (lITG) compared with the control group. Voxel-wised static functional connectivity (sFC) and dynamic functional connectivity (dFC) analyses using lITG as the seed region afforded complementary and overlapping results. The table tennis players exhibited decreased sFC in the right middle temporal gyrus and left inferior parietal gyrus. Conversely, they displayed increased dFC from the lITG to prefrontal cortex, particularly the left middle frontal gyrus, left superior frontal gyrus-medial, and left superior frontal gyrus-dorsolateral. These findings suggest that table tennis players demonstrate altered visuomotor transformation and executive function pathways. Both pathways involve the lITG, which is a vital node in the ventral visual stream. These static and dynamic analyses provide complementary and overlapping results, which may help us better understand the neural mechanisms underlying the changes in intrinsic brain activity and network organization induced by long-term table tennis skill training.
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Affiliation(s)
- Yuyang Li
- Key Laboratory of Medical Neurobiology of Zhejiang Province, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China
| | - Mengqi Zhao
- School of Psychology, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Yuting Cao
- Key Laboratory of Medical Neurobiology of Zhejiang Province, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Yanyan Gao
- School of Psychology, Zhejiang Normal University, Jinhua, China
- Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China
| | - Yadan Wang
- College of Information and Electronic Technology, Jiamusi University, Jiamusi, China
| | - Bing Yun
- Department of Public Physical and Art Education, Zhejiang University, Hangzhou, China
| | - Le Luo
- Hangzhou Wuyunshan Hospital, Hangzhou, China
| | - Wenming Liu
- Department of Sport Science, College of Education, Zhejiang University, Hangzhou, China
| | - Chanying Zheng
- Key Laboratory of Medical Neurobiology of Zhejiang Province, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
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23
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Bolton TAW, Van De Ville D, Amico E, Preti MG, Liégeois R. The arrow-of-time in neuroimaging time series identifies causal triggers of brain function. Hum Brain Mapp 2023; 44:4077-4087. [PMID: 37209360 PMCID: PMC10258533 DOI: 10.1002/hbm.26331] [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: 11/15/2022] [Revised: 03/07/2023] [Accepted: 04/18/2023] [Indexed: 05/22/2023] Open
Abstract
Moving from association to causal analysis of neuroimaging data is crucial to advance our understanding of brain function. The arrow-of-time (AoT), that is, the known asymmetric nature of the passage of time, is the bedrock of causal structures shaping physical phenomena. However, almost all current time series metrics do not exploit this asymmetry, probably due to the difficulty to account for it in modeling frameworks. Here, we introduce an AoT-sensitive metric that captures the intensity of causal effects in multivariate time series, and apply it to high-resolution functional neuroimaging data. We find that causal effects underlying brain function are more distinctively localized in space and time than functional activity or connectivity, thereby allowing us to trace neural pathways recruited in different conditions. Overall, we provide a mapping of the causal brain that challenges the association paradigm of brain function.
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Affiliation(s)
- Thomas A. W. Bolton
- Connectomics Laboratory, Department of RadiologyCentre Hospitalier Universitaire VaudoisLausanneSwitzerland
- Department of Clinical NeurosciencesCentre Hospitalier Universitaire VaudoisLausanneSwitzerland
| | - Dimitri Van De Ville
- Neuro‐X InstituteÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
| | - Enrico Amico
- Neuro‐X InstituteÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
| | - Maria G. Preti
- Neuro‐X InstituteÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
- CIBM Center for Biomedical ImagingVaudSwitzerland
| | - Raphaël Liégeois
- Neuro‐X InstituteÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
- Department of Radiology and Medical InformaticsUniversity of GenevaGenevaSwitzerland
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24
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Zhang X, Lu B, Chen C, Yang L, Chen W, Yao D, Hou J, Qiu J, Li F, Xu P. The correlation between upper body grip strength and resting-state EEG network. Med Biol Eng Comput 2023:10.1007/s11517-023-02865-4. [PMID: 37338738 DOI: 10.1007/s11517-023-02865-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 06/07/2023] [Indexed: 06/21/2023]
Abstract
Current research in the field of neuroscience primarily focuses on the analysis of electroencephalogram (EEG) activities associated with movement within the central nervous system. However, there is a dearth of studies investigating the impact of prolonged individual strength training on the resting state of the brain. Therefore, it is crucial to examine the correlation between upper body grip strength and resting-state EEG networks. In this study, coherence analysis was utilized to construct resting-state EEG networks using the available datasets. A multiple linear regression model was established to examine the correlation between the brain network properties of individuals and their maximum voluntary contraction (MVC) during gripping tasks. The model was used to predict individual MVC. The beta and gamma frequency bands showed significant correlation between RSN connectivity and MVC (p < 0.05), particularly in left hemisphere frontoparietal and fronto-occipital connectivity. RSN properties were consistently correlated with MVC in both bands, with correlation coefficients greater than 0.60 (p < 0.01). Additionally, predicted MVC positively correlated with actual MVC, with a coefficient of 0.70 and root mean square error of 5.67 (p < 0.01). The results show that the resting-state EEG network is closely related to upper body grip strength, which can indirectly reflect an individual's muscle strength through the resting brain network.
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Affiliation(s)
- Xiabing Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Bin Lu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Chunli Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Lei Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Wanjun Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, 611731, China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China
| | - Jingming Hou
- Department of Rehabilitation, Southwest Hospital, Army Medical University, Chongqing, 400038, China
| | - Jing Qiu
- Robotics Research Center, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China.
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, 611731, China.
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China.
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, 611731, China.
- Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, 610041, China.
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25
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Bedel HA, Sivgin I, Dalmaz O, Dar SUH, Çukur T. BolT: Fused window transformers for fMRI time series analysis. Med Image Anal 2023; 88:102841. [PMID: 37224718 DOI: 10.1016/j.media.2023.102841] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 02/07/2023] [Accepted: 05/10/2023] [Indexed: 05/26/2023]
Abstract
Deep-learning models have enabled performance leaps in analysis of high-dimensional functional MRI (fMRI) data. Yet, many previous methods are suboptimally sensitive for contextual representations across diverse time scales. Here, we present BolT, a blood-oxygen-level-dependent transformer model, for analyzing multi-variate fMRI time series. BolT leverages a cascade of transformer encoders equipped with a novel fused window attention mechanism. Encoding is performed on temporally-overlapped windows within the time series to capture local representations. To integrate information temporally, cross-window attention is computed between base tokens in each window and fringe tokens from neighboring windows. To gradually transition from local to global representations, the extent of window overlap and thereby number of fringe tokens are progressively increased across the cascade. Finally, a novel cross-window regularization is employed to align high-level classification features across the time series. Comprehensive experiments on large-scale public datasets demonstrate the superior performance of BolT against state-of-the-art methods. Furthermore, explanatory analyses to identify landmark time points and regions that contribute most significantly to model decisions corroborate prominent neuroscientific findings in the literature.
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Affiliation(s)
- Hasan A Bedel
- Department of Electrical and Electronics Engineering, Bilkent University, Ankara 06800, Turkey; National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara 06800, Turkey
| | - Irmak Sivgin
- Department of Electrical and Electronics Engineering, Bilkent University, Ankara 06800, Turkey; National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara 06800, Turkey
| | - Onat Dalmaz
- Department of Electrical and Electronics Engineering, Bilkent University, Ankara 06800, Turkey; National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara 06800, Turkey
| | - Salman U H Dar
- Department of Electrical and Electronics Engineering, Bilkent University, Ankara 06800, Turkey; National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara 06800, Turkey
| | - Tolga Çukur
- Department of Electrical and Electronics Engineering, Bilkent University, Ankara 06800, Turkey; National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara 06800, Turkey; Neuroscience Program, Bilkent University, Ankara 06800, Turkey.
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26
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Truzzi A, Cusack R. The development of intrinsic timescales: A comparison between the neonate and adult brain. Neuroimage 2023; 275:120155. [PMID: 37169116 DOI: 10.1016/j.neuroimage.2023.120155] [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: 01/23/2023] [Revised: 05/01/2023] [Accepted: 05/02/2023] [Indexed: 05/13/2023] Open
Abstract
In human adults and other mammals, different brain regions have distinct intrinsic timescales over which they integrate information, from shorter in unimodal sensory-motor regions to longer in transmodal higher-order regions. These have been related to cognitive performance and clinical symptoms, but it remains unclear how they develop. We asked if there are regional differences in timescales at birth that could shape learning by acting as an inductive bias, or if they develop later as the temporal statistics of the environment are learned. We used resting-state fMRI to characterise timescales in human neonates and adults. They were highly consistent across two independent neonatal groups, but in both sensory-motor and higher order areas, timescales were longer in infants compared to adults, as might be expected from their less developed myelination, and recent evidence of longer neural segments in infants watching naturalistic stimuli. In adults, we replicated the finding that transmodal areas have longer timescales than sensory-motor areas, but in infants the opposite pattern was found, driven by long infant timescales in the somotomotor network. Across regions within single brain networks, both positive (limbic) and negative (visual) correlations were found between neonates and adults. In conclusion, neonatal timescales were found to be highly structured, but distinct from adults, suggesting they act as an inductive bias that favours learning on longer timescales, particularly in unimodal regions and then develop with experience or maturation. This "take it slow" initial approach might help human infants to create more regularised, holistic representations of the input less bound to fleeting details, which would favour the development of abstract and contextual representations.
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Affiliation(s)
- Anna Truzzi
- School of Psychology, Trinity College Dublin, Dublin, Ireland; Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland.
| | - Rhodri Cusack
- School of Psychology, Trinity College Dublin, Dublin, Ireland; Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
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27
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McGowan AL, Sayed F, Boyd ZM, Jovanova M, Kang Y, Speer ME, Cosme D, Mucha PJ, Ochsner KN, Bassett DS, Falk EB, Lydon-Staley DM. Dense Sampling Approaches for Psychiatry Research: Combining Scanners and Smartphones. Biol Psychiatry 2023; 93:681-689. [PMID: 36797176 PMCID: PMC10038886 DOI: 10.1016/j.biopsych.2022.12.012] [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: 05/13/2022] [Revised: 11/22/2022] [Accepted: 12/14/2022] [Indexed: 12/24/2022]
Abstract
Together, data from brain scanners and smartphones have sufficient coverage of biology, psychology, and environment to articulate between-person differences in the interplay within and across biological, psychological, and environmental systems thought to underlie psychopathology. An important next step is to develop frameworks that combine these two modalities in ways that leverage their coverage across layers of human experience to have maximum impact on our understanding and treatment of psychopathology. We review literature published in the last 3 years highlighting how scanners and smartphones have been combined to date, outline and discuss the strengths and weaknesses of existing approaches, and sketch a network science framework heretofore underrepresented in work combining scanners and smartphones that can push forward our understanding of health and disease.
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Affiliation(s)
- Amanda L McGowan
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychology, Concordia University, Montréal, Québec, Canada
| | - Farah Sayed
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Zachary M Boyd
- Department of Mathematics, Brigham Young University, Provo, Utah
| | - Mia Jovanova
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Yoona Kang
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Megan E Speer
- Department of Psychology, Columbia University, New York, New York
| | - Danielle Cosme
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Peter J Mucha
- Department of Mathematics, Dartmouth College, Hanover, New Hampshire
| | - Kevin N Ochsner
- Department of Psychology, Columbia University, New York, New York
| | - Dani S Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Electrical & Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Santa Fe Institute, Santa Fe, New Mexico
| | - Emily B Falk
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania; Marketing Department, Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania; Operations, Information and Decisions, Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania
| | - David M Lydon-Staley
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania; Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania.
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28
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Kong R, Tan YR, Wulan N, Ooi LQR, Farahibozorg SR, Harrison S, Bijsterbosch JD, Bernhardt BC, Eickhoff S, Yeo BTT. Comparison Between Gradients and Parcellations for Functional Connectivity Prediction of Behavior. Neuroimage 2023; 273:120044. [PMID: 36940760 DOI: 10.1016/j.neuroimage.2023.120044] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 03/15/2023] [Accepted: 03/17/2023] [Indexed: 03/23/2023] Open
Abstract
Resting-state functional connectivity (RSFC) is widely used to predict behavioral measures. To predict behavioral measures, representing RSFC with parcellations and gradients are the two most popular approaches. Here, we compare parcellation and gradient approaches for RSFC-based prediction of a broad range of behavioral measures in the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. Among the parcellation approaches, we consider group-average "hard" parcellations (Schaefer et al., 2018), individual-specific "hard" parcellations (Kong et al., 2021a), and an individual-specific "soft" parcellation (spatial independent component analysis with dual regression; Beckmann et al., 2009). For gradient approaches, we consider the well-known principal gradients (Margulies et al., 2016) and the local gradient approach that detects local RSFC changes (Laumann et al., 2015). Across two regression algorithms, individual-specific hard-parcellation performs the best in the HCP dataset, while the principal gradients, spatial independent component analysis and group-average "hard" parcellations exhibit similar performance. On the other hand, principal gradients and all parcellation approaches perform similarly in the ABCD dataset. Across both datasets, local gradients perform the worst. Finally, we find that the principal gradient approach requires at least 40 to 60 gradients to perform as well as parcellation approaches. While most principal gradient studies utilize a single gradient, our results suggest that incorporating higher order gradients can provide significant behaviorally relevant information. Future work will consider the inclusion of additional parcellation and gradient approaches for comparison.
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Affiliation(s)
- Ru Kong
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Yan Rui Tan
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - Naren Wulan
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Leon Qi Rong Ooi
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Seyedeh-Rezvan Farahibozorg
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Samuel Harrison
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Janine D Bijsterbosch
- Department of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Simon Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
| | - B T Thomas Yeo
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
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29
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Tian Y, Vitiello MV, Wang H, Wang Y, Dong D, Xu H, Yu P, Qiu J, He Q, Chen H, Feng T, Lei X. Risk of insomnia during COVID-19: effects of depression and brain functional connectivity. Cereb Cortex 2023:7030621. [PMID: 36749000 DOI: 10.1093/cercor/bhad016] [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: 10/27/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 02/08/2023] Open
Abstract
Normal sleepers may be at risk for insomnia during COVID-19. Identifying psychological factors and neural markers that predict their insomnia risk, as well as investigating possible courses of insomnia development, could lead to more precise targeted interventions for insomnia during similar public health emergencies. Insomnia severity index of 306 participants before and during COVID-19 were employed to determine the development of insomnia, while pre-COVID-19 psychometric and resting-state fMRI data were used to explore corresponding psychological and neural markers of insomnia development. Normal sleepers as a group reported a significant increase in insomnia symptoms after COVID-19 outbreak (F = 4.618, P = 0.0102, df = 2, 609.9). Depression was found to significantly contribute to worse insomnia (β = 0.066, P = 0.024). Subsequent analysis found that functional connectivity between the precentral gyrus and middle/inferior temporal gyrus mediated the association between pre-COVID-19 depression and insomnia symptoms during COVID-19. Cluster analysis identified that postoutbreak insomnia symptoms followed 3 courses (lessened, slightly worsened, and developed into mild insomnia), and pre-COVID-19 depression symptoms and functional connectivities predicted these courses. Timely identification and treatment of at-risk individuals may help avoid the development of insomnia in the face of future health-care emergencies, such as those arising from COVID-19 variants.
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Affiliation(s)
- Yun Tian
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China.,Key Laboratory of Cognition and Personality, Ministry of Education, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China
| | - Michael V Vitiello
- Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Box 356560, 1959 NE Pacific Street, Seattle, WA 98195-6560, United States
| | - Haien Wang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China.,Key Laboratory of Cognition and Personality, Ministry of Education, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China
| | - Yulin Wang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China.,Key Laboratory of Cognition and Personality, Ministry of Education, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China
| | - Debo Dong
- Key Laboratory of Cognition and Personality, Ministry of Education, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China.,Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Wilhelm-Johnen-Strasse, Jülich 52425, Germany
| | - Hongzhou Xu
- Key Laboratory of Cognition and Personality, Ministry of Education, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China
| | - Ping Yu
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China.,Key Laboratory of Cognition and Personality, Ministry of Education, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality, Ministry of Education, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China.,Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China
| | - Qinghua He
- Key Laboratory of Cognition and Personality, Ministry of Education, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China.,Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China
| | - Hong Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China.,Research Center of Psychology and Social Development, Southwest University, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China
| | - Tingyong Feng
- Key Laboratory of Cognition and Personality, Ministry of Education, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China.,Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China.,Key Laboratory of Cognition and Personality, Ministry of Education, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China
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30
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Zhu Z, Wang H, Bi H, Lv J, Zhang X, Wang S, Zou L. Dynamic functional connectivity changes of resting-state brain network in attention-deficit/hyperactivity disorder. Behav Brain Res 2023; 437:114121. [PMID: 36162641 DOI: 10.1016/j.bbr.2022.114121] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 09/16/2022] [Accepted: 09/20/2022] [Indexed: 11/26/2022]
Abstract
Patients with attention-deficit/hyperactivity disorder (ADHD) have shown abnormal functional connectivity and network disruptions at the whole-brain static level. However, the changes in brain networks in ADHD patients from dynamic functional connectivity (DFC) perspective have not been fully understood. Accordingly, we executed DFC analysis on resting-state fMRI data of 25 ADHD patients and 27 typically developing (TD) children. A sliding window and Pearson correlation were used to construct the dynamic brain network of all subjects. The k-means+ + clustering method was used to recognize three recurring DFC states, and finally, the mean dwell time, the fraction of time spent for each state, and graph theory metrics were quantified for further analysis. Our results showed that ADHD patients had abnormally increased mean dwell time and the fraction of time spent in state 2, which reached a significant level (p < 0.05). In addition, a weak correlation between the default mode network was associated in three states, and the positive correlations between visual network and attention network were smaller than TD in three states. Finally, the integration of each network node of ADHD in state 2 is more potent than that of TD, and the degree of node segregation is smaller than that of TD. These findings provide new evidence for the DFC study of ADHD; dynamic changes may better explain the developmental delay of ADHD and have particular significance for studying neurological mechanisms and adjuvant therapy of ADHD.
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Affiliation(s)
- Zhihao Zhu
- The School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, China
| | - Hongwei Wang
- The School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou University, Changzhou, Jiangsu 213164, China
| | - Hui Bi
- The School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou University, Changzhou, Jiangsu 213164, China
| | - Jidong Lv
- The School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, China
| | - Xiaotong Zhang
- The College of Electrical Engineering, Zhejiang University, 38 Zheda Road, Hangzhou 310000, China
| | - Suhong Wang
- Clinical Psychology, The Third Affiliated Hospital of Soochow University, Juqian Road No. 185, Changzhou, Jiangsu 213164, China.
| | - Ling Zou
- The School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, China; The Key Laboratory of Brain Machine Collaborative Intelligence Foundation of Zhejiang Province, Hangzhou, Zhejiang 310018, China.
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31
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Targeting neural correlates of placebo effects. COGNITIVE, AFFECTIVE, & BEHAVIORAL NEUROSCIENCE 2022; 23:217-236. [PMID: 36517733 DOI: 10.3758/s13415-022-01039-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/26/2022] [Indexed: 12/15/2022]
Abstract
Harnessing the placebo effects would prompt critical ramifications for research and clinical practice. Noninvasive brain stimulation (NIBS) techniques, such as transcranial magnetic stimulation and multifocal transcranial electric stimulation, could manipulate the placebo response by modulating the activity and excitability of its neural correlates. To identify potential stimulation targets, we conducted a meta-analysis to investigate placebo-associated regions in healthy volunteers, including studies with emotional components and painful stimuli. Using biophysical modeling, we identified NIBS solutions to manipulate placebo effects by targeting either a single key region or multiple connected areas. Moving to a network-oriented approach, we then ran a quantitative network mapping analysis on the functional connectivity profile of clusters emerging from the meta-analysis. As a result, we suggest a multielectrode optimized montage engaging the connectivity patterns of placebo-associated functional brain networks. These NIBS solutions hope to provide a starting point to actively control, modulate or enhance placebo effects in future clinical studies and cognitive enhancement studies.
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32
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One-year-later spontaneous EEG features predict visual exploratory human phenotypes. Commun Biol 2022; 5:1361. [PMID: 36509841 PMCID: PMC9744741 DOI: 10.1038/s42003-022-04294-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 11/24/2022] [Indexed: 12/14/2022] Open
Abstract
During visual exploration, eye movements are controlled by multiple stimulus- and goal-driven factors. We recently showed that the dynamics of eye movements -how/when the eye move- during natural scenes' free viewing were similar across individuals and identified two viewing styles: static and dynamic, characterized respectively by longer or shorter fixations. Interestingly, these styles could be revealed at rest, in the absence of any visual stimulus. This result supports a role of intrinsic activity in eye movement dynamics. Here we hypothesize that these two viewing styles correspond to different spontaneous patterns of brain activity. One year after the behavioural experiments, static and dynamic viewers were called back to the lab to record high density EEG activity during eyes open and eyes closed. Static viewers show higher cortical inhibition, slower individual alpha frequency peak, and longer memory of alpha oscillations. The opposite holds for dynamic viewers. We conclude that some properties of spontaneous activity predict exploratory eye movement dynamics during free viewing.
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33
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Parviainen T, Lyyra P, Nokia MS. Cardiorespiratory rhythms, brain oscillatory activity and cognition: review of evidence and proposal for significance. Neurosci Biobehav Rev 2022; 142:104908. [DOI: 10.1016/j.neubiorev.2022.104908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 09/26/2022] [Accepted: 10/05/2022] [Indexed: 11/28/2022]
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34
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Ladwig Z, Seitzman BA, Dworetsky A, Yu Y, Adeyemo B, Smith DM, Petersen SE, Gratton C. BOLD cofluctuation 'events' are predicted from static functional connectivity. Neuroimage 2022; 260:119476. [PMID: 35842100 PMCID: PMC9428936 DOI: 10.1016/j.neuroimage.2022.119476] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 06/09/2022] [Accepted: 07/12/2022] [Indexed: 11/17/2022] Open
Abstract
Recent work identified single time points ("events") of high regional cofluctuation in functional Magnetic Resonance Imaging (fMRI) which contain more large-scale brain network information than other, low cofluctuation time points. This suggested that events might be a discrete, temporally sparse signal which drives functional connectivity (FC) over the timeseries. However, a different, not yet explored possibility is that network information differences between time points are driven by sampling variability on a constant, static, noisy signal. Using a combination of real and simulated data, we examined the relationship between cofluctuation and network structure and asked if this relationship was unique, or if it could arise from sampling variability alone. First, we show that events are not discrete - there is a gradually increasing relationship between network structure and cofluctuation; ∼50% of samples show very strong network structure. Second, using simulations we show that this relationship is predicted from sampling variability on static FC. Finally, we show that randomly selected points can capture network structure about as well as events, largely because of their temporal spacing. Together, these results suggest that, while events exhibit particularly strong representations of static FC, there is little evidence that events are unique timepoints that drive FC structure. Instead, a parsimonious explanation for the data is that events arise from a single static, but noisy, FC structure.
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Affiliation(s)
- Zach Ladwig
- Interdepartmental Neuroscience Program, Northwestern University
| | - Benjamin A Seitzman
- Department of Radiation Oncology, Washington University St. Louis School of Medicine
| | | | - Yuhua Yu
- Department of Psychology, Northwestern University
| | - Babatunde Adeyemo
- Department of Neurology, Washington University St. Louis School of Medicine
| | - Derek M Smith
- Department of Neurology, Division of Cognitive Neurology/Neuropsychology, The Johns Hopkins University School of Medicine
| | - Steven E Petersen
- Department of Radiology, Washington University St. Louis School of Medicine; Department of Neurology, Washington University St. Louis School of Medicine; Department of Psychological and Brain Sciences, Washington University St. Louis School of Medicine; Department of Neuroscience, Washington University St. Louis School of Medicine; Department of Biomedical Engineering, Washington University St. Louis School of Medicine
| | - Caterina Gratton
- Interdepartmental Neuroscience Program, Northwestern University; Department of Psychology, Northwestern University; Department of Neurology, Northwestern University.
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35
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Zhang J, Zhuang K, Sun J, Liu C, Fan L, Wang X, Gu J, Qiu J. Retrieval flexibility links to creativity: evidence from computational linguistic measure. Cereb Cortex 2022; 33:4964-4976. [PMID: 36218835 DOI: 10.1093/cercor/bhac392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 09/08/2022] [Accepted: 09/09/2022] [Indexed: 11/13/2022] Open
Abstract
Creativity, the ability to generate original and valuable products, has long been linked to semantic retrieval processes. The associative theory of creativity posits flexible retrieval ability as an important basis for creative idea generation. However, there is insufficient research on how flexible memory retrieval acts on creative activities. This study aimed to capture different dynamic aspects of retrieval processes and examine the behavioral and neural associations between retrieval flexibility and creativity. We developed 5 metrics to quantify retrieval flexibility based on previous studies, which confirmed the important role of creativity. Our findings showed that retrieval flexibility was positively correlated with multiple creativity-related behavior constructs and can promote distinct search patterns in different creative groups. Moreover, high flexibility was associated with the lifetime of a specific brain state during rest, characterized by interactions among large-scale cognitive brain systems. The flexible functional connectivity within and between default mode, executive control, and salience provides further evidence on brain dynamics of creativity. Retrieval flexibility mediated the links between the lifetime of the related brain state and creativity. This new approach is expected to enhance our knowledge of the role of retrieval flexibility in creativity from a dynamic perspective.
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Affiliation(s)
- Jingyi Zhang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Kaixiang Zhuang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Jiangzhou Sun
- College of International Studies, Southwest University, Chongqing 400715, China
| | - Cheng Liu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Li Fan
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Xueyang Wang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Jing Gu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China.,Faculty of Psychology, Southwest University, Chongqing 400715, China.,Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing 400715 , China
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36
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Schlemm E, Frey BM, Mayer C, Petersen M, Fiehler J, Hanning U, Kühn S, Twerenbold R, Gallinat J, Gerloff C, Thomalla G, Cheng B. Equalization of Brain State Occupancy Accompanies Cognitive Impairment in Cerebral Small Vessel Disease. Biol Psychiatry 2022; 92:592-602. [PMID: 35691727 DOI: 10.1016/j.biopsych.2022.03.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 03/08/2022] [Accepted: 03/23/2022] [Indexed: 01/02/2023]
Abstract
BACKGROUND Cognitive impairment is a hallmark of cerebral small vessel disease (cSVD). Functional magnetic resonance imaging has highlighted connections between patterns of brain activity and variability in behavior. We aimed to characterize the associations between imaging markers of cSVD, dynamic connectivity, and cognitive impairment. METHODS We obtained magnetic resonance imaging and clinical data from the population-based Hamburg City Health Study. cSVD was quantified by white matter hyperintensities and peak-width of skeletonized mean diffusivity (PSMD). Resting-state blood oxygen level-dependent signals were clustered into discrete brain states, for which fractional occupancies (%) and dwell times (seconds) were computed. Cognition in multiple domains was assessed using validated tests. Regression analysis was used to quantify associations between white matter damage, spatial coactivation patterns, and cognitive function. RESULTS Data were available for 979 participants (ages 45-74 years, median white matter hyperintensity volume 0.96 mL). Clustering identified five brain states with the most time spent in states characterized by activation (+) or suppression (-) of the default mode network (DMN) (fractional occupancy: DMN+ = 25.1 ± 7.2%, DMN- = 25.5 ± 7.2%). Every 4.7-fold increase in white matter hyperintensity volume was associated with a 0.95-times reduction of the odds of occupying DMN+ or DMN-. Time spent in DMN-related brain states was associated with executive function. CONCLUSIONS Associations between white matter damage, whole-brain spatial coactivation patterns, and cognition suggest equalization of time spent in different brain states as a marker for cSVD-associated cognitive decline. Reduced gradients between brain states in association with brain damage and cognitive impairment reflect the dedifferentiation hypothesis of neurocognitive aging in a network-theoretical context.
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Affiliation(s)
- Eckhard Schlemm
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany.
| | - Benedikt M Frey
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Carola Mayer
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Marvin Petersen
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Jens Fiehler
- Department of Neuroradiology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Uta Hanning
- Department of Neuroradiology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Simone Kühn
- Department of Psychiatry, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Raphael Twerenbold
- Department of Cardiology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Jürgen Gallinat
- Department of Psychiatry, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Christian Gerloff
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Götz Thomalla
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Bastian Cheng
- Department of Neurology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
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37
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Tian Y, Xie C, Lei X. Isolation of subjectively reported sleepiness and objectively measured vigilance during sleep deprivation: a resting-state fMRI study. Cogn Neurodyn 2022; 16:1151-1162. [PMID: 36237404 PMCID: PMC9508300 DOI: 10.1007/s11571-021-09772-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 11/28/2021] [Accepted: 12/13/2021] [Indexed: 11/03/2022] Open
Abstract
Subjectively reported sleepiness and objectively measured vigilance are often used to assess and monitor operating performance. Evidence suggests that the response patterns of the two measures are independent of each other. However, the neural mechanism underlying this phenomenon remains unclear. This study aimed to investigate whether subjective sleepiness and objective vigilance were associated with each other. Thirty-three participants were subjected to 34 h of acute sleep deprivation. We collected sleepiness, vigilance, and resting-state fMRI data. We also located the neural mechanism of isolation of object and subject parameters. Firstly, the correlation analysis showed that there was no statistically significant correlation between the changes in vigilance and sleepiness during the sleep deprivation period. Then, implementing the support vector machine algorithm through functional connectivities as features, we found that different functional connectivity patterns underline the isolation of these two factors during sleep deprivation. The functional connectivities involved in characterizing the vulnerability of objective vigilance are more extensive, involving the connectivities within the sensorimotor network, between the subcortical and cortical network, and among multiple cortical networks. The functional connectivity involved in characterizing the vulnerability of subjective sleepiness is limited to the communication between the subcortical thalamus and the somatosensory cortex. In addition, we found that implementing global signal regression would reduce the model's power to predict vigilance and sleepiness. This work contributes to our understanding of how sleep deprivation affects individual cognition and behavior, and will be of use in the evaluation and prediction of cognitive performance during sleep loss. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-021-09772-0.
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Affiliation(s)
- Yun Tian
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715 China
- Key Laboratory of Cognition and Personality (Ministry of Education), Chongqing, 400715 China
| | - Chao Xie
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715 China
- Key Laboratory of Cognition and Personality (Ministry of Education), Chongqing, 400715 China
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38
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Zhang M, Sun Y, Saggar M. Cross-attractor repertoire provides new perspective on structure-function relationship in the brain. Neuroimage 2022; 259:119401. [PMID: 35732244 PMCID: PMC9503321 DOI: 10.1016/j.neuroimage.2022.119401] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 05/16/2022] [Accepted: 06/17/2022] [Indexed: 11/24/2022] Open
Abstract
The brain exhibits complex intrinsic dynamics, i.e., spontaneously arising activity patterns without any external inputs or tasks. Such intrinsic dynamics and their alteration are thought to play crucial roles in typical as well as atypical cognitive functioning. Linking the ever-changing intrinsic dynamics to the rather static anatomy is a challenging endeavor. Dynamical systems models are important tools for understanding how structure and function are linked in the brain. Here, we provide a novel modeling framework to examine how functional connectivity depends on structural connectivity in the brain. Existing modeling frameworks typically focus on noise-driven (or stochastic) dynamics near a single attractor. Complementing existing approaches, we examine deterministic features of the distribution of attractors, in particular, how regional states are correlated across all attractors - cross-attractor coordination. We found that cross-attractor coordination between brain regions better predicts human functional connectivity than noise-driven single-attractor dynamics. Importantly, cross-attractor coordination better accounts for the nonlinear dependency of functional connectivity on structural connectivity. Our findings suggest that functional connectivity patterns in the brain may reflect transitions between attractors, which impose an energy cost. The framework may be used to predict transitions and energy costs associated with experimental or clinical interventions.
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Affiliation(s)
- Mengsen Zhang
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA; Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA.
| | - Yinming Sun
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA
| | - Manish Saggar
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA.
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39
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Ooi LQR, Chen J, Zhang S, Kong R, Tam A, Li J, Dhamala E, Zhou JH, Holmes AJ, Yeo BTT. Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI. Neuroimage 2022; 263:119636. [PMID: 36116616 DOI: 10.1016/j.neuroimage.2022.119636] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 08/24/2022] [Accepted: 09/15/2022] [Indexed: 10/31/2022] Open
Abstract
A fundamental goal across the neurosciences is the characterization of relationships linking brain anatomy, functioning, and behavior. Although various MRI modalities have been developed to probe these relationships, direct comparisons of their ability to predict behavior have been lacking. Here, we compared the ability of anatomical T1, diffusion and functional MRI (fMRI) to predict behavior at an individual level. Cortical thickness, area and volume were extracted from anatomical T1 images. Diffusion Tensor Imaging (DTI) and approximate Neurite Orientation Dispersion and Density Imaging (NODDI) models were fitted to the diffusion images. The resulting metrics were projected to the Tract-Based Spatial Statistics (TBSS) skeleton. We also ran probabilistic tractography for the diffusion images, from which we extracted the stream count, average stream length, and the average of each DTI and NODDI metric across tracts connecting each pair of brain regions. Functional connectivity (FC) was extracted from both task and resting-state fMRI. Individualized prediction of a wide range of behavioral measures were performed using kernel ridge regression, linear ridge regression and elastic net regression. Consistency of the results were investigated with the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. In both datasets, FC-based models gave the best prediction performance, regardless of regression model or behavioral measure. This was especially true for the cognitive component. Furthermore, all modalities were able to predict cognition better than other behavioral components. Combining all modalities improved prediction of cognition, but not other behavioral components. Finally, across all behaviors, combining resting and task FC yielded prediction performance similar to combining all modalities. Overall, our study suggests that in the case of healthy children and young adults, behaviorally-relevant information in T1 and diffusion features might reflect a subset of the variance captured by FC.
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Affiliation(s)
- Leon Qi Rong Ooi
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Jianzhong Chen
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Shaoshi Zhang
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Ru Kong
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Angela Tam
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Jingwei Li
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Elvisha Dhamala
- Yale University, Departments of Psychology and Psychiatry, New Haven, CT, United States; Kavli Institute for Neuroscience, Yale University, New Haven, CT, United States
| | - Juan Helen Zhou
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - Avram J Holmes
- Yale University, Departments of Psychology and Psychiatry, New Haven, CT, United States; Wu Tsai Institute, Yale University, New Haven, CT, United States
| | - B T Thomas Yeo
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore.
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40
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Zarkali A, Luppi AI, Stamatakis EA, Reeves S, McColgan P, Leyland LA, Lees AJ, Weil RS. Changes in dynamic transitions between integrated and segregated states underlie visual hallucinations in Parkinson's disease. Commun Biol 2022; 5:928. [PMID: 36075964 PMCID: PMC9458713 DOI: 10.1038/s42003-022-03903-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 08/25/2022] [Indexed: 11/09/2022] Open
Abstract
Hallucinations are a core feature of psychosis and common in Parkinson's. Their transient, unexpected nature suggests a change in dynamic brain states, but underlying causes are unknown. Here, we examine temporal dynamics and underlying structural connectivity in Parkinson's-hallucinations using a combination of functional and structural MRI, network control theory, neurotransmitter density and genetic analyses. We show that Parkinson's-hallucinators spent more time in a predominantly Segregated functional state with fewer between-state transitions. The transition from integrated-to-segregated state had lower energy cost in Parkinson's-hallucinators; and was therefore potentially preferable. The regional energy needed for this transition was correlated with regional neurotransmitter density and gene expression for serotoninergic, GABAergic, noradrenergic and cholinergic, but not dopaminergic, receptors. We show how the combination of neurochemistry and brain structure jointly shape functional brain dynamics leading to hallucinations and highlight potential therapeutic targets by linking these changes to neurotransmitter systems involved in early sensory and complex visual processing.
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Affiliation(s)
- Angeliki Zarkali
- Dementia Research Centre, University College London, 8-11 Queen Square, London, WC1N 3AR, UK.
| | - Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Suzanne Reeves
- Division of Psychiatry, University College London, 149 Tottenham Court Rd, London, W1T 7BN, UK
| | - Peter McColgan
- Huntington's Disease Centre, University College London, Russell Square House, London, WC1B 5EH, UK
| | - Louise-Ann Leyland
- Dementia Research Centre, University College London, 8-11 Queen Square, London, WC1N 3AR, UK
| | - Andrew J Lees
- Reta Lila Weston Institute of Neurological Studies, University College London, 1 Wakefield Street, London, WC1N 1PJ, UK
| | - Rimona S Weil
- Dementia Research Centre, University College London, 8-11 Queen Square, London, WC1N 3AR, UK
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, WC1N 3AR, UK
- Movement Disorders Consortium, University College London, London, WC1N 3BG, UK
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41
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Favaretto C, Allegra M, Deco G, Metcalf NV, Griffis JC, Shulman GL, Brovelli A, Corbetta M. Subcortical-cortical dynamical states of the human brain and their breakdown in stroke. Nat Commun 2022; 13:5069. [PMID: 36038566 PMCID: PMC9424299 DOI: 10.1038/s41467-022-32304-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 07/25/2022] [Indexed: 11/17/2022] Open
Abstract
The mechanisms controlling dynamical patterns in spontaneous brain activity are poorly understood. Here, we provide evidence that cortical dynamics in the ultra-slow frequency range (<0.01–0.1 Hz) requires intact cortical-subcortical communication. Using functional magnetic resonance imaging (fMRI) at rest, we identify Dynamic Functional States (DFSs), transient but recurrent clusters of cortical and subcortical regions synchronizing at ultra-slow frequencies. We observe that shifts in cortical clusters are temporally coincident with shifts in subcortical clusters, with cortical regions flexibly synchronizing with either limbic regions (hippocampus/amygdala), or subcortical nuclei (thalamus/basal ganglia). Focal lesions induced by stroke, especially those damaging white matter connections between basal ganglia/thalamus and cortex, provoke anomalies in the fraction times, dwell times, and transitions between DFSs, causing a bias toward abnormal network integration. Dynamical anomalies observed 2 weeks after stroke recover in time and contribute to explaining neurological impairment and long-term outcome. Favaretto et al. show that the brain rapidly alternates between transient connectivity patterns, with cortical regions flexibly synchronizing with two groups of subcortical regions, and that this dynamic is abnormal in stroke patients.
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Affiliation(s)
- Chiara Favaretto
- Padova Neuroscience Center (PNC), University of Padova, via Orus 2/B, 35129, Padova, Italy. .,Department of Neuroscience (DNS), University of Padova, via Giustiniani 2, 35128, Padova, Italy.
| | - Michele Allegra
- Padova Neuroscience Center (PNC), University of Padova, via Orus 2/B, 35129, Padova, Italy.,Department of Physics and Astronomy "Galileo Galilei", University of Padova, via Marzolo 8, 35131, Padova, Italy.,Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS, 13005, Marseille, France
| | - Gustavo Deco
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, 08005, Barcelona, Catalonia, Spain.,Institució Catalana de Recerca I Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010, Barcelona, Catalonia, Spain
| | - Nicholas V Metcalf
- Department of Neurology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Joseph C Griffis
- Department of Neurology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Gordon L Shulman
- Department of Neurology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA.,Department of Radiology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Andrea Brovelli
- Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS, 13005, Marseille, France
| | - Maurizio Corbetta
- Padova Neuroscience Center (PNC), University of Padova, via Orus 2/B, 35129, Padova, Italy. .,Department of Neuroscience (DNS), University of Padova, via Giustiniani 2, 35128, Padova, Italy. .,Department of Neurology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA. .,Department of Radiology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA. .,VIMM, Venetian Institute of Molecular Medicine (VIMM), Biomedical Foundation, via Orus 2, 35129, Padova, Italy.
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42
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Saggar M, Shine JM, Liégeois R, Dosenbach NUF, Fair D. Precision dynamical mapping using topological data analysis reveals a hub-like transition state at rest. Nat Commun 2022; 13:4791. [PMID: 35970984 PMCID: PMC9378660 DOI: 10.1038/s41467-022-32381-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 07/27/2022] [Indexed: 01/01/2023] Open
Abstract
In the absence of external stimuli, neural activity continuously evolves from one configuration to another. Whether these transitions or explorations follow some underlying arrangement or lack a predictable ordered plan remains to be determined. Here, using fMRI data from highly sampled individuals (~5 hours of resting-state data per individual), we aimed to reveal the rules that govern transitions in brain activity at rest. Our Topological Data Analysis based Mapper approach characterized a highly visited transition state of the brain that acts as a switch between different neural configurations to organize the spontaneous brain activity. Further, while the transition state was characterized by a uniform representation of canonical resting-state networks (RSNs), the periphery of the landscape was dominated by a subject-specific combination of RSNs. Altogether, we revealed rules or principles that organize spontaneous brain activity using a precision dynamics approach.
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Affiliation(s)
- Manish Saggar
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
| | - James M Shine
- Brain and Mind Center, The University of Sydney, Sydney, NSW, Australia
| | - Raphaël Liégeois
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Nico U F Dosenbach
- Departments of Neurology, Radiology, Pediatrics and Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA
| | - Damien Fair
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, USA
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43
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Fu W, Dai C, Chen J, Wang L, Song T, Peng Z, Xu M, Xu L, Tang Y, Shao Y. Altered insular functional connectivity correlates to impaired vigilant attention after sleep deprivation: A resting-state functional magnetic resonance imaging study. Front Neurosci 2022; 16:889009. [PMID: 35958999 PMCID: PMC9361853 DOI: 10.3389/fnins.2022.889009] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 07/05/2022] [Indexed: 11/25/2022] Open
Abstract
Objectives This study used resting-state functional magnetic resonance imaging (rs-fMRI) scans to assess the dominant effects of 36 h total sleep deprivation (TSD) on vigilant attention and changes in the resting-state network. Materials and methods Twenty-two healthy college students were enrolled in this study. Participants underwent two rs-fMRI scans, once in rested wakefulness (RW) and once after 36 h of TSD. We used psychomotor vigilance tasks (PVT) to measure vigilant attention. The region-of-interest to region-of-interest correlation was employed to analyze the relationship within the salience network (SN) and between other networks after 36 h of TSD. Furthermore, Pearson’s correlation analysis investigated the relationship between altered insular functional connectivity and PVT performance. Results After 36 h of TSD, participants showed significantly decreased vigilant attention. Additionally, TSD induced decreased functional connectivity between the visual and parietal regions, whereas, a significant increase was observed between the anterior cingulate cortex and insula. Moreover, changes in functional connectivity in the anterior cingulate cortex and insula showed a significant positive correlation with the response time to PVT. Conclusion Our results suggest that 36 h of TSD impaired vigilant visual attention, resulting in slower reaction times. The decrease in visual-parietal functional connectivity may be related to the decrease in the reception of information in the brain. Enhanced functional connectivity of the anterior cingulate cortex with the insula revealed that the brain network compensation occurs mainly in executive function.
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Affiliation(s)
- Weiwei Fu
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Cimin Dai
- School of Psychology, Beijing Sport University, Beijing, China
| | - Jie Chen
- School of Psychology, Beijing Sport University, Beijing, China
| | - Letong Wang
- School of Psychology, Beijing Sport University, Beijing, China
| | - Tao Song
- School of Psychology, Beijing Sport University, Beijing, China
| | - Ziyi Peng
- School of Psychology, Beijing Sport University, Beijing, China
| | - Mengmeng Xu
- School of Psychology, Beijing Sport University, Beijing, China
| | - Lin Xu
- School of Psychology, Beijing Sport University, Beijing, China
| | - Yuguo Tang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- *Correspondence: Yuguo Tang,
| | - Yongcong Shao
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- School of Psychology, Beijing Sport University, Beijing, China
- Yongcong Shao,
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44
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The modulatory effect of adaptive task-switching training on resting-state neural network dynamics in younger and older adults. Sci Rep 2022; 12:9541. [PMID: 35680953 PMCID: PMC9184743 DOI: 10.1038/s41598-022-13708-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 05/26/2022] [Indexed: 11/08/2022] Open
Abstract
With increasing life expectancy and active aging, it becomes crucial to investigate methods which could compensate for generally detected cognitive aging processes. A promising candidate is adaptive cognitive training, during which task difficulty is adjusted to the participants' performance level to enhance the training and potential transfer effects. Measuring intrinsic brain activity is suitable for detecting possible distributed training-effects since resting-state dynamics are linked to the brain's functional flexibility and the effectiveness of different cognitive processes. Therefore, we investigated if adaptive task-switching training could modulate resting-state neural dynamics in younger (18-25 years) and older (60-75 years) adults (79 people altogether). We examined spectral power density on resting-state EEG data for measuring oscillatory activity, and multiscale entropy for detecting intrinsic neural complexity. Decreased coarse timescale entropy and lower frequency band power as well as increased fine timescale entropy and higher frequency band power revealed a shift from more global to local information processing with aging before training. However, cognitive training modulated these age-group differences, as coarse timescale entropy and lower frequency band power increased from pre- to post-training in the old-training group. Overall, our results suggest that cognitive training can modulate neural dynamics even when measured outside of the trained task.
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45
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Zhao M, Yan W, Luo N, Zhi D, Fu Z, Du Y, Yu S, Jiang T, Calhoun VD, Sui J. An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data. Med Image Anal 2022; 78:102413. [PMID: 35305447 PMCID: PMC9035078 DOI: 10.1016/j.media.2022.102413] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 10/27/2021] [Accepted: 03/01/2022] [Indexed: 12/30/2022]
Abstract
Functional magnetic resonance imaging (fMRI) as a promising tool to investigate psychotic disorders can be decomposed into useful imaging features such as time courses (TCs) of independent components (ICs) and functional network connectivity (FNC) calculated by TC cross-correlation. TCs reflect the temporal dynamics of brain activity and the FNC characterizes temporal coherence across intrinsic brain networks. Both features have been used as input to deep learning approaches with decent results. However, few studies have tried to leverage their complementary information to learn optimal representations at multiple facets. Motivated by this, we proposed a Hybrid Deep Learning Framework integrating brain Connectivity and Activity (HDLFCA) together by combining convolutional recurrent neural network (C-RNN) and deep neural network (DNN), aiming to improve classification accuracy and interpretability simultaneously. Specifically, C-RNNAM was proposed to extract temporal dynamic dependencies with an attention module (AM) to automatically learn discriminative knowledge from TC nodes, while DNN was applied to identify the most group-discriminative FNC patterns with layer-wise relevance propagation (LRP). Then, both prediction outputs were concatenated to build a new feature matrix, generating the final decision by logistic regression. The effectiveness of HDLFCA was validated on both multi-site schizophrenia (SZ, n ∼ 1100) and public autism datasets (ABIDE, n ∼ 1522) by outperforming 12 alternative models at 2.8-8.9% accuracy, including 8 models using either static FNC or TCs and 4 models using dynamic FNC. Appreciable classification accuracy was achieved for HC vs. SZ (85.3%) and HC vs. Autism (72.4%) respectively. More importantly, the most group-discriminative brain regions can be easily attributed and visualized, providing meaningful biological interpretability and highlighting the great potential of the proposed HDLFCA model in the identification of valid neuroimaging biomarkers.
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Affiliation(s)
- Min Zhao
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Weizheng Yan
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Na Luo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - 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
| | - Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Shan Yu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - 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
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
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Chen J, Tam A, Kebets V, Orban C, Ooi LQR, Asplund CL, Marek S, Dosenbach NUF, Eickhoff SB, Bzdok D, Holmes AJ, Yeo BTT. Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study. Nat Commun 2022; 13:2217. [PMID: 35468875 PMCID: PMC9038754 DOI: 10.1038/s41467-022-29766-8] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 03/18/2022] [Indexed: 12/30/2022] Open
Abstract
How individual differences in brain network organization track behavioral variability is a fundamental question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, most studies focus on single behavioral traits, thus not capturing broader relationships across behaviors. In a large sample of 1858 typically developing children from the Adolescent Brain Cognitive Development (ABCD) study, we show that predictive network features are distinct across the domains of cognitive performance, personality scores and mental health assessments. On the other hand, traits within each behavioral domain are predicted by similar network features. Predictive network features and models generalize to other behavioral measures within the same behavioral domain. Although tasks are known to modulate the functional connectome, predictive network features are similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood.
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Affiliation(s)
- Jianzhong Chen
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore.,Centre for Translational MR Research, National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Angela Tam
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore.,Centre for Translational MR Research, National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Valeria Kebets
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore.,Centre for Translational MR Research, National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Csaba Orban
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore.,Centre for Translational MR Research, National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Leon Qi Rong Ooi
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.,Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore.,Centre for Translational MR Research, National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore.,Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
| | - Christopher L Asplund
- Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore.,Centre for Translational MR Research, National University of Singapore, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore.,Division of Social Sciences, Yale-NUS College, Singapore, Singapore.,Department of Psychology, National University of Singapore, Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
| | - Scott Marek
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.,Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.,Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA.,Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviours (INM-7), Research Center Jülich, Jülich, Germany
| | - Danilo Bzdok
- Department of Biomedical Engineering, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.,Mila - Quebec AI Institute, Montreal, QC, Canada
| | - Avram J Holmes
- Yale University, Departments of Psychology and Psychiatry, New Haven, CT, USA
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore. .,Centre for Sleep and Cognition, National University of Singapore, Singapore, Singapore. .,Centre for Translational MR Research, National University of Singapore, Singapore, Singapore. .,N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore. .,Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore. .,Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
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47
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Maltbie E, Yousefi B, Zhang X, Kashyap A, Keilholz S. Comparison of Resting-State Functional MRI Methods for Characterizing Brain Dynamics. Front Neural Circuits 2022; 16:681544. [PMID: 35444518 PMCID: PMC9013751 DOI: 10.3389/fncir.2022.681544] [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: 03/16/2021] [Accepted: 03/11/2022] [Indexed: 11/13/2022] Open
Abstract
Resting-state functional MRI (fMRI) exhibits time-varying patterns of functional connectivity. Several different analysis approaches have been developed for examining these resting-state dynamics including sliding window connectivity (SWC), phase synchrony (PS), co-activation pattern (CAP), and quasi-periodic patterns (QPP). Each of these approaches can be used to generate patterns of activity or inter-areal coordination which vary across time. The individual frames can then be clustered to produce temporal groupings commonly referred to as “brain states.” Several recent publications have investigated brain state alterations in clinical populations, typically using a single method for quantifying frame-wise functional connectivity. This study directly compares the results of k-means clustering in conjunction with three of these resting-state dynamics methods (SWC, CAP, and PS) and quantifies the brain state dynamics across several metrics using high resolution data from the human connectome project. Additionally, these three dynamics methods are compared by examining how the brain state characterizations vary during the repeated sequences of brain states identified by a fourth dynamic analysis method, QPP. The results indicate that the SWC, PS, and CAP methods differ in the clusters and trajectories they produce. A clear illustration of these differences is given by how each one results in a very different clustering profile for the 24s sequences explicitly identified by the QPP algorithm. PS clustering is sensitive to QPPs with the mid-point of most QPP sequences grouped into the same single cluster. CAPs are also highly sensitive to QPPs, separating each phase of the QPP sequences into different sets of clusters. SWC (60s window) is less sensitive to QPPs. While the QPPs are slightly more likely to occur during specific SWC clusters, the SWC clustering does not vary during the 24s QPP sequences, the goal of this work is to improve both the practical and theoretical understanding of different resting-state dynamics methods, thereby enabling investigators to better conceptualize and implement these tools for characterizing functional brain networks.
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48
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Deng S, Li J, Thomas Yeo BT, Gu S. Control theory illustrates the energy efficiency in the dynamic reconfiguration of functional connectivity. Commun Biol 2022; 5:295. [PMID: 35365757 PMCID: PMC8975837 DOI: 10.1038/s42003-022-03196-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 02/22/2022] [Indexed: 11/09/2022] Open
Abstract
The brain’s functional connectivity fluctuates over time instead of remaining steady in a stationary mode even during the resting state. This fluctuation establishes the dynamical functional connectivity that transitions in a non-random order between multiple modes. Yet it remains unexplored how the transition facilitates the entire brain network as a dynamical system and what utility this mechanism for dynamic reconfiguration can bring over the widely used graph theoretical measurements. To address these questions, we propose to conduct an energetic analysis of functional brain networks using resting-state fMRI and behavioral measurements from the Human Connectome Project. Through comparing the state transition energy under distinct adjacent matrices, we justify that dynamic functional connectivity leads to 60% less energy cost to support the resting state dynamics than static connectivity when driving the transition through default mode network. Moreover, we demonstrate that combining graph theoretical measurements and our energy-based control measurements as the feature vector can provide complementary prediction power for the behavioral scores (Combination vs. Control: t = 9.41, p = 1.64e−13; Combination vs. Graph: t = 4.92, p = 3.81e−6). Our approach integrates statistical inference and dynamical system inspection towards understanding brain networks. A framework that allows for the statistical investigation of the dynamic aspect of functional connectivity derived from resting-state fMRI is developed that is shown to complementarily predict individual differences in behavioral measurements compared to existing approaches.
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Affiliation(s)
- Shikuang Deng
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Jingwei Li
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany.,Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore.,Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, Singapore.,N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore.,Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
| | - Shi Gu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
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49
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Hsieh H, Xu Q, Yang F, Zhang Q, Hao J, Liu G, Liu R, Yu Q, Zhang Z, Xing W, Bernhardt BC, Lu G, Zhang Z. Distinct Functional Cortico-Striato-Thalamo-Cerebellar Networks in Genetic Generalized and Focal Epilepsies with Generalized Tonic-Clonic Seizures. J Clin Med 2022; 11:jcm11061612. [PMID: 35329938 PMCID: PMC8951449 DOI: 10.3390/jcm11061612] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 02/18/2022] [Accepted: 03/09/2022] [Indexed: 02/04/2023] Open
Abstract
This study aimed to delineate cortico-striato-thalamo-cerebellar network profiles based on static and dynamic connectivity analysis in genetic generalized and focal epilepsies with generalized tonic-clonic seizures, and to evaluate its potential for distinguishing these two epilepsy syndromes. A total of 342 individuals participated in the study (114 patients with genetic generalized epilepsy with generalized tonic-clonic seizures (GE-GTCS), and 114 age- and sex-matched patients with focal epilepsy with focal to bilateral tonic-clonic seizure (FE-FBTS), 114 healthy controls). Resting-state fMRI data were examined through static and dynamic functional connectivity (dFC) analyses, constructing cortico-striato-thalamo-cerebellar networks. Network patterns were compared between groups, and were correlated to epilepsy duration. A pattern-learning algorithm was applied to network features for classifying both epilepsy syndromes. FE-FBTS and GE-GTCS both presented with altered functional connectivity in subregions of the motor/premotor and somatosensory networks. Among these two groups, the connectivity within the cerebellum increased in the static, while the dFC variability decreased; conversely, the connectivity of the thalamus decreased in FE-FBTS and increased in GE-GTCS in the static state. Connectivity differences between patient groups were mainly located in the thalamus and cerebellum, and correlated with epilepsy duration. Support vector machine (SVM) classification had accuracies of 66.67%, 68.42%, and 77.19% when using static, dynamic, and combined approaches to categorize GE-GTCS and FE-GTCS. Network features with high discriminative ability predominated in the thalamic and cerebellar connectivities. The network embedding of the thalamus and cerebellum likely plays an important differential role in GE-GTCS and FE-FBTS, and could serve as an imaging biomarker for differential diagnosis.
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Affiliation(s)
- Hsinyu Hsieh
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China; (H.H.); (Q.X.); (F.Y.); (Q.Z.); (J.H.); (G.L.); (R.L.); (Q.Y.); (Z.Z.); (G.L.)
| | - Qiang Xu
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China; (H.H.); (Q.X.); (F.Y.); (Q.Z.); (J.H.); (G.L.); (R.L.); (Q.Y.); (Z.Z.); (G.L.)
| | - Fang Yang
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China; (H.H.); (Q.X.); (F.Y.); (Q.Z.); (J.H.); (G.L.); (R.L.); (Q.Y.); (Z.Z.); (G.L.)
| | - Qirui Zhang
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China; (H.H.); (Q.X.); (F.Y.); (Q.Z.); (J.H.); (G.L.); (R.L.); (Q.Y.); (Z.Z.); (G.L.)
| | - Jingru Hao
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China; (H.H.); (Q.X.); (F.Y.); (Q.Z.); (J.H.); (G.L.); (R.L.); (Q.Y.); (Z.Z.); (G.L.)
| | - Gaoping Liu
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China; (H.H.); (Q.X.); (F.Y.); (Q.Z.); (J.H.); (G.L.); (R.L.); (Q.Y.); (Z.Z.); (G.L.)
| | - Ruoting Liu
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China; (H.H.); (Q.X.); (F.Y.); (Q.Z.); (J.H.); (G.L.); (R.L.); (Q.Y.); (Z.Z.); (G.L.)
| | - Qianqian Yu
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China; (H.H.); (Q.X.); (F.Y.); (Q.Z.); (J.H.); (G.L.); (R.L.); (Q.Y.); (Z.Z.); (G.L.)
| | - Zixuan Zhang
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China; (H.H.); (Q.X.); (F.Y.); (Q.Z.); (J.H.); (G.L.); (R.L.); (Q.Y.); (Z.Z.); (G.L.)
| | - Wei Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University/Changzhou First People’s Hospital, Changzhou 213004, China;
| | - Boris C. Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC H3A 2B4, Canada;
| | - Guangming Lu
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China; (H.H.); (Q.X.); (F.Y.); (Q.Z.); (J.H.); (G.L.); (R.L.); (Q.Y.); (Z.Z.); (G.L.)
| | - Zhiqiang Zhang
- Department of Diagnostic Radiology, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210093, China; (H.H.); (Q.X.); (F.Y.); (Q.Z.); (J.H.); (G.L.); (R.L.); (Q.Y.); (Z.Z.); (G.L.)
- Correspondence:
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50
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Bauer LG, Hirsch F, Jones C, Hollander M, Grohs P, Anand A, Plant C, Wohlschläger A. Quantification of Kuramoto Coupling Between Intrinsic Brain Networks Applied to fMRI Data in Major Depressive Disorder. Front Comput Neurosci 2022; 16:729556. [PMID: 35311219 PMCID: PMC8929174 DOI: 10.3389/fncom.2022.729556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 01/28/2022] [Indexed: 11/13/2022] Open
Abstract
Organized patterns of system-wide neural activity adapt fluently within the brain to adjust behavioral performance to environmental demands. In major depressive disorder (MD), markedly different co-activation patterns across the brain emerge from a rather similar structural substrate. Despite the application of advanced methods to describe the functional architecture, e.g., between intrinsic brain networks (IBNs), the underlying mechanisms mediating these differences remain elusive. Here we propose a novel complementary approach for quantifying the functional relations between IBNs based on the Kuramoto model. We directly estimate the Kuramoto coupling parameters (K) from IBN time courses derived from empirical fMRI data in 24 MD patients and 24 healthy controls. We find a large pattern with a significant number of Ks depending on the disease severity score Hamilton D, as assessed by permutation testing. We successfully reproduced the dependency in an independent test data set of 44 MD patients and 37 healthy controls. Comparing the results to functional connectivity from partial correlations (FC), to phase synchrony (PS) as well as to first order auto-regressive measures (AR) between the same IBNs did not show similar correlations. In subsequent validation experiments with artificial data we find that a ground truth of parametric dependencies on artificial regressors can be recovered. The results indicate that the calculation of Ks can be a useful addition to standard methods of quantifying the brain's functional architecture.
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Affiliation(s)
- Lena G. Bauer
- Research Network Data Science, University of Vienna, Vienna, Austria
| | - Fabian Hirsch
- Departement of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
- TUMNIC, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Corey Jones
- Departement of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
- TUMNIC, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Matthew Hollander
- Departement of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
- TUMNIC, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Philipp Grohs
- Research Network Data Science, University of Vienna, Vienna, Austria
- Faculty of Mathematics, University of Vienna, Vienna, Austria
| | - Amit Anand
- Center for Behavioral Health, Cleveland Clinic, Cleveland, OH, United States
| | - Claudia Plant
- Research Network Data Science, University of Vienna, Vienna, Austria
- Faculty of Computer Science, University of Vienna, Vienna, Austria
| | - Afra Wohlschläger
- Departement of Neuroradiology, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
- TUMNIC, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
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