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Luppi AI, Gellersen HM, Liu ZQ, Peattie ARD, Manktelow AE, Adapa R, Owen AM, Naci L, Menon DK, Dimitriadis SI, Stamatakis EA. Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics. Nat Commun 2024; 15:4745. [PMID: 38834553 PMCID: PMC11150439 DOI: 10.1038/s41467-024-48781-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 05/10/2024] [Indexed: 06/06/2024] Open
Abstract
Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK.
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- St John's College, University of Cambridge, Cambridge, UK.
- Montreal Neurological Institute, McGill University, Montreal, Canada.
| | - Helena M Gellersen
- German Center for Neurodegenerative Diseases, Magdeburg, Germany
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Zhen-Qi Liu
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Alexander R D Peattie
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Anne E Manktelow
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Ram Adapa
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Adrian M Owen
- Department of Psychology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
- Department of Physiology and Pharmacology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - Stavros I Dimitriadis
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain
- Institut de Neurociències, University of Barcelona, Barcelona, Spain
- Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Integrative Neuroimaging Lab, Thessaloniki, Greece
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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Mochalski LN, Friedrich P, Li X, Kröll JP, Eickhoff SB, Weis S. Inter- and intra-subject similarity in network functional connectivity across a full narrative movie. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.14.594107. [PMID: 38798405 PMCID: PMC11118367 DOI: 10.1101/2024.05.14.594107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Naturalistic paradigms, such as watching movies during functional magnetic resonance imaging (fMRI), are thought to prompt the emotional and cognitive processes typically elicited in real life situations. Therefore, naturalistic viewing (NV) holds great potential for studying individual differences. However, in how far NV elicits similarity within and between subjects on a network level, particularly depending on emotions portrayed in movies, is currently unknown. We used the studyforrest dataset to investigate the inter- and intra-subject similarity in network functional connectivity (NFC) of 14 meta-analytically defined networks across a full narrative, audio-visual movie split into 8 consecutive movie segments. We characterized the movie segments by valence and arousal portrayed within the sequences, before utilizing a linear mixed model to analyze which factors explain inter- and intra-subject similarity. Our results showed that the model best explaining inter-subject similarity comprised network, movie segment, valence and a movie segment by valence interaction. Intra-subject similarity was influenced significantly by the same factors and an additional three-way interaction between movie segment, valence and arousal. Overall, inter- and intra-subject similarity in NFC were sensitive to the ongoing narrative and emotions in the movie. Lowest similarity both within and between subjects was seen in the emotional regulation network and networks associated with long-term memory processing, which might be explained by specific features and content of the movie. We conclude that detailed characterization of movie features is crucial for NV research.
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Ciric R, Xu A, Poldrack RA. hyve, a compositional visualisation engine for brain imaging data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.18.590179. [PMID: 38659772 PMCID: PMC11042383 DOI: 10.1101/2024.04.18.590179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Visualisations facilitate the interpretation of geometrically structured data and results. However, heterogeneous geometries-such as volumes, surfaces, and networks-have traditionally mandated different software approaches. We introduce hyve, a Python library that uses a compositional functional framework to enable parametric implementation of custom visualisations for different brain geometries. Under this framework, users compose a reusable visualisation protocol from geometric primitives for representing data geometries, input primitives for common data formats and research objectives, and output primitives for producing interactive displays or configurable snapshots. hyve also writes documentation for user-constructed protocols, automates serial production of multiple visualisations, and includes an API for semantically organising an editable multi-panel figure. Through the seamless composition of input, output, and geometric primitives, hyve supports creating visualisations for a range of neuroimaging research objectives.
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Affiliation(s)
- Rastko Ciric
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Anna Xu
- Department of Psychology, Stanford University, Stanford, CA, USA
- Stanford Data Science, Stanford University, Stanford, CA, USA
| | - Russell A. Poldrack
- Department of Psychology, Stanford University, Stanford, CA, USA
- Stanford Data Science, Stanford University, Stanford, CA, USA
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Lindquist MA. Sliding windows analysis can undo the effects of preprocessing when applied to fMRI data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.06.561221. [PMID: 37873165 PMCID: PMC10592634 DOI: 10.1101/2023.10.06.561221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Resting-state fMRI (rs-fMRI) data is used to study the intrinsic functional connectivity (FC) in the human brain. In the past decade, interest has focused on studying the temporal dynamics of FC on short timescales, ranging from seconds to minutes. These studies of time-varying FC (TVFC) have enabled the classification of whole-brain dynamic FC profiles into distinct "brain states", defined as recurring whole-brain connectivity profiles reliably observed across subjects and sessions. The analysis of rs-fMRI data is complicated by the fact that the measured BOLD signal consists of changes induced by neuronal activation, as well as non-neuronal nuisance fluctuations that should be removed prior to further analysis. Thus, the data undergoes significant preprocesing prior to analysis. In previous work [24], we illustrated the potential pitfalls involved with using modular preprocessing pipelines, showing how later preprocessing steps can reintroduce correlation with signal previously removed from the data. Here we show that the problem runs deeper, and that certain statistical analysis techniques can potentially interact with preprocessing and reintroduce correlations with previously removed signal. One such technique is the popular sliding window analysis, used to compute TVFC. In this paper, we discuss the problem both theoretically and empirically in application to test-retest rs-fMRI data. Importantly, we show that we are able to obtain essentially the same brain states and state transitions when analyzing motion induced signal as we do when analyzing the preprocessed but windowed data. Our results cast doubt on whether the estimated brain states obtained using sliding window analysis are neuronal in nature, or simply reflect non-neuronal nuisance signal variation (e.g., motion).
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Wang Q, Qi L, He C, Feng H, Xie C. Age- and gender-related dispersion of brain networks across the lifespan. GeroScience 2024; 46:1303-1318. [PMID: 37542582 PMCID: PMC10828139 DOI: 10.1007/s11357-023-00900-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 07/30/2023] [Indexed: 08/07/2023] Open
Abstract
The effects of age and gender on large-scale resting-state networks (RSNs) reflecting within- and between-network connectivity in the healthy brain remain unclear. This study investigated how age and gender influence the brain network roles and topological properties underlying the ageing process. Ten RSNs were constructed based on 998 participants from the REST-meta-MDD cohort. Multivariate linear regression analysis was used to examine the independent and interactive influences of age and gender on large-scale RSNs and their topological properties. A support vector regression model integrating whole-brain network features was used to predict brain age across the lifespan and cognitive decline in an Alzheimer's disease spectrum (ADS) sample. Differential effects of age and gender on brain network roles were demonstrated across the lifespan. Specifically, cingulo-opercular, auditory, and visual (VIS) networks showed more incohesive features reflected by decreased intra-network connectivity with ageing. Further, females displayed distinctive brain network trajectory patterns in middle-early age, showing enhanced network connectivity within the fronto-parietal network (FPN) and salience network (SAN) and weakened network connectivity between the FPN-somatomotor, FPN-VIS, and SAN-VIS networks. Age - but not gender - induced widespread decrease in topological properties of brain networks. Importantly, these differential network features predicted brain age and cognitive impairment in the ADS sample. By showing that age and gender exert specific dispersion of dynamic network roles and trajectories across the lifespan, this study has expanded our understanding of age- and gender-related brain changes with ageing. Moreover, the findings may be useful for detecting early-stage dementia.
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Affiliation(s)
- Qing Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Lingyu Qi
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Cancan He
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Haixia Feng
- Department of Nursing, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Chunming Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China.
- Institute of Neuropsychiatry, Affiliated ZhongDa Hospital, Southeast University, Nanjing, Jiangsu, 210009, China.
- The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu, 210096, China.
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Liu M, He J, Liu D, Hou M, Ma Y. Bibliometric and visualized analysis of dynamic balance and brain function using web of science and CiteSpace from 1995 to 2022. Heliyon 2024; 10:e24300. [PMID: 38293478 PMCID: PMC10824782 DOI: 10.1016/j.heliyon.2024.e24300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 01/05/2024] [Accepted: 01/05/2024] [Indexed: 02/01/2024] Open
Abstract
Purpose This study aims to explore the dynamic balance of human beings and investigate the relationship between functional structure as well as functional connectivity. Through a comprehensive bibliometric and visual analysis of the research literature from 1995 to 2022, we quantitatively display the development of the dynamic balance and brain structure as well as functional connection. Our objective is to present new trends and frontiers in the study of dynamic balance and brain function through bibliometrics software, providing valuable insights for future research in this domain. Methods The literature on dynamic balance, brain structure and functional connectivity between 1995 and 2022 was retrieved from the Web of Science database. We employed CiteSpace software to analyze various aspects, including the year of publication, journal, authors, keywords, institutions, countries, and references. Based on the analysis results, a co-reference map was generated to visually observe research hotspots and knowledge structures. Results A total of 1533 records were retrieved during the survey period (1995-2022), with a gradually increase in the number of annual publications. Notably, the data suggests a notable increase in publications between 2020 and 2021. The number of publications increased by 20 % from 2020 to 2021. The journal "Proceedings of the National Academy of Sciences (PNAS)" emerged as the most prolific journal. Among the cited authors, Deco and Gustavo ranked at the top. Key research terms in this field include "neural network", "functional connectivity", "dynamic", "model" and "brain". Particularly, the keyword "neural network" exhibited the strongest growth. The analysis of keywords cluster revealed the top 10 clusters of research themes. Oxford University stood out as the most productive institution, while the United States held the greatest influence with the highest number of publications and centrality. The reference cluster analysis further demonstrated the top 10 clusters in the literature. Conclusion Through the use of CiteSpace software, this study performed a comprehensive bibliometric and visual analysis of the Web of Science research literature on human dynamic balance and brain structural as well as functional connectivity over the past few decades. This may help researchers identify new perspectives on potential collaborators as well as collaborating institutions, hot topics, and research frontiers in the research field. The results provided an intuitive displayed overview of research trends, hotspots and frontiers in this field, facilitating a general understanding of its progression. Through unremitting efforts, it provides valuable guidance and reference for future research work.
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Affiliation(s)
- Mengjiao Liu
- Research Academy of Grand Health, Faculty of Sports Sciences, Ningbo University, Ningbo 315211, China
| | - Jian He
- Research Academy of Grand Health, Faculty of Sports Sciences, Ningbo University, Ningbo 315211, China
| | - Dongwei Liu
- School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou 310018, China
| | - Meijin Hou
- National Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou 350108, China
- Key Laboratory of Orthopaedics and Traumatology of Traditional Chinese Medicine and Rehabilitation, Fujian University of Traditional Chinese Medicine, Fujian, China
| | - Ye Ma
- Research Academy of Grand Health, Faculty of Sports Sciences, Ningbo University, Ningbo 315211, China
- National Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou 350108, China
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Luo L, Liao Y, Jia F, Ning G, Liu J, Li X, Chen X, Ma X, He X, Fu C, Cai X, Qu H. Altered dynamic functional and effective connectivity in drug-naive children with Tourette syndrome. Transl Psychiatry 2024; 14:48. [PMID: 38253543 PMCID: PMC10803732 DOI: 10.1038/s41398-024-02779-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 01/08/2024] [Accepted: 01/12/2024] [Indexed: 01/24/2024] Open
Abstract
Tourette syndrome (TS) is a developmental neuropsychiatric disorder characterized by repetitive, stereotyped, involuntary tics, the neurological basis of which remains unclear. Although traditional resting-state MRI (rfMRI) studies have identified abnormal static functional connectivity (FC) in patients with TS, dynamic FC (dFC) remains relatively unexplored. The rfMRI data of 54 children with TS and 46 typically developing children (TDC) were analyzed using group independent component analysis to obtain independent components (ICs), and a sliding-window approach to generate dFC matrices. All dFC matrices were clustered into two reoccurring states, the state transition metrics were obtained. We conducted Granger causality and nodal topological analyses to further investigate the brain regions that may play the most important roles in driving whole-brain switching between different states. We found that children with TS spent more time in state 2 (PFDR < 0.001), a state characterized by strong connectivity between ICs, and switched more quickly between states (PFDR = 0.025) than TDC. The default mode network (DMN) may play an important role in abnormal state transitions because the FC that changed the most between the two states was between the DMN and other networks. Additionally, the DMN had increased degree centrality, efficiency and altered causal influence on other networks. Certain alterations related to executive function (r = -0.309, P < 0.05) and tic symptom ratings (r = 0.282; 0.413, P < 0.05) may represent important aspects of the pathophysiology of TS. These findings facilitate our understanding of the neural basis for the clinical presentation of TS.
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Affiliation(s)
- Lekai Luo
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, 610021, Sichuan, PR China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, 610021, Sichuan, PR China
| | - Yi Liao
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, 610021, Sichuan, PR China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, 610021, Sichuan, PR China
| | - Fenglin Jia
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, 610021, Sichuan, PR China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, 610021, Sichuan, PR China
| | - Gang Ning
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, 610021, Sichuan, PR China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, 610021, Sichuan, PR China
| | - Jing Liu
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, 610021, Sichuan, PR China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, 610021, Sichuan, PR China
| | - Xuesheng Li
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, 610021, Sichuan, PR China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, 610021, Sichuan, PR China
| | - Xijian Chen
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, 610021, Sichuan, PR China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, 610021, Sichuan, PR China
| | - Xinmao Ma
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, 610021, Sichuan, PR China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, 610021, Sichuan, PR China
| | - Xuejia He
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, 610021, Sichuan, PR China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, 610021, Sichuan, PR China
| | - Chuan Fu
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, 610021, Sichuan, PR China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, 610021, Sichuan, PR China
| | - Xiaotang Cai
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, 610021, Sichuan, PR China.
- Department of Rehabilitation, West China Second University Hospital, Chengdu, 610021, Sichuan, PR China.
| | - Haibo Qu
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, 610021, Sichuan, PR China.
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, 610021, Sichuan, PR China.
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Chen R, Singh M, Braver TS, Ching S. Dynamical models reveal anatomically reliable attractor landscapes embedded in resting state brain networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.15.575745. [PMID: 38293124 PMCID: PMC10827065 DOI: 10.1101/2024.01.15.575745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Analyses of functional connectivity (FC) in resting-state brain networks (RSNs) have generated many insights into cognition. However, the mechanistic underpinnings of FC and RSNs are still not well-understood. It remains debated whether resting state activity is best characterized as noise-driven fluctuations around a single stable state, or instead, as a nonlinear dynamical system with nontrivial attractors embedded in the RSNs. Here, we provide evidence for the latter, by constructing whole-brain dynamical systems models from individual resting-state fMRI (rfMRI) recordings, using the Mesoscale Individualized NeuroDynamic (MINDy) platform. The MINDy models consist of hundreds of neural masses representing brain parcels, connected by fully trainable, individualized weights. We found that our models manifested a diverse taxonomy of nontrivial attractor landscapes including multiple equilibria and limit cycles. However, when projected into anatomical space, these attractors mapped onto a limited set of canonical RSNs, including the default mode network (DMN) and frontoparietal control network (FPN), which were reliable at the individual level. Further, by creating convex combinations of models, bifurcations were induced that recapitulated the full spectrum of dynamics found via fitting. These findings suggest that the resting brain traverses a diverse set of dynamics, which generates several distinct but anatomically overlapping attractor landscapes. Treating rfMRI as a unimodal stationary process (i.e., conventional FC) may miss critical attractor properties and structure within the resting brain. Instead, these may be better captured through neural dynamical modeling and analytic approaches. The results provide new insights into the generative mechanisms and intrinsic spatiotemporal organization of brain networks.
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Affiliation(s)
- Ruiqi Chen
- Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, MO 63108
| | - Matthew Singh
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO 63108
| | - Todd S. Braver
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO 63108
| | - ShiNung Ching
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO 63108
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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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Wang Q, He C, Wang Z, Fan D, Zhang Z, Xie C. Connectomics-based resting-state functional network alterations predict suicidality in major depressive disorder. Transl Psychiatry 2023; 13:365. [PMID: 38012129 PMCID: PMC10682490 DOI: 10.1038/s41398-023-02655-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 10/30/2023] [Accepted: 11/07/2023] [Indexed: 11/29/2023] Open
Abstract
Suicidal behavior is a major concern for patients who suffer from major depressive disorder (MDD). However, dynamic alterations and dysfunction of resting-state networks (RSNs) in MDD patients with suicidality have remained unclear. Thus, we investigated whether subjects with different severity of suicidal ideation and suicidal behavior may have different disturbances in brain RSNs and whether these changes could be used as the diagnostic biomarkers to discriminate MDD with or without suicidal ideation and suicidal behavior. Then a multicenter, cross-sectional study of 528 MDD patients with or without suicidality and 998 healthy controls was performed. We defined the probability of dying by the suicide of the suicidality components as a 'suicidality gradient'. We constructed ten RSNs, including default mode (DMN), subcortical (SUB), ventral attention (VAN), and visual network (VIS). The network connections of RSNs were analyzed among MDD patients with different suicidality gradients and healthy controls using ANCOVA, chi-squared tests, and network-based statistical analysis. And support vector machine (SVM) model was designed to distinguish patients with mild-to-severe suicidal ideation, and suicidal behavior. We found the following abnormalities with increasing suicidality gradient in MDD patients: within-network connectivity values initially increased and then decreased, and one-versus-other network values decreased first and then increased. Besides, within- and between-network connectivity values of the various suicidality gradients are mainly negatively correlated with HAMD anxiety and positively correlated with weight. We found that VIS and DMN-VIS values were affected by age (p < 0.05), cingulo-opercular network, and SUB-VAN values were statistically influenced by sex (p < 0.05). Furthermore, the SVM model could distinguish MDD patients with different suicidality gradients (AUC range, 0.73-0.99). In conclusion, we have identified that disrupted brain connections were present in MDD patients with different suicidality gradient. These findings provided useful information about the pathophysiological mechanisms of MDD patients with suicidality.
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Affiliation(s)
- Qing Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Cancan He
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Zan Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
- Institute of Neuropsychiatry, Affiliated ZhongDa Hospital, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Dandan Fan
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Zhijun Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
- Institute of Neuropsychiatry, Affiliated ZhongDa Hospital, Southeast University, Nanjing, Jiangsu, 210009, China
- The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Chunming Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China.
- Institute of Neuropsychiatry, Affiliated ZhongDa Hospital, Southeast University, Nanjing, Jiangsu, 210009, China.
- The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu, 210009, China.
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11
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Luckett PH, Park KY, Lee JJ, Lenze EJ, Wetherell JL, Eyler L, Snyder AZ, Ances BM, Shimony JS, Leuthardt EC. Data-efficient resting-state functional magnetic resonance imaging brain mapping with deep learning. J Neurosurg 2023; 139:1258-1269. [PMID: 37060318 PMCID: PMC10576012 DOI: 10.3171/2023.3.jns2314] [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: 01/04/2023] [Accepted: 03/01/2023] [Indexed: 04/16/2023]
Abstract
OBJECTIVE Resting-state functional MRI (RS-fMRI) enables the mapping of function within the brain and is emerging as an efficient tool for the presurgical evaluation of eloquent cortex. Models capable of reliable and precise mapping of resting-state networks (RSNs) with a reduced scanning time would lead to improved patient comfort while reducing the cost per scan. The aims of the present study were to develop a deep 3D convolutional neural network (3DCNN) capable of voxel-wise mapping of language (LAN) and motor (MOT) RSNs with minimal quantities of RS-fMRI data. METHODS Imaging data were gathered from multiple ongoing studies at Washington University School of Medicine and other thoroughly characterized, publicly available data sets. All study participants (n = 2252 healthy adults) were cognitively screened and completed structural neuroimaging and RS-fMRI. Random permutations of RS-fMRI regions of interest were used to train a 3DCNN. After training, model inferences were compared using varying amounts of RS-fMRI data from the control data set as well as 5 patients with glioblastoma multiforme. RESULTS The trained model achieved 96% out-of-sample validation accuracy on data encompassing a large age range collected on multiple scanner types and varying sequence parameters. Testing on out-of-sample control data showed 97.9% similarity between results generated using either 50 or 200 RS-fMRI time points, corresponding to approximately 2.5 and 10 minutes, respectively (96.9% LAN, 96.3% MOT true-positive rate). In evaluating data from patients with brain tumors, the 3DCNN was able to accurately map LAN and MOT networks despite structural and functional alterations. CONCLUSIONS Functional maps produced by the 3DCNN can inform surgical planning in patients with brain tumors in a time-efficient manner. The authors present a highly efficient method for presurgical functional mapping and thus improved functional preservation in patients with brain tumors.
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Affiliation(s)
- Patrick H. Luckett
- Division of Neurotechnology, Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri
| | - Ki Yun Park
- Division of Neurotechnology, Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri
- Department of Neuroscience, Washington University School of Medicine, St. Louis, Missouri
| | - John J. Lee
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Eric J Lenze
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Julie L Wetherell
- Mental Health Impact Unit 3, VA San Diego Healthcare System, San Diego, California
- Department of Psychiatry, University of California, San Diego, California
| | - Lisa Eyler
- Department of Psychiatry, University of California, San Diego, California
| | - Abraham Z. Snyder
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri
| | - Beau M. Ances
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri
| | - Joshua S. Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Eric C. Leuthardt
- Division of Neurotechnology, Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri
- Department of Neuroscience, Washington University School of Medicine, St. Louis, Missouri
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO
- Center for Innovation in Neuroscience and Technology, Division of Neurotechnology, Washington University School of Medicine, St. Louis, MO
- Brain Laser Center, Washington University School of Medicine, St. Louis, Missouri
- National Center for Adaptive Neurotechnologies
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12
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Lee K, Ji JL, Fonteneau C, Berkovitch L, Rahmati M, Pan L, Repovš G, Krystal JH, Murray JD, Anticevic A. Human brain state dynamics reflect individual neuro-phenotypes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.18.557763. [PMID: 37790400 PMCID: PMC10542143 DOI: 10.1101/2023.09.18.557763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Neural activity and behavior manifest state and trait dynamics, as well as variation within and between individuals. However, the mapping of state-trait neural variation to behavior is not well understood. To address this gap, we quantify moment-to-moment changes in brain-wide co-activation patterns derived from resting-state functional magnetic resonance imaging. In healthy young adults, we identify reproducible spatio-temporal features of co-activation patterns at the single subject level. We demonstrate that a joint analysis of state-trait neural variations and feature reduction reveal general motifs of individual differences, encompassing state-specific and general neural features that exhibit day-to-day variability. The principal neural variations co-vary with the principal variations of behavioral phenotypes, highlighting cognitive function, emotion regulation, alcohol and substance use. Person-specific probability of occupying a particular co-activation pattern is reproducible and associated with neural and behavioral features. This combined analysis of state-trait variations holds promise for developing reproducible neuroimaging markers of individual life functional outcome.
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Affiliation(s)
- Kangjoo Lee
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Clara Fonteneau
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Lucie Berkovitch
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Saclay CEA Centre, Neurospin, Gif-Sur-Yvette Cedex, France
- Department of Psychiatry, GHU Paris Psychiatrie et Neurosciences, Service Hospitalo-Universitaire, Paris, France
- Université Paris Cité, 15 Rue de l'École de Médecine, F-75006 Paris, France
| | - Masih Rahmati
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Lining Pan
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Grega Repovš
- Department of Psychology, University of Ljubljana, Ljubljana, Slovenia
| | - John H Krystal
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - John D Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA
- Department of Physics, Yale University, New Haven, CT, USA
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychology, Yale University School of Medicine, New Haven, CT, USA
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13
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Uddin LQ, Betzel RF, Cohen JR, Damoiseaux JS, De Brigard F, Eickhoff SB, Fornito A, Gratton C, Gordon EM, Laird AR, Larson-Prior L, McIntosh AR, Nickerson LD, Pessoa L, Pinho AL, Poldrack RA, Razi A, Sadaghiani S, Shine JM, Yendiki A, Yeo BTT, Spreng RN. Controversies and progress on standardization of large-scale brain network nomenclature. Netw Neurosci 2023; 7:864-905. [PMID: 37781138 PMCID: PMC10473266 DOI: 10.1162/netn_a_00323] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 05/10/2023] [Indexed: 10/03/2023] Open
Abstract
Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.
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Affiliation(s)
- Lucina Q. Uddin
- Department of Psychiatry and Biobehavioral Sciences and Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Jessica R. Cohen
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, USA
| | - Jessica S. Damoiseaux
- Institute of Gerontology and Department of Psychology, Wayne State University, Detroit, MI, USA
| | | | - Simon B. Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Evan M. Gordon
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA
| | - Angela R. Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | - Linda Larson-Prior
- Deptartment of Psychiatry and Department of Neurobiology and Developmental Sciences, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - A. Randal McIntosh
- Institute for Neuroscience and Neurotechnology, Simon Fraser University, Vancouver, BC, Canada
| | | | - Luiz Pessoa
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Ana Luísa Pinho
- Brain and Mind Institute, Western University, London, Ontario, Canada
| | | | - Adeel Razi
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
| | - Sepideh Sadaghiani
- Department of Psychology, University of Illinois, Urbana Champaign, IL, USA
| | - James M. Shine
- Brain and Mind Center, University of Sydney, Sydney, Australia
| | - Anastasia Yendiki
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - B. T. Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - R. Nathan Spreng
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
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14
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Greenwell S, Faskowitz J, Pritschet L, Santander T, Jacobs EG, Betzel RF. High-amplitude network co-fluctuations linked to variation in hormone concentrations over the menstrual cycle. Netw Neurosci 2023; 7:1181-1205. [PMID: 37781152 PMCID: PMC10473261 DOI: 10.1162/netn_a_00307] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 12/20/2022] [Indexed: 10/03/2023] Open
Abstract
Many studies have shown that the human endocrine system modulates brain function, reporting associations between fluctuations in hormone concentrations and brain connectivity. However, how hormonal fluctuations impact fast changes in brain network organization over short timescales remains unknown. Here, we leverage a recently proposed framework for modeling co-fluctuations between the activity of pairs of brain regions at a framewise timescale. In previous studies we showed that time points corresponding to high-amplitude co-fluctuations disproportionately contributed to the time-averaged functional connectivity pattern and that these co-fluctuation patterns could be clustered into a low-dimensional set of recurring "states." Here, we assessed the relationship between these network states and quotidian variation in hormone concentrations. Specifically, we were interested in whether the frequency with which network states occurred was related to hormone concentration. We addressed this question using a dense-sampling dataset (N = 1 brain). In this dataset, a single individual was sampled over the course of two endocrine states: a natural menstrual cycle and while the subject underwent selective progesterone suppression via oral hormonal contraceptives. During each cycle, the subject underwent 30 daily resting-state fMRI scans and blood draws. Our analysis of the imaging data revealed two repeating network states. We found that the frequency with which state 1 occurred in scan sessions was significantly correlated with follicle-stimulating and luteinizing hormone concentrations. We also constructed representative networks for each scan session using only "event frames"-those time points when an event was determined to have occurred. We found that the weights of specific subsets of functional connections were robustly correlated with fluctuations in the concentration of not only luteinizing and follicle-stimulating hormones, but also progesterone and estradiol.
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Affiliation(s)
- Sarah Greenwell
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Program in Neurosciences, Indiana University, Bloomington, IN, USA
| | - Laura Pritschet
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Tyler Santander
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Emily G. Jacobs
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, USA
- Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Program in Neurosciences, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
- Network Science Institute, Indiana University, Bloomington, IN, USA
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15
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Betzel RF, Cutts SA, Tanner J, Greenwell SA, Varley T, Faskowitz J, Sporns O. Hierarchical organization of spontaneous co-fluctuations in densely sampled individuals using fMRI. Netw Neurosci 2023; 7:926-949. [PMID: 37781150 PMCID: PMC10473297 DOI: 10.1162/netn_a_00321] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 05/03/2023] [Indexed: 10/03/2023] Open
Abstract
Edge time series decompose functional connectivity into its framewise contributions. Previous studies have focused on characterizing the properties of high-amplitude frames (time points when the global co-fluctuation amplitude takes on its largest value), including their cluster structure. Less is known about middle- and low-amplitude co-fluctuations (peaks in co-fluctuation time series but of lower amplitude). Here, we directly address those questions, using data from two dense-sampling studies: the MyConnectome project and Midnight Scan Club. We develop a hierarchical clustering algorithm to group peak co-fluctuations of all magnitudes into nested and multiscale clusters based on their pairwise concordance. At a coarse scale, we find evidence of three large clusters that, collectively, engage virtually all canonical brain systems. At finer scales, however, each cluster is dissolved, giving way to increasingly refined patterns of co-fluctuations involving specific sets of brain systems. We also find an increase in global co-fluctuation magnitude with hierarchical scale. Finally, we comment on the amount of data needed to estimate co-fluctuation pattern clusters and implications for brain-behavior studies. Collectively, the findings reported here fill several gaps in current knowledge concerning the heterogeneity and richness of co-fluctuation patterns as estimated with edge time series while providing some practical guidance for future studies.
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Affiliation(s)
- Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
- Network Science Institute, Indiana University, Bloomington, IN, USA
| | - Sarah A. Cutts
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
| | - Jacob Tanner
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Sarah A. Greenwell
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Thomas Varley
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
- Network Science Institute, Indiana University, Bloomington, IN, USA
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16
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Lynch CJ, Elbau I, Ng T, Ayaz A, Zhu S, Manfredi N, Johnson M, Wolk D, Power JD, Gordon EM, Kay K, Aloysi A, Moia S, Caballero-Gaudes C, Victoria LW, Solomonov N, Goldwaser E, Zebley B, Grosenick L, Downar J, Vila-Rodriguez F, Daskalakis ZJ, Blumberger DM, Williams N, Gunning FM, Liston C. Expansion of a frontostriatal salience network in individuals with depression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.09.551651. [PMID: 37645792 PMCID: PMC10461904 DOI: 10.1101/2023.08.09.551651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Hundreds of neuroimaging studies spanning two decades have revealed differences in brain structure and functional connectivity in depression, but with modest effect sizes, complicating efforts to derive mechanistic pathophysiologic insights or develop biomarkers. 1 Furthermore, although depression is a fundamentally episodic condition, few neuroimaging studies have taken a longitudinal approach, which is critical for understanding cause and effect and delineating mechanisms that drive mood state transitions over time. The emerging field of precision functional mapping using densely-sampled longitudinal neuroimaging data has revealed unexpected, functionally meaningful individual differences in brain network topology in healthy individuals, 2-5 but these approaches have never been applied to individuals with depression. Here, using precision functional mapping techniques and 11 datasets comprising n=187 repeatedly sampled individuals and >21,000 minutes of fMRI data, we show that the frontostriatal salience network is expanded two-fold in most individuals with depression. This effect was replicable in multiple samples, including large-scale, group-average data (N=1,231 subjects), and caused primarily by network border shifts affecting specific functional systems, with three distinct modes of encroachment occurring in different individuals. Salience network expansion was unexpectedly stable over time, unaffected by changes in mood state, and detectable in children before the subsequent onset of depressive symptoms in adolescence. Longitudinal analyses of individuals scanned up to 62 times over 1.5 years identified connectivity changes in specific frontostriatal circuits that tracked fluctuations in specific symptom domains and predicted future anhedonia symptoms before they emerged. Together, these findings identify a stable trait-like brain network topology that may confer risk for depression and mood-state dependent connectivity changes in frontostriatal circuits that predict the emergence and remission of depressive symptoms over time.
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17
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Ibáñez-Berganza M, Lucibello C, Santucci F, Gili T, Gabrielli A. Noise cleaning the precision matrix of short time series. Phys Rev E 2023; 108:024313. [PMID: 37723818 DOI: 10.1103/physreve.108.024313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 08/02/2023] [Indexed: 09/20/2023]
Abstract
We present a comparison between various algorithms of inference of covariance and precision matrices in small data sets of real vectors of the typical length and dimension of human brain activity time series retrieved by functional magnetic resonance imaging (fMRI). Assuming a Gaussian model underlying the neural activity, the problem consists of denoising the empirically observed matrices to obtain a better estimator of the (unknown) true precision and covariance matrices. We consider several standard noise-cleaning algorithms and compare them on two types of data sets. The first type consists of synthetic time series sampled from a generative Gaussian model of which we can vary the fraction of dimensions per sample q and the strength of off-diagonal correlations. The second type consists of time series of fMRI brain activity of human subjects at rest. The reliability of each algorithm is assessed in terms of test-set likelihood and, in the case of synthetic data, of the distance from the true precision matrix. We observe that the so-called optimal rotationally invariant estimator, based on random matrix theory, leads to a significantly lower distance from the true precision matrix in synthetic data and higher test likelihood in natural fMRI data. We propose a variant of the optimal rotationally invariant estimator in which one of its parameters is optimzed by cross-validation. In the severe undersampling regime (large q) typical of fMRI series, it outperforms all the other estimators. We furthermore propose a simple algorithm based on an iterative likelihood gradient ascent, leading to very accurate estimations in weakly correlated synthetic data sets.
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Affiliation(s)
- Miguel Ibáñez-Berganza
- Networks Unit, IMT School for Advanced Studies Lucca, Piazza San Francesco 19, 50100 Lucca, Italy and Istituto Italiano di Tecnologia. Largo Barsanti e Matteucci, 53, 80125 Napoli, Italy
| | - Carlo Lucibello
- AI Lab, Institute for Data Science and Analytics, Bocconi University, 20136 Milano, Italy
| | - Francesca Santucci
- Networks Unit, IMT School for Advanced Studies Lucca, Piazza San Francesco 19, 50100 Lucca, Italy
| | - Tommaso Gili
- Networks Unit, IMT School for Advanced Studies Lucca, Piazza San Francesco 19, 50100 Lucca, Italy
| | - Andrea Gabrielli
- Dipartimento di Ingegneria Civile, Informatica e delle Tecnologie Aeronautiche, Universitá degli Studi Roma Tre, Via Vito Volterra 62, 00146 Rome, Italy and Centro Ricerche Enrico Fermi, Via Panisperna 89a, 00184 Rome, Italy
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18
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Wheelock MD, Strain JF, Mansfield P, Tu JC, Tanenbaum A, Preische O, Chhatwal JP, Cash DM, Cruchaga C, Fagan AM, Fox NC, Graff-Radford NR, Hassenstab J, Jack CR, Karch CM, Levin J, McDade EM, Perrin RJ, Schofield PR, Xiong C, Morris JC, Bateman RJ, Jucker M, Benzinger TLS, Ances BM, Eggebrecht AT, Gordon BA, Allegri R, Araki A, Barthelemy N, Bateman R, Bechara J, Benzinger T, Berman S, Bodge C, Brandon S, Brooks W, Brosch J, Buck J, Buckles V, Carter K, Cash D, Cash L, Chen C, Chhatwal J, Chrem P, Chua J, Chui H, Cruchaga C, Day GS, De La Cruz C, Denner D, Diffenbacher A, Dincer A, Donahue T, Douglas J, Duong D, Egido N, Esposito B, Fagan A, Farlow M, Feldman B, Fitzpatrick C, Flores S, Fox N, Franklin E, Friedrichsen N, Fujii H, Gardener S, Ghetti B, Goate A, Goldberg S, Goldman J, Gonzalez A, Gordon B, Gräber-Sultan S, Graff-Radford N, Graham M, Gray J, Gremminger E, Grilo M, Groves A, Haass C, Häsler L, Hassenstab J, Hellm C, Herries E, Hoechst-Swisher L, Hofmann A, Holtzman D, Hornbeck R, Igor Y, Ihara R, Ikeuchi T, Ikonomovic S, Ishii K, Jack C, Jerome G, Johnson E, Jucker M, Karch C, Käser S, Kasuga K, Keefe S, Klunk W, Koeppe R, Koudelis D, Kuder-Buletta E, Laske C, Lee JH, Levey A, Levin J, Li Y, Lopez O, Marsh J, Martinez R, Martins R, Mason NS, Masters C, Mawuenyega K, McCullough A, McDade E, Mejia A, Morenas-Rodriguez E, Mori H, Morris J, Mountz J, Mummery C, Nadkami N, Nagamatsu A, Neimeyer K, Niimi Y, Noble J, Norton J, Nuscher B, O'Connor A, Obermüller U, Patira R, Perrin R, Ping L, Preische O, Renton A, Ringman J, Salloway S, Sanchez-Valle R, Schofield P, Senda M, Seyfried N, Shady K, Shimada H, Sigurdson W, Smith J, Smith L, Snitz B, Sohrabi H, Stephens S, Taddei K, Thompson S, Vöglein J, Wang P, Wang Q, Weamer E, Xiong C, Xu J, Xu X. Brain network decoupling with increased serum neurofilament and reduced cognitive function in Alzheimer's disease. Brain 2023; 146:2928-2943. [PMID: 36625756 PMCID: PMC10316768 DOI: 10.1093/brain/awac498] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 11/21/2022] [Accepted: 12/12/2022] [Indexed: 01/11/2023] Open
Abstract
Neurofilament light chain, a putative measure of neuronal damage, is measurable in blood and CSF and is predictive of cognitive function in individuals with Alzheimer's disease. There has been limited prior work linking neurofilament light and functional connectivity, and no prior work has investigated neurofilament light associations with functional connectivity in autosomal dominant Alzheimer's disease. Here, we assessed relationships between blood neurofilament light, cognition, and functional connectivity in a cross-sectional sample of 106 autosomal dominant Alzheimer's disease mutation carriers and 76 non-carriers. We employed an innovative network-level enrichment analysis approach to assess connectome-wide associations with neurofilament light. Neurofilament light was positively correlated with deterioration of functional connectivity within the default mode network and negatively correlated with connectivity between default mode network and executive control networks, including the cingulo-opercular, salience, and dorsal attention networks. Further, reduced connectivity within the default mode network and between the default mode network and executive control networks was associated with reduced cognitive function. Hierarchical regression analysis revealed that neurofilament levels and functional connectivity within the default mode network and between the default mode network and the dorsal attention network explained significant variance in cognitive composite scores when controlling for age, sex, and education. A mediation analysis demonstrated that functional connectivity within the default mode network and between the default mode network and dorsal attention network partially mediated the relationship between blood neurofilament light levels and cognitive function. Our novel results indicate that blood estimates of neurofilament levels correspond to direct measurements of brain dysfunction, shedding new light on the underlying biological processes of Alzheimer's disease. Further, we demonstrate how variation within key brain systems can partially mediate the negative effects of heightened total serum neurofilament levels, suggesting potential regions for targeted interventions. Finally, our results lend further evidence that low-cost and minimally invasive blood measurements of neurofilament may be a useful marker of brain functional connectivity and cognitive decline in Alzheimer's disease.
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Affiliation(s)
- Muriah D Wheelock
- Department of Radiology, Washington University in St. Louis, MO, USA
| | - Jeremy F Strain
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO, USA
| | | | - Jiaxin Cindy Tu
- Department of Radiology, Washington University in St. Louis, MO, USA
| | - Aaron Tanenbaum
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO, USA
| | - Oliver Preische
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Jasmeer P Chhatwal
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - David M Cash
- Dementia Research Center, UCL Queen Square, London, UK.,UK Dementia Research Institute, College London, London, UK
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University in St. Louis, MO, USA
| | - Anne M Fagan
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO, USA
| | - Nick C Fox
- Dementia Research Center, UCL Queen Square, London, UK.,UK Dementia Research Institute, College London, London, UK
| | | | - Jason Hassenstab
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO, USA
| | | | - Celeste M Karch
- Department of Psychiatry, Washington University in St. Louis, MO, USA
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany.,German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Eric M McDade
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO, USA
| | - Richard J Perrin
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO, USA.,Department of Pathology & Immunology, Washington University in St. Louis, MO, USA
| | - Peter R Schofield
- Neuroscience Research Australia, Sydney, NSW, Australia.,School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Chengjie Xiong
- Division of Biostatistics, Washington University in St. Louis, MO, USA
| | - John C Morris
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO, USA
| | - Randal J Bateman
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO, USA
| | - Mathias Jucker
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Tammie L S Benzinger
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO, USA
| | - Beau M Ances
- Department of Neurology, Washington University in Saint Louis, St. Louis, MO, USA
| | - Adam T Eggebrecht
- Department of Radiology, Washington University in St. Louis, MO, USA
| | - Brian A Gordon
- Department of Radiology, Washington University in St. Louis, MO, USA
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19
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Zhang M, Chowdhury S, Saggar M. Temporal Mapper: Transition networks in simulated and real neural dynamics. Netw Neurosci 2023; 7:431-460. [PMID: 37397880 PMCID: PMC10312258 DOI: 10.1162/netn_a_00301] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 12/07/2022] [Indexed: 07/26/2023] Open
Abstract
Characterizing large-scale dynamic organization of the brain relies on both data-driven and mechanistic modeling, which demands a low versus high level of prior knowledge and assumptions about how constituents of the brain interact. However, the conceptual translation between the two is not straightforward. The present work aims to provide a bridge between data-driven and mechanistic modeling. We conceptualize brain dynamics as a complex landscape that is continuously modulated by internal and external changes. The modulation can induce transitions between one stable brain state (attractor) to another. Here, we provide a novel method-Temporal Mapper-built upon established tools from the field of topological data analysis to retrieve the network of attractor transitions from time series data alone. For theoretical validation, we use a biophysical network model to induce transitions in a controlled manner, which provides simulated time series equipped with a ground-truth attractor transition network. Our approach reconstructs the ground-truth transition network from simulated time series data better than existing time-varying approaches. For empirical relevance, we apply our approach to fMRI data gathered during a continuous multitask experiment. We found that occupancy of the high-degree nodes and cycles of the transition network was significantly associated with subjects' behavioral performance. Taken together, we provide an important first step toward integrating data-driven and mechanistic modeling of brain dynamics.
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Affiliation(s)
- Mengsen Zhang
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Department of Psychiatry, University of North Carolina at Chapel Hill, NC, USA
| | - Samir Chowdhury
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Manish Saggar
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
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20
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Yu Y, Gratton C, Smith DM. From correlation to communication: Disentangling hidden factors from functional connectivity changes. Netw Neurosci 2023; 7:411-430. [PMID: 37397894 PMCID: PMC10312287 DOI: 10.1162/netn_a_00290] [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: 03/18/2022] [Accepted: 11/02/2022] [Indexed: 01/11/2024] Open
Abstract
While correlations in the BOLD fMRI signal are widely used to capture functional connectivity (FC) and its changes across contexts, its interpretation is often ambiguous. The entanglement of multiple factors including local coupling of two neighbors and nonlocal inputs from the rest of the network (affecting one or both regions) limits the scope of the conclusions that can be drawn from correlation measures alone. Here we present a method of estimating the contribution of nonlocal network input to FC changes across different contexts. To disentangle the effect of task-induced coupling change from the network input change, we propose a new metric, "communication change," utilizing BOLD signal correlation and variance. With a combination of simulation and empirical analysis, we demonstrate that (1) input from the rest of the network accounts for a moderate but significant amount of task-induced FC change and (2) the proposed "communication change" is a promising candidate for tracking the local coupling in task context-induced change. Additionally, when compared to FC change across three different tasks, communication change can better discriminate specific task types. Taken together, this novel index of local coupling may have many applications in improving our understanding of local and widespread interactions across large-scale functional networks.
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Affiliation(s)
- Yuhua Yu
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Department of Neurology, Northwestern University, Evanston, IL, USA
- Department of Psychology, Florida State University, Tallahassee, FL, USA
| | - Derek M. Smith
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Department of Neurology, Division of Cognitive Neurology/Neuropsychology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
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21
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Shi Y, Shen Z, Zeng W, Luo S, Zhou L, Wang N. A schizophrenia study based on multi-frequency dynamic functional connectivity analysis of fMRI. Front Hum Neurosci 2023; 17:1164685. [PMID: 37250690 PMCID: PMC10213427 DOI: 10.3389/fnhum.2023.1164685] [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: 02/13/2023] [Accepted: 04/17/2023] [Indexed: 05/31/2023] Open
Abstract
At present, fMRI studies mainly focus on the entire low-frequency band (0. 01-0.08 Hz). However, the neuronal activity is dynamic, and different frequency bands may contain different information. Therefore, a novel multi-frequency-based dynamic functional connectivity (dFC) analysis method was proposed in this study, which was then applied to a schizophrenia study. First, three frequency bands (Conventional: 0.01-0.08 Hz, Slow-5: 0.0111-0.0302 Hz, and Slow-4: 0.0302-0.0820 Hz) were obtained using Fast Fourier Transform. Next, the fractional amplitude of low-frequency fluctuations was used to identify abnormal regions of interest (ROIs) of schizophrenia, and dFC among these abnormal ROIs was implemented by the sliding time window method at four window-widths. Finally, recursive feature elimination was employed to select features, and the support vector machine was applied for the classification of patients with schizophrenia and healthy controls. The experimental results showed that the proposed multi-frequency method (Combined: Slow-5 and Slow-4) had a better classification performance compared with the conventional method at shorter sliding window-widths. In conclusion, our results revealed that the dFCs among the abnormal ROIs varied at different frequency bands and the efficiency of combining multiple features from different frequency bands can improve classification performance. Therefore, it would be a promising approach for identifying brain alterations in schizophrenia.
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Affiliation(s)
- Yuhu Shi
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Zehao Shen
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Weiming Zeng
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Sizhe Luo
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Lili Zhou
- Surgery Department of Tongji University Affiliated Yangpu Central Hospital, Shanghai, China
| | - Nizhuan Wang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
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22
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Whiteside DJ, Malpetti M, Jones PS, Ghosh BCP, Coyle‐Gilchrist I, van Swieten JC, Seelaar H, Jiskoot L, Borroni B, Sanchez‐Valle R, Moreno F, Laforce R, Graff C, Synofzik M, Galimberti D, Masellis M, Tartaglia MC, Finger E, Vandenberghe R, de Mendonça A, Tagliavini F, Butler CR, Santana I, Ber IL, Gerhard A, Ducharme S, Levin J, Danek A, Otto M, Sorbi S, Pasquier F, Bouzigues A, Russell LL, Rohrer JD, Rowe JB, Rittman T. Temporal dynamics predict symptom onset and cognitive decline in familial frontotemporal dementia. Alzheimers Dement 2023; 19:1947-1962. [PMID: 36377606 PMCID: PMC7614527 DOI: 10.1002/alz.12824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 09/13/2022] [Accepted: 09/19/2022] [Indexed: 11/16/2022]
Abstract
INTRODUCTION We tested whether changes in functional networks predict cognitive decline and conversion from the presymptomatic prodrome to symptomatic disease in familial frontotemporal dementia (FTD). METHODS For hypothesis generation, 36 participants with behavioral variant FTD (bvFTD) and 34 controls were recruited from one site. For hypothesis testing, we studied 198 symptomatic FTD mutation carriers, 341 presymptomatic mutation carriers, and 329 family members without mutations. We compared functional network dynamics between groups, with clinical severity and with longitudinal clinical progression. RESULTS We identified a characteristic pattern of dynamic network changes in FTD, which correlated with neuropsychological impairment. Among presymptomatic mutation carriers, this pattern of network dynamics was found to a greater extent in those who subsequently converted to the symptomatic phase. Baseline network dynamic changes predicted future cognitive decline in symptomatic participants and older presymptomatic participants. DISCUSSION Dynamic network abnormalities in FTD predict cognitive decline and symptomatic conversion. HIGHLIGHTS We investigated brain network predictors of dementia symptom onset Frontotemporal dementia results in characteristic dynamic network patterns Alterations in network dynamics are associated with neuropsychological impairment Network dynamic changes predict symptomatic conversion in presymptomatic carriers Network dynamic changes are associated with longitudinal cognitive decline.
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Affiliation(s)
- David J. Whiteside
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeCambridgeshireUK
- Cambridge University Hospitals NHS Foundation TrustCambridgeUK
| | - Maura Malpetti
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeCambridgeshireUK
| | - P. Simon Jones
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeCambridgeshireUK
| | - Boyd C. P. Ghosh
- Wessex Neurological CentreUniversity Hospital SouthamptonSouthamptonUK
| | | | | | - Harro Seelaar
- Department of NeurologyErasmus Medical CentreRotterdamNetherlands
| | - Lize Jiskoot
- Department of NeurologyErasmus Medical CentreRotterdamNetherlands
| | - Barbara Borroni
- Centre for Neurodegenerative DisordersDepartment of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
| | - Raquel Sanchez‐Valle
- Alzheimer's Disease and Other Cognitive Disorders UnitNeurology Service, Hospital ClínicInstitut d'Investigacións Biomèdiques August Pi I SunyerUniversity of BarcelonaBarcelonaSpain
| | - Fermin Moreno
- Cognitive Disorders UnitDepartment of NeurologyDonostia University HospitalSan SebastianGipuzkoaSpain
- Neuroscience AreaBiodonostia Health Research InstituteSan SebastianGipuzkoaSpain
| | - Robert Laforce
- CHU de Québec, and Faculté de MédecineDépartement des Sciences NeurologiquesClinique Interdisciplinaire de Mémoire, Université LavalQCCanada
| | - Caroline Graff
- Center for Alzheimer ResearchDivision of NeurogeriatricsDepartment of Neurobiology, Care Sciences and SocietyBioclinicum, Karolinska InstitutetSolnaSweden
- Unit for Hereditary Dementias, Theme AgingKarolinska University HospitalSolnaSweden
| | - Matthis Synofzik
- Department of Neurodegenerative DiseasesHertie‐Institute for Clinical Brain ResearchTübingenGermany
- Center of NeurologyUniversity of TübingenTübingenGermany
| | - Daniela Galimberti
- Fondazione IRCCS Ospedale PoliclinicoMilanItaly
- Department of Biomedical, Surgical and Dental SciencesUniversity of MilanMilanItaly
| | - Mario Masellis
- Sunnybrook Health Sciences CentreSunnybrook Research InstituteUniversity of TorontoTorontoCanada
| | | | - Elizabeth Finger
- Department of Clinical Neurological SciencesUniversity of Western OntarioLondonOntarioCanada
| | - Rik Vandenberghe
- Laboratory for Cognitive NeurologyDepartment of NeurosciencesKU LeuvenLeuvenBelgium
- Neurology ServiceUniversity Hospitals LeuvenBelgium
- Leuven Brain InstituteKU LeuvenLeuvenBelgium
| | | | | | - Chris R. Butler
- Nuffield Department of Clinical NeurosciencesMedical Sciences DivisionUniversity of OxfordOxfordUK
- Department of Brain SciencesImperial College LondonLondonUK
| | - Isabel Santana
- University Hospital of Coimbra (HUC)Neurology Service, Faculty of MedicineUniversity of CoimbraCoimbraPortugal
- Center for Neuroscience and Cell BiologyFaculty of MedicineUniversity of CoimbraCoimbraPortugal
| | - Isabelle Le Ber
- Paris Brain Institute – Institut du Cerveau – ICMInserm U1127, CNRS UMR 7225, AP‐HP ‐ Hôpital Pitié‐SalpêtrièreSorbonne UniversitéParisFrance
- Centre de référence des démences rares ou précoces, IM2ADépartement de NeurologieAP‐HP ‐ Hôpital Pitié‐SalpêtrièreParisFrance
- Département de NeurologieAP‐HP ‐ Hôpital Pitié‐SalpêtrièreParisFrance
| | - Alexander Gerhard
- Division of Neuroscience and Experimental PsychologyWolfson Molecular Imaging CentreUniversity of ManchesterManchesterUK
- Departments of Geriatric Medicine and Nuclear MedicineUniversity of Duisburg‐ EssenDuisburgGermany
| | - Simon Ducharme
- Department of PsychiatryMcGill University Health CentreMcGill UniversityMontrealQuébecCanada
- Department of Neurology & NeurosurgeryMcConnell Brain Imaging CentreMontreal Neurological InstituteMcGill UniversityMontrealCanada
| | - Johannes Levin
- Neurologische KlinikLudwig‐Maximilians‐Universität MünchenMunichGermany
- German Center for Neurodegenerative Diseases (DZNE)MunichGermany
- Munich Cluster of Systems NeurologyMunichGermany
| | - Adrian Danek
- Neurologische KlinikLudwig‐Maximilians‐Universität MünchenMunichGermany
| | - Markus Otto
- Department of NeurologyUniversity of UlmUlmGermany
| | - Sandro Sorbi
- Department of NeurofarbaUniversity of FlorenceFlorenceItaly
- IRCCS Fondazione Don Carlo GnocchiFlorenceItaly
| | - Florence Pasquier
- Univ LilleLilleFrance
- Inserm 1172LilleFrance
- CHU, CNR‐MAJ, Labex DistalzLiCEND LilleLilleFrance
| | - Arabella Bouzigues
- Department of Neurodegenerative DiseaseDementia Research Centre UCL Institute of NeurologyQueen SquareLondonUK
| | - Lucy L. Russell
- Department of Neurodegenerative DiseaseDementia Research Centre UCL Institute of NeurologyQueen SquareLondonUK
| | - Jonathan D. Rohrer
- Department of Neurodegenerative DiseaseDementia Research Centre UCL Institute of NeurologyQueen SquareLondonUK
| | - James B. Rowe
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeCambridgeshireUK
- Cambridge University Hospitals NHS Foundation TrustCambridgeUK
- MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
| | - Timothy Rittman
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeCambridgeshireUK
- Cambridge University Hospitals NHS Foundation TrustCambridgeUK
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23
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Ding Z, Ding Z, Chen Y, Lv D, Li T, Shang T, Ma J, Zhan C, Yang X, Xiao J, Sun Z, Wang N, Guo W, Li C, Yu Z, Li P. Decreased gray matter volume and dynamic functional alterations in medicine-free obsessive-compulsive disorder. BMC Psychiatry 2023; 23:289. [PMID: 37098479 PMCID: PMC10131325 DOI: 10.1186/s12888-023-04740-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 03/31/2023] [Indexed: 04/27/2023] Open
Abstract
BACKGROUND Previous studies discovered the presence of abnormal structures and functions in the brain regions of patients with obsessive-compulsive disorder (OCD). Nevertheless, whether structural changes in brain regions are coupled with alterations in dynamic functional connectivity (dFC) at rest in medicine-free patients with OCD remains vague. METHODS Three-dimensional T1-weighed magnetic resonance imaging (MRI) and resting-state functional MRI were performed on 50 medicine-free OCD and 50 healthy controls (HCs). Firstly, the differences in gray matter volume (GMV) between OCD and HCs were compared. Then, brain regions with aberrant GMV were used as seeds for dFC analysis. The relationship of altered GMV and dFC with clinical parameters in OCD was explored using partial correlation analysis. Finally, support vector machine was applied to examine whether altered multimodal imaging data might be adopted to distinguish OCD from HCs. RESULTS Our findings indicated that GMV in the left superior temporal gyrus (STG) and right supplementary motor area (SMA) was reduced in OCD, and the dFC between the left STG and the left cerebellum Crus I and left thalamus, and between the right SMA and right dorsolateral prefrontal cortex (DLPFC) and left precuneus was decreased at rest in OCD. The brain regions both with altered GMV and dFC values could discriminate OCD from HCs with the accuracy of 0.85, sensitivity of 0.90 and specificity of 0.80. CONCLUSION The decreased gray matter structure coupling with dynamic function in the left STG and right SMA at rest may be crucial in the pathophysiology of OCD. TRIAL REGISTRATION Study on the mechanism of brain network in obsessive-compulsive disorder with multi-model magnetic resonance imaging (registration date: 08/11/2017; registration number: ChiCTR-COC-17,013,301).
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Affiliation(s)
- Zhenning Ding
- Medical Technology Department, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China
| | - Zhipeng Ding
- Medical Technology Department, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China
| | - Yunhui Chen
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China
| | - Dan Lv
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China
| | - Tong Li
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China
| | - Tinghuizi Shang
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China
| | - Jidong Ma
- Department of Psychiatry, Baiyupao Psychiatric Hospital of Harbin, Harbin, Heilongjiang, 150050, China
| | - Chuang Zhan
- Department of Psychiatry, Baiyupao Psychiatric Hospital of Harbin, Harbin, Heilongjiang, 150050, China
| | - Xu Yang
- Medical Technology Department, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China
| | - Jian Xiao
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China
| | - Zhenghai Sun
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China
| | - Na Wang
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China
| | - Wenbin Guo
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, China
| | - Chengchong Li
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China.
| | - Zengyan Yu
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China.
| | - Ping Li
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China.
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24
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Nakuci J, Wasylyshyn N, Cieslak M, Elliott JC, Bansal K, Giesbrecht B, Grafton ST, Vettel JM, Garcia JO, Muldoon SF. Within-subject reproducibility varies in multi-modal, longitudinal brain networks. Sci Rep 2023; 13:6699. [PMID: 37095180 PMCID: PMC10126005 DOI: 10.1038/s41598-023-33441-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 04/12/2023] [Indexed: 04/26/2023] Open
Abstract
Network neuroscience provides important insights into brain function by analyzing complex networks constructed from diffusion Magnetic Resonance Imaging (dMRI), functional MRI (fMRI) and Electro/Magnetoencephalography (E/MEG) data. However, in order to ensure that results are reproducible, we need a better understanding of within- and between-subject variability over long periods of time. Here, we analyze a longitudinal, 8 session, multi-modal (dMRI, and simultaneous EEG-fMRI), and multiple task imaging data set. We first confirm that across all modalities, within-subject reproducibility is higher than between-subject reproducibility. We see high variability in the reproducibility of individual connections, but observe that in EEG-derived networks, during both rest and task, alpha-band connectivity is consistently more reproducible than connectivity in other frequency bands. Structural networks show a higher reliability than functional networks across network statistics, but synchronizability and eigenvector centrality are consistently less reliable than other network measures across all modalities. Finally, we find that structural dMRI networks outperform functional networks in their ability to identify individuals using a fingerprinting analysis. Our results highlight that functional networks likely reflect state-dependent variability not present in structural networks, and that the type of analysis should depend on whether or not one wants to take into account state-dependent fluctuations in connectivity.
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Affiliation(s)
- Johan Nakuci
- Neuroscience Program, University at Buffalo, SUNY, Buffalo, NY, 14260, USA.
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, 14260, USA.
| | - Nick Wasylyshyn
- U.S. CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, 21005, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Matthew Cieslak
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
| | - James C Elliott
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
| | - Kanika Bansal
- U.S. CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, 21005, USA
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Barry Giesbrecht
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
- Institute for Collaborative Biotechnologies, University of California, Santa Barbara, CA, 93106, USA
| | - Scott T Grafton
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
- Institute for Collaborative Biotechnologies, University of California, Santa Barbara, CA, 93106, USA
| | - Jean M Vettel
- U.S. CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, 21005, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, 93106, USA
| | - Javier O Garcia
- U.S. CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, MD, 21005, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sarah F Muldoon
- Neuroscience Program, University at Buffalo, SUNY, Buffalo, NY, 14260, USA.
- Department of Mathematics and CDSE Program, University at Buffalo, SUNY, Buffalo, NY, 14260, USA.
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25
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Long Y, Ouyang X, Yan C, Wu Z, Huang X, Pu W, Cao H, Liu Z, Palaniyappan L. Evaluating test-retest reliability and sex-/age-related effects on temporal clustering coefficient of dynamic functional brain networks. Hum Brain Mapp 2023; 44:2191-2208. [PMID: 36637216 PMCID: PMC10028647 DOI: 10.1002/hbm.26202] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 11/25/2022] [Accepted: 01/01/2023] [Indexed: 01/14/2023] Open
Abstract
The multilayer dynamic network model has been proposed as an effective method to understand the brain function. In particular, derived from the definition of clustering coefficient in static networks, the temporal clustering coefficient provides a direct measure of the topological stability of dynamic brain networks and shows potential in predicting altered brain functions. However, test-retest reliability and demographic-related effects on this measure remain to be evaluated. Using a data set from the Human Connectome Project (157 male and 180 female healthy adults; 22-37 years old), the present study investigated: (1) the test-retest reliability of temporal clustering coefficient across four repeated resting-state functional magnetic resonance imaging scans as measured by intraclass correlation coefficient (ICC); and (2) sex- and age-related effects on temporal clustering coefficient. The results showed that (1) the temporal clustering coefficient had overall moderate test-retest reliability (ICC > 0.40 over a wide range of densities) at both global and subnetwork levels, (2) female subjects showed significantly higher temporal clustering coefficient than males at both global and subnetwork levels, particularly within the default-mode and subcortical regions, and (3) temporal clustering coefficient of the subcortical subnetwork was positively correlated with age in young adults. The results of sex effects were robustly replicated in an independent REST-meta-MDD data set, while the results of age effects were not. Our findings suggest that the temporal clustering coefficient is a relatively reliable and reproducible approach for identifying individual differences in brain function, and provide evidence for demographically related effects on the human brain dynamic connectomes.
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Affiliation(s)
- Yicheng Long
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Xuan Ouyang
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Chaogan Yan
- CAS Key Laboratory of Behavioral Science, Institute of PsychologyChinese Academy of SciencesBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression Research, Institute of PsychologyChinese Academy of SciencesBeijingChina
| | - Zhipeng Wu
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Xiaojun Huang
- Department of PsychiatryJiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical CollegeNanchangChina
| | - Weidan Pu
- Medical Psychological InstituteThe Second Xiangya Hospital, Central South UniversityChangshaChina
| | - Hengyi Cao
- Center for Psychiatric NeuroscienceFeinstein Institute for Medical ResearchManhassetNew YorkUSA
- Division of Psychiatry ResearchZucker Hillside HospitalGlen OaksNew YorkUSA
| | - Zhening Liu
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Lena Palaniyappan
- Department of PsychiatryUniversity of Western OntarioLondonOntarioCanada
- Robarts Research InstituteUniversity of Western OntarioLondonOntarioCanada
- Lawson Health Research InstituteLondonOntarioCanada
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26
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Peng X, Liu Q, Hubbard CS, Wang D, Zhu W, Fox MD, Liu H. Robust dynamic brain coactivation states estimated in individuals. SCIENCE ADVANCES 2023; 9:eabq8566. [PMID: 36652524 PMCID: PMC9848428 DOI: 10.1126/sciadv.abq8566] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 12/14/2022] [Indexed: 06/01/2023]
Abstract
A confluence of evidence indicates that brain functional connectivity is not static but rather dynamic. Capturing transient network interactions in the individual brain requires a technology that offers sufficient within-subject reliability. Here, we introduce an individualized network-based dynamic analysis technique and demonstrate that it is reliable in detecting subject-specific brain states during both resting state and a cognitively challenging language task. We evaluate the extent to which brain states show hemispheric asymmetries and how various phenotypic factors such as handedness and gender might influence network dynamics, discovering a right-lateralized brain state that occurred more frequently in men than in women and more frequently in right-handed versus left-handed individuals. Longitudinal brain state changes were also shown in 42 patients with subcortical stroke over 6 months. Our approach could quantify subject-specific dynamic brain states and has potential for use in both basic and clinical neuroscience research.
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Affiliation(s)
- Xiaolong Peng
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Liu
- Changping Laboratory, Beijing, China
| | - Catherine S. Hubbard
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
| | - Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Michael D. Fox
- Center for Brain Circuit Therapeutics, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Hesheng Liu
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
- Changping Laboratory, Beijing, China
- Biomedical Pioneering Innovation Center, Peking University, Beijing, China
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Noro Y, Li R, Matsui T, Jimura K. A method for reconstruction of interpretable brain networks from transient synchronization in resting-state BOLD fluctuations. Front Neuroinform 2023; 16:960607. [PMID: 36713290 PMCID: PMC9878402 DOI: 10.3389/fninf.2022.960607] [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: 06/03/2022] [Accepted: 12/22/2022] [Indexed: 01/13/2023] Open
Abstract
Resting-state (rs) fMRI has been widely used to examine brain-wide large-scale spatiotemporal architectures, known as resting-state networks (RSNs). Recent studies have focused on the temporally evolving characteristics of RSNs, but it is unclear what temporal characteristics are reflected in the networks. To address this issue, we devised a novel method for voxel-based visualization of spatiotemporal characteristics of rs-fMRI with a time scale of tens of seconds. We first extracted clusters of dominant activity-patterns using a region-of-interest approach and then used these temporal patterns of the clusters to obtain voxel-based activation patterns related to the clusters. We found that activation patterns related to the clusters temporally evolved with a characteristic temporal structure and showed mutual temporal alternations over minutes. The voxel-based representation allowed the decoding of activation patterns of the clusters in rs-fMRI using a meta-analysis of functional activations. The activation patterns of the clusters were correlated with behavioral measures. Taken together, our analysis highlights a novel approach to examine brain activity dynamics during rest.
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Affiliation(s)
- Yusuke Noro
- Department of Biosciences and Informatics, Keio University, Yokohama, Japan
| | - Ruixiang Li
- Department of Physiology, The University of Tokyo School of Medicine, Tokyo, Japan
| | - Teppei Matsui
- Department of Biology, Okayama University, Okayama, Japan,PRESTO, Japan Science and Technology Agency, Tokyo, Japan,Teppei Matsui ✉
| | - Koji Jimura
- Department of Informatics, Gunma University, Maebashi, Japan,*Correspondence: Koji Jimura ✉
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28
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Luckett PH, Lee JJ, Park KY, Raut RV, Meeker KL, Gordon EM, Snyder AZ, Ances BM, Leuthardt EC, Shimony JS. Resting state network mapping in individuals using deep learning. Front Neurol 2023; 13:1055437. [PMID: 36712434 PMCID: PMC9878609 DOI: 10.3389/fneur.2022.1055437] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 12/28/2022] [Indexed: 01/14/2023] Open
Abstract
Introduction Resting state functional MRI (RS-fMRI) is currently used in numerous clinical and research settings. The localization of resting state networks (RSNs) has been utilized in applications ranging from group analysis of neurodegenerative diseases to individual network mapping for pre-surgical planning of tumor resections. Reproducibility of these results has been shown to require a substantial amount of high-quality data, which is not often available in clinical or research settings. Methods In this work, we report voxelwise mapping of a standard set of RSNs using a novel deep 3D convolutional neural network (3DCNN). The 3DCNN was trained on publicly available functional MRI data acquired in n = 2010 healthy participants. After training, maps that represent the probability of a voxel belonging to a particular RSN were generated for each participant, and then used to calculate mean and standard deviation (STD) probability maps, which are made publicly available. Further, we compared our results to previously published resting state and task-based functional mappings. Results Our results indicate this method can be applied in individual subjects and is highly resistant to both noisy data and fewer RS-fMRI time points than are typically acquired. Further, our results show core regions within each network that exhibit high average probability and low STD. Discussion The 3DCNN algorithm can generate individual RSN localization maps, which are necessary for clinical applications. The similarity between 3DCNN mapping results and task-based fMRI responses supports the association of specific functional tasks with RSNs.
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Affiliation(s)
- Patrick H. Luckett
- Division of Neurotechnology, Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, United States
| | - John J. Lee
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
| | - Ki Yun Park
- Division of Neurotechnology, Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, United States
| | - Ryan V. Raut
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, United States
- MindScope Program, Allen Institute, Seattle, WA, United States
| | - Karin L. Meeker
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Evan M. Gordon
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Abraham Z. Snyder
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Beau M. Ances
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Eric C. Leuthardt
- Division of Neurotechnology, Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, United States
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, United States
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO, United States
- Center for Innovation in Neuroscience and Technology, Division of Neurotechnology, Washington University School of Medicine, St. Louis, MO, United States
- Brain Laser Center, Washington University School of Medicine, St. Louis, MO, United States
- National Center for Adaptive Neurotechnologies, Albany, NY, United States
| | - Joshua S. Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
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29
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Si X, Zhou Y, Li S, Zhang X, Han S, Xiang S, Ming D. Brain-Computer Interfaces in Visualized Medicine. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1199:127-153. [PMID: 37460730 DOI: 10.1007/978-981-32-9902-3_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
The brain-computer interface (BCI), also known as a brain-machine interface (BMI), has attracted extensive attention in biomedical applications. More importantly, BCI technologies have substantially revolutionized early predictions, diagnostic techniques, and rehabilitation strategies addressing acute diseases because of BCI's innovations and clinical translations. Therefore, in this chapter, a comprehensive description of the basic concepts of BCI will be exhibited, and various visualization techniques employed in BCI's medical applications will be discussed.
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Affiliation(s)
- Xiaopeng Si
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China.
| | - Yu Zhou
- College of Medical Technology and Engineering, Henan University of Science and Technology, Henan, China
| | - Sicheng Li
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Xingjian Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Shunli Han
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Shaoxin Xiang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
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30
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Dimitriadis SI. Assessing the Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher's Choice Paths. Neuroinformatics 2023; 21:71-88. [PMID: 36372844 DOI: 10.1007/s12021-022-09610-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/05/2022] [Indexed: 11/15/2022]
Abstract
There is a growing interest in the neuroscience community on the advantages of multilayer functional brain networks. Researchers usually treated different frequencies separately at distinct functional brain networks. However, there is strong evidence that these networks share complementary information while their interdependencies could reveal novel findings. For this purpose, neuroscientists adopt multilayer networks, which can be described mathematically as an extension of trivial single-layer networks. Multilayer networks have become popular in neuroscience due to their advantage to integrate different sources of information. Here, Ι will focus on the multi-frequency multilayer functional connectivity analysis on resting-state fMRI (rs-fMRI) recordings. However, constructing a multilayer network depends on selecting multiple pre-processing steps that can affect the final network topology. Here, I analyzed the rs-fMRI dataset from a single human performing scanning over a period of 18 months (84 scans in total), and the rs-fMRI dataset containing 25 subjects with 3 repeat scans. I focused on assessing the reproducibility of multi-frequency multilayer topologies exploring the effect of two filtering methods for extracting frequencies from BOLD activity, three connectivity estimators, with or without a topological filtering scheme, and two spatial scales. Finally, I untangled specific combinations of researchers' choices that yield consistently brain networks with repeatable topologies, giving me the chance to recommend best practices over consistent topologies.
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Affiliation(s)
- Stavros I Dimitriadis
- Department of Clinical Psychology and Psychobiology, Faculty of Psychology, University of Barcelona, Passeig de la Vall d'Hebron, 171, 08035, Barcelona, Spain.
- Institut de Neurociències, University of Barcelona, Campus Mundet, Edifici de PonentPasseig de la Vall d'Hebron, 171, 08035, Barcelona, Spain.
- Integrative Neuroimaging Lab, 55133, Thessaloniki, Greece.
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Wales, CF24 4HQ, Cardiff, UK.
- Neuroinformatics Group, School of Psychology, College of Biomedical and Life Sciences, Cardiff University Brain Research Imaging Centre (CUBRIC), CF24 4HQ, Cardiff, Wales, UK.
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, CF24 4HQ, Wales, UK.
- Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, CF24 4HQ, Cardiff, Wales, UK.
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, CF24 4HQ, Wales, UK.
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31
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Abdulaev SK, Tarumov DA, Shamrey VK, Trufanov AG, Puchkov NA, Markin KV, Prochik YE. [Functional impairments in large-scale brain projects in opioid addiction]. Zh Nevrol Psikhiatr Im S S Korsakova 2023; 123:165-170. [PMID: 37315257 DOI: 10.17116/jnevro2023123051165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
OBJECTIVE To assess the functional state of large-scale resting networks of the brain in patients with opioid intoxication. MATERIAL AND METHODS Thirty-one male subjects, aged 27.4+5.1 years, were studied. Resting state functional MRI was performed in 12 patients with heroin intoxication aged 29.1+5.9 years. The control group consisted of 16 healthy volunteers without bad habits aged 26.2+4.2 years. RESULTS In the group of opioids intoxication, there is a decrease in the functional activity of the salience network with the executive control network and the default mode network of the brain (p<0.05) compared to the control group. A positive correlation of functional connections is found between the anterior cingulate cortex and the medial prefrontal cortex (T=2.74; p=0.041), which is not recorded in the control group. The functional connections between the default mode network and executive control are more highly represented in opioid intoxication in comparison with the control group (medial prefrontal cortex - left posterior parietal cortex T=7.5; p=0.001; medial prefrontal cortex - right posterior parietal cortex T=3.71; p=0.014; posterior cingulate cortex - left posterior parietal cortex T=6.15; p=0.002; posterior cingulate cortex - right posterior parietal cortex T=3.25; p=0.023; posterior cingulate cortex - right dorsolateral prefrontal cortex T=2.83; p=0.037). CONCLUSION The results indicate that functional connections in large-scale resting networks are disrupted during opioid intoxication, which indicates a disturbance of the normal functional architectonics of the brain.
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Affiliation(s)
- Sh K Abdulaev
- Kirov Military Medical Academy, St. Petersburg, Russia
| | - D A Tarumov
- Kirov Military Medical Academy, St. Petersburg, Russia
| | - V K Shamrey
- Kirov Military Medical Academy, St. Petersburg, Russia
| | - A G Trufanov
- Kirov Military Medical Academy, St. Petersburg, Russia
| | - N A Puchkov
- Kirov Military Medical Academy, St. Petersburg, Russia
| | - K V Markin
- Kirov Military Medical Academy, St. Petersburg, Russia
| | - Ya E Prochik
- Kirov Military Medical Academy, St. Petersburg, Russia
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32
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Vazquez-Trejo V, Nardos B, Schlaggar BL, Fair DA, Miranda-Dominguez O. Use of connectotyping on task functional MRI data reveals dynamic network level cross talking during task performance. Front Neurosci 2022; 16:951907. [DOI: 10.3389/fnins.2022.951907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Task-based functional MRI (fMRI) has greatly improved understanding of brain functioning, enabling the identification of brain areas associated with specific cognitive operations. Traditional analyses are limited to associating activation patterns in particular regions with specific cognitive operation, largely ignoring regional cross-talk or dynamic connectivity, which we propose is crucial for characterization of brain function in the context of task fMRI. We use connectotyping, which efficiently models functional brain connectivity to reveal the progression of temporal brain connectivity patterns in task fMRI. Connectotyping was employed on data from twenty-four participants (12 male, mean age 24.8 years, 2.57 std. dev) who performed a widely spaced event-related fMRI word vs. pseudoword decision task, where stimuli were presented every 20 s. After filtering for movement, we ended up with 15 participants that completed each trial and had enough usable data for our analyses. Connectivity matrices were calculated per participant across time for each stimuli type. A Repeated Measures ANOVA applied on the connectotypes was used to characterize differences across time for words and pseudowords. Our group level analyses found significantly different dynamic connectivity patterns during word vs. pseudoword processing between the Fronto-Parietal and Cingulo-Parietal Systems, areas involved in cognitive task control, memory retrieval, and semantic processing. Our findings support the presence of dynamic changes in functional connectivity during task execution and that such changes can be characterized using connectotyping but not with traditional Pearson’s correlations.
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33
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Stoliker D, Egan GF, Friston KJ, Razi A. Neural Mechanisms and Psychology of Psychedelic Ego Dissolution. Pharmacol Rev 2022; 74:876-917. [PMID: 36786290 DOI: 10.1124/pharmrev.121.000508] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 06/26/2022] [Accepted: 06/29/2022] [Indexed: 11/22/2022] Open
Abstract
Neuroimaging studies of psychedelics have advanced our understanding of hierarchical brain organization and the mechanisms underlying their subjective and therapeutic effects. The primary mechanism of action of classic psychedelics is binding to serotonergic 5-HT2A receptors. Agonist activity at these receptors leads to neuromodulatory changes in synaptic efficacy that can have a profound effect on hierarchical message-passing in the brain. Here, we review the cognitive and neuroimaging evidence for the effects of psychedelics: in particular, their influence on selfhood and subject-object boundaries-known as ego dissolution-surmised to underwrite their subjective and therapeutic effects. Agonism of 5-HT2A receptors, located at the apex of the cortical hierarchy, may have a particularly powerful effect on sentience and consciousness. These effects can endure well after the pharmacological half-life, suggesting that psychedelics may have effects on neural plasticity that may play a role in their therapeutic efficacy. Psychologically, this may be accompanied by a disarming of ego resistance that increases the repertoire of perceptual hypotheses and affords alternate pathways for thought and behavior, including those that undergird selfhood. We consider the interaction between serotonergic neuromodulation and sentience through the lens of hierarchical predictive coding, which speaks to the value of psychedelics in understanding how we make sense of the world and specific predictions about effective connectivity in cortical hierarchies that can be tested using functional neuroimaging. SIGNIFICANCE STATEMENT: Classic psychedelics bind to serotonergic 5-HT2A receptors. Their agonist activity at these receptors leads to neuromodulatory changes in synaptic efficacy, resulting in a profound effect on information processing in the brain. Here, we synthesize an abundance of brain imaging research with pharmacological and psychological interpretations informed by the framework of predictive coding. Moreover, predictive coding is suggested to offer more sophisticated interpretations of neuroimaging findings by bridging the role between the 5-HT2A receptors and large-scale brain networks.
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Affiliation(s)
- Devon Stoliker
- Turner Institute for Brain and Mental Health (D.S., G.F.E., A.R.) and Monash Biomedical Imaging (G.F.E., A.R.), Monash University, Clayton, Victoria, Australia; Wellcome Centre for Human Neuroimaging, UCL, London, United Kingdom (K.J.F., A.R.); and CIFAR Azrieli Global Scholar, CIFAR, Toronto, Canada (A.R.)
| | - Gary F Egan
- Turner Institute for Brain and Mental Health (D.S., G.F.E., A.R.) and Monash Biomedical Imaging (G.F.E., A.R.), Monash University, Clayton, Victoria, Australia; Wellcome Centre for Human Neuroimaging, UCL, London, United Kingdom (K.J.F., A.R.); and CIFAR Azrieli Global Scholar, CIFAR, Toronto, Canada (A.R.)
| | - Karl J Friston
- Turner Institute for Brain and Mental Health (D.S., G.F.E., A.R.) and Monash Biomedical Imaging (G.F.E., A.R.), Monash University, Clayton, Victoria, Australia; Wellcome Centre for Human Neuroimaging, UCL, London, United Kingdom (K.J.F., A.R.); and CIFAR Azrieli Global Scholar, CIFAR, Toronto, Canada (A.R.)
| | - Adeel Razi
- Turner Institute for Brain and Mental Health (D.S., G.F.E., A.R.) and Monash Biomedical Imaging (G.F.E., A.R.), Monash University, Clayton, Victoria, Australia; Wellcome Centre for Human Neuroimaging, UCL, London, United Kingdom (K.J.F., A.R.); and CIFAR Azrieli Global Scholar, CIFAR, Toronto, Canada (A.R.)
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Gonzalez-Castillo J, Fernandez IS, Handwerker DA, Bandettini PA. Ultra-slow fMRI fluctuations in the fourth ventricle as a marker of drowsiness. Neuroimage 2022; 259:119424. [PMID: 35781079 PMCID: PMC9377091 DOI: 10.1016/j.neuroimage.2022.119424] [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: 03/30/2022] [Revised: 06/16/2022] [Accepted: 06/29/2022] [Indexed: 10/17/2022] Open
Abstract
Wakefulness levels modulate estimates of functional connectivity (FC), and, if unaccounted for, can become a substantial confound in resting-state fMRI. Unfortunately, wakefulness is rarely monitored due to the need for additional concurrent recordings (e.g., eye tracking, EEG). Recent work has shown that strong fluctuations around 0.05Hz, hypothesized to be CSF inflow, appear in the fourth ventricle (FV) when subjects fall asleep, and that they correlate significantly with the global signal. The analysis of these fluctuations could provide an easy way to evaluate wakefulness in fMRI-only data and improve our understanding of FC during sleep. Here we evaluate this possibility using the 7T resting-state sample from the Human Connectome Project (HCP). Our results replicate the observation that fourth ventricle ultra-slow fluctuations (∼0.05Hz) with inflow-like characteristics (decreasing in intensity for successive slices) are present in scans during which subjects did not comply with instructions to keep their eyes open (i.e., drowsy scans). This is true despite the HCP data not being optimized for the detection of inflow-like effects. In addition, time-locked BOLD fluctuations of the same frequency could be detected in large portions of grey matter with a wide range of temporal delays and contribute in significant ways to our understanding of how FC changes during sleep. First, these ultra-slow fluctuations explain half of the increase in global signal that occurs during descent into sleep. Similarly, global shifts in FC between awake and sleep states are driven by changes in this slow frequency band. Second, they can influence estimates of inter-regional FC. For example, disconnection between frontal and posterior components of the Defulat Mode Network (DMN) typically reported during sleep were only detectable after regression of these ultra-slow fluctuations. Finally, we report that the temporal evolution of the power spectrum of these ultra-slow FV fluctuations can help us reproduce sample-level sleep patterns (e.g., a substantial number of subjects descending into sleep 3 minutes following scanning onset), partially rank scans according to overall drowsiness levels, and predict individual segments of elevated drowsiness (at 60 seconds resolution) with 71% accuracy.
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Affiliation(s)
- Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD.
| | - Isabel S Fernandez
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD
| | - Daniel A Handwerker
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD
| | - Peter A Bandettini
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD; Functional MRI Core, National Institutes of Health, Bethesda, MD
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35
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Signal Variability and Cognitive Function in Older Long-Term Survivors of Breast Cancer with Exposure to Chemotherapy: A Prospective Longitudinal Resting-State fMRI Study. Brain Sci 2022; 12:brainsci12101283. [PMID: 36291217 PMCID: PMC9599386 DOI: 10.3390/brainsci12101283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 09/15/2022] [Accepted: 09/19/2022] [Indexed: 01/07/2023] Open
Abstract
The purpose of this study was to assess the effect of chemotherapy on brain functional resting-state signal variability and cognitive function in older long-term survivors of breast cancer. This prospective longitudinal study enrolled women age ≥ 65 years of age who were breast cancer survivors after exposure to chemotherapy (CH), age-matched survivors not exposed to chemotherapy, and healthy controls. Participants completed resting-state functional brain MRI and neurocognitive testing upon enrollment (timepoint 1, TP1) and again two years later (timepoint 2, TP2). There were 20 participants in each of the three groups at TP1. The CH group showed a significant decrease in SDBOLD (blood-oxygen-level-dependent signal variability in standard deviation) in the right middle occipital gyrus (ΔSDBOLD = -0.0018, p = 0.0085, q (pFDR) = 0.043 at MNI (42, -76, 17)) and right middle temporal gyrus (ΔSDBOLD = -0.0021, p = 0.0006, q (pFDR) = 0.001 at MNI (63, -39, -12)). There were negative correlations between the crystallized composite scores and SDBOLD values at the right inferior occipital gyrus (correlation coefficient r = -0.84, p = 0.001, q (pFDR) = 0.016) and right middle temporal gyrus (r = -0.88, p = 0.000, q (pFDR) = 0.017) for the CH group at TP1. SDBOLD could be a potentially useful neuroimaging marker for older long-term survivors of breast cancer with exposure to chemotherapy.
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36
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Matsui T, Yamashita KI. Static and Dynamic Functional Connectivity Alterations in Alzheimer's Disease and Neuropsychiatric Diseases. Brain Connect 2022. [PMID: 35994384 DOI: 10.1089/brain.2022.0044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
To date, numerous studies have documented various alterations in resting brain activity in Alzheimer's disease (AD) and other neuropsychiatric diseases. In particular, disease-related alterations of functional connectivity (FC) in the resting state networks (RSN) have been documented. Altered FC in RSN is useful not only for interpreting the phenotype of diseases but also for diagnosing the diseases. More recently, several studies proposed the dynamics of resting-brain activity as a useful marker for detecting altered RSNs related to AD and other diseases. In contrast to previous studies, which focused on FC calculated using an entire fMRI scan (static FC), these newer studies focused the on temporal dynamics of FC within the scan (dynamic FC) to provide more sensitive measures to characterize RSNs. However, despite the increasing popularity of dFC, several studies cautioned that the results obtained in commonly used analyses for dFC require careful interpretation. In this mini-review, we review recent studies exploring alterations of static and dynamic functional connectivity in AD and other neuropsychiatric diseases. We then discuss how to utilize and interpret dFC for studying resting brain activity in diseases.
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Affiliation(s)
- Teppei Matsui
- Okayama University - Tsushima Campus, Tsushima-kita 1-1-1, Okayama, Japan, 700-8530;
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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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Moghimi P, Dang AT, Do Q, Netoff TI, Lim KO, Atluri G. Evaluation of functional MRI-based human brain parcellation: a review. J Neurophysiol 2022; 128:197-217. [PMID: 35675446 DOI: 10.1152/jn.00411.2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Brain parcellations play a crucial role in the analysis of brain imaging data sets, as they can significantly affect the outcome of the analysis. In recent years, several novel approaches for constructing MRI-based brain parcellations have been developed with promising results. In the absence of ground truth, several evaluation approaches have been used to evaluate currently available brain parcellations. In this article, we review and critique methods used for evaluating functional brain parcellations constructed using fMRI data sets. We also describe how some of these evaluation methods have been used to estimate the optimal parcellation granularity. We provide a critical discussion of the current approach to the problem of identifying the optimal brain parcellation that is suited for a given neuroimaging study. We argue that the criteria for an optimal brain parcellation must depend on the application the parcellation is intended for. We describe a teleological approach to the evaluation of brain parcellations, where brain parcellations are evaluated in different contexts and optimal brain parcellations for each context are identified separately. We conclude by discussing several directions for further research that would result in improved evaluation strategies.
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Affiliation(s)
- Pantea Moghimi
- Department of Neurobiology, University of Chicago, Chicago, Illinois
| | - Anh The Dang
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
| | - Quan Do
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
| | - Theoden I Netoff
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota
| | - Kelvin O Lim
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
| | - Gowtham Atluri
- Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio
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Kang DW, Wang SM, Um YH, Kim NY, Lee CU, Lim HK. Impact of APOE ε4 Carrier Status on Associations Between Subthreshold, Positive Amyloid-β Deposition, Brain Function, and Cognitive Performance in Cognitively Normal Older Adults: A Prospective Study. Front Aging Neurosci 2022; 14:871323. [PMID: 35677201 PMCID: PMC9168227 DOI: 10.3389/fnagi.2022.871323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 04/12/2022] [Indexed: 12/19/2022] Open
Abstract
BackgroundA growing body of evidence suggests a deteriorating effect of subthreshold amyloid-beta (Aβ) accumulation on cognition before the onset of clinical symptoms of Alzheimer's disease (AD). Despite the association between the Aβ-dependent pathway and the APOE ε4 allele, the impact of this allele on the progression from the subthreshold Aβ deposits to cognitive function impairment is unclear. Furthermore, the comparative analysis of positive Aβ accumulation in the preclinical phase is lacking.ObjectiveThis study aimed to explore the differential effect of the APOE ε4 carrier status on the association between Aβ deposition, resting-state brain function, and cognitive performance in cognitively normal (CN) older adults, depending on the Aβ burden status.MethodsOne hundred and eighty-two older CN adults underwent resting-state functional magnetic resonance imaging, [18F] flutemetamol (FMM) positron emission tomography, a neuropsychological battery, and APOE genotyping. We evaluated the resting-state brain function by measuring the local and remote functional connectivity (FC) and measured the remote FC in the default-mode network (DMN), central-executive network (CEN), and salience network (SN). In addition, the subjects were dichotomized into those with subthreshold and positive Aβ deposits using a neocortical standardized uptake value ratio with the cut-off value of 0.62, which was calculated with respect to the pons.ResultsThe present result showed that APOE ε4 carrier status moderated the relationship between Aβ deposition, local and remote resting-state brain function, and cognitive performance in each CN subthreshold and positive Aβ group. We observed the following: (i) the APOE ε4 carrier status-Aβ deposition and APOE ε4 carrier status-local FC interaction for the executive and memory function; (ii) the APOE ε4 carrier status-regional Aβ accumulation interaction for the local FC; and (iv) the APOE ε4 carrier status-local FC interaction for the remote inter-network FC between the DMN and CEN, contributing higher cognitive performance in the APOE ε4 carrier with higher inter-network FC. Finally, these results were modulated according to Aβ positivity.ConclusionThis study is the first attempt to thoroughly examine the influence of the APOE ε4 carrier status from the subthreshold to positive Aβ accumulation during the preclinical phase.
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Affiliation(s)
- Dong Woo Kang
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Sheng-Min Wang
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Yoo Hyun Um
- Department of Psychiatry, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Nak-Young Kim
- Department of Psychiatry, Keyo Hospital, Uiwang, South Korea
| | - Chang Uk Lee
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Hyun Kook Lim
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
- *Correspondence: Hyun Kook Lim
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Yang H, Zhang H, Meng C, Wohlschläger A, Brandl F, Di X, Wang S, Tian L, Biswal B. Frequency-specific coactivation patterns in resting-state and their alterations in schizophrenia: An fMRI study. Hum Brain Mapp 2022; 43:3792-3808. [PMID: 35475569 PMCID: PMC9294298 DOI: 10.1002/hbm.25884] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 03/05/2022] [Accepted: 04/05/2022] [Indexed: 11/09/2022] Open
Abstract
The resting‐state human brain is a dynamic system that shows frequency‐dependent characteristics. Recent studies demonstrate that coactivation pattern (CAP) analysis can identify recurring brain states with similar coactivation configurations. However, it is unclear whether and how CAPs depend on the frequency bands. The current study investigated the spatial and temporal characteristics of CAPs in the four frequency sub‐bands from slow‐5 (0.01–0.027 Hz), slow‐4 (0.027–0.073 Hz), slow‐3 (0.073–0.198 Hz), to slow‐2 (0.198–0.25 Hz), in addition to the typical low‐frequency range (0.01–0.08 Hz). In the healthy subjects, six CAP states were obtained at each frequency band in line with our prior study. Similar spatial patterns with the typical range were observed in slow‐5, 4, and 3, but not in slow‐2. While the frequency increased, all CAP states displayed shorter persistence, which caused more between‐state transitions. Specifically, from slow‐5 to slow‐4, the coactivation not only changed significantly in distributed cortical networks, but also increased in the basal ganglia as well as the amygdala. Schizophrenia patients showed significant alteration in the persistence of CAPs of slow‐5. Using leave‐one‐pair‐out, hold‐out and resampling validations, the highest classification accuracy (84%) was achieved by slow‐4 among different frequency bands. In conclusion, our findings provide novel information about spatial and temporal characteristics of CAP states at different frequency bands, which contributes to a better understanding of the frequency aspect of biomarkers for schizophrenia and other disorders.
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Affiliation(s)
- Hang Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hong Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chun Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Afra Wohlschläger
- Department of Neuroradiology, TUM-Neuroimaging Center, Technical University of Munich (TUM), Munich, Germany
| | - Felix Brandl
- Department of Psychiatry, TUM-Neuroimaging Center, Technical University of Munich (TUM), Munich, Germany
| | - Xin Di
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
| | - Shuai Wang
- Department of Psychiatry, The Affiliated Wuxi Mental Health Center of Nanjing Medical University, Wuxi, China
| | - Lin Tian
- Department of Psychiatry, The Affiliated Wuxi Mental Health Center of Nanjing Medical University, Wuxi, China
| | - Bharat Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
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Miller RL, Vergara VM, Pearlson GD, Calhoun VD. Multiframe Evolving Dynamic Functional Connectivity (EVOdFNC): A Method for Constructing and Investigating Functional Brain Motifs. Front Neurosci 2022; 16:770468. [PMID: 35516809 PMCID: PMC9063321 DOI: 10.3389/fnins.2022.770468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 01/24/2022] [Indexed: 11/28/2022] Open
Abstract
The study of brain network connectivity as a time-varying property began relatively recently and, to date, has remained primarily concerned with capturing a handful of discrete static states that characterize connectivity as measured on a timescale shorter than that of the full scan. Capturing group-level representations of temporally evolving patterns of connectivity is a challenging and important next step in fully leveraging the information available in large resting state functional magnetic resonance imaging (rs-fMRI) studies. We introduce a flexible, extensible data-driven framework for the stable identification of group-level multiframe (movie-style) dynamic functional network connectivity (dFNC) states. Our approach employs uniform manifold approximation and embedding (UMAP) to produce a continuity-preserving planar embedding of high-dimensional time-varying measurements of whole-brain functional network connectivity. Planar linear exemplars summarizing dominant dynamic trends across the population are computed from local linear approximations to the two-dimensional 2D embedded trajectories. A high-dimensional representation of each 2D exemplar segment is obtained by averaging the dFNC observations corresponding to the n planar nearest neighbors of τ evenly spaced points along the 2D line segment representation (where n is the UMAP number-of-neighbors parameter and τ is the temporal duration of trajectory segments being approximated). Each of the 2D exemplars thus “lifts” to a multiframe high-dimensional dFNC trajectory of length τ. The collection of high-dimensional temporally evolving dFNC representations (EVOdFNCs) derived in this manner are employed as dynamic basis objects with which to characterize observed high-dimensional dFNC trajectories, which are then expressed as weighted combination of these basis objects. Our approach yields new insights into anomalous patterns of fluidly varying whole-brain connectivity that are significantly associated with schizophrenia as a broad diagnosis as well as with certain symptoms of this serious disorder. Importantly, we show that relative to conventional hidden Markov modeling with single-frame unvarying dFNC summary states, EVOdFNCs are more sensitive to positive symptoms of schizophrenia including hallucinations and delusions, suggesting that a more dynamic characterization is needed to help illuminate such a complex brain disorder.
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Affiliation(s)
- Robyn L. Miller
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
- *Correspondence: Robyn L. Miller,
| | - Victor M. Vergara
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | | | - Vince D. Calhoun
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
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Wu X, Kong X, Vatansever D, Liu Z, Zhang K, Sahakian BJ, Robbins TW, Feng J, Thompson P, Zhang J. Dynamic changes in brain lateralization correlate with human cognitive performance. PLoS Biol 2022; 20:e3001560. [PMID: 35298460 PMCID: PMC8929635 DOI: 10.1371/journal.pbio.3001560] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 01/31/2022] [Indexed: 12/12/2022] Open
Abstract
Hemispheric lateralization constitutes a core architectural principle of human brain organization underlying cognition, often argued to represent a stable, trait-like feature. However, emerging evidence underlines the inherently dynamic nature of brain networks, in which time-resolved alterations in functional lateralization remain uncharted. Integrating dynamic network approaches with the concept of hemispheric laterality, we map the spatiotemporal architecture of whole-brain lateralization in a large sample of high-quality resting-state fMRI data (N = 991, Human Connectome Project). We reveal distinct laterality dynamics across lower-order sensorimotor systems and higher-order associative networks. Specifically, we expose 2 aspects of the laterality dynamics: laterality fluctuations (LF), defined as the standard deviation of laterality time series, and laterality reversal (LR), referring to the number of zero crossings in laterality time series. These 2 measures are associated with moderate and extreme changes in laterality over time, respectively. While LF depict positive association with language function and cognitive flexibility, LR shows a negative association with the same cognitive abilities. These opposing interactions indicate a dynamic balance between intra and interhemispheric communication, i.e., segregation and integration of information across hemispheres. Furthermore, in their time-resolved laterality index, the default mode and language networks correlate negatively with visual/sensorimotor and attention networks, which are linked to better cognitive abilities. Finally, the laterality dynamics are associated with functional connectivity changes of higher-order brain networks and correlate with regional metabolism and structural connectivity. Our results provide insights into the adaptive nature of the lateralized brain and new perspectives for future studies of human cognition, genetics, and brain disorders. Hemispheric lateralization constitutes a core architectural principle of human brain organization, often argued to represent a stable, trait-like feature, but how does this fit with our increasing appreciation of the inherently dynamic nature of brain networks? This neuroimaging study reveals the dynamic nature of functional brain lateralization at resting-state and its relationship with language function and cognitive flexibility.
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Affiliation(s)
- Xinran Wu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Xiangzhen Kong
- Department of Psychology and Behavioral Sciences, Zhejiang University, Zhejiang, China
| | - Deniz Vatansever
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Zhaowen Liu
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Kai Zhang
- School of Computer Science and Technology, East China Normal University, Shanghai, China
| | - Barbara J. Sahakian
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Department of the Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - Trevor W. Robbins
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Department of the Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, United Kingdom
- Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China
- Shanghai Center for Mathematical Sciences, Shanghai, China
| | - Paul Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Jie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- * E-mail:
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Dai X, Lopez-Pintado S. Tukey's Depth for Object Data. J Am Stat Assoc 2022; 118:1760-1772. [PMID: 37791295 PMCID: PMC10545316 DOI: 10.1080/01621459.2021.2011298] [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: 02/26/2021] [Accepted: 11/22/2021] [Indexed: 10/19/2022]
Abstract
We develop a novel exploratory tool for non-Euclidean object data based on data depth, extending celebrated Tukey's depth for Euclidean data. The proposed metric halfspace depth, applicable to data objects in a general metric space, assigns to data points depth values that characterize the centrality of these points with respect to the distribution and provides an interpretable center-outward ranking. Desirable theoretical properties that generalize standard depth properties postulated for Euclidean data are established for the metric halfspace depth. The depth median, defined as the deepest point, is shown to have high robustness as a location descriptor both in theory and in simulation. We propose an efficient algorithm to approximate the metric halfspace depth and illustrate its ability to adapt to the intrinsic data geometry. The metric halfspace depth was applied to an Alzheimer's disease study, revealing group differences in the brain connectivity, modeled as covariance matrices, for subjects in different stages of dementia. Based on phylogenetic trees of 7 pathogenic parasites, our proposed metric halfspace depth was also used to construct a meaningful consensus estimate of the evolutionary history and to identify potential outlier trees.
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Affiliation(s)
- Xiongtao Dai
- Department of Statistics, Iowa State University, Ames, Iowa 50011 USA
| | - Sara Lopez-Pintado
- Department of Health Sciences, Northeastern University, Boston, MA 02115 USA
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Faskowitz J, Betzel RF, Sporns O. Edges in brain networks: Contributions to models of structure and function. Netw Neurosci 2022; 6:1-28. [PMID: 35350585 PMCID: PMC8942607 DOI: 10.1162/netn_a_00204] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 08/02/2021] [Indexed: 11/16/2022] Open
Abstract
Network models describe the brain as sets of nodes and edges that represent its distributed organization. So far, most discoveries in network neuroscience have prioritized insights that highlight distinct groupings and specialized functional contributions of network nodes. Importantly, these functional contributions are determined and expressed by the web of their interrelationships, formed by network edges. Here, we underscore the important contributions made by brain network edges for understanding distributed brain organization. Different types of edges represent different types of relationships, including connectivity and similarity among nodes. Adopting a specific definition of edges can fundamentally alter how we analyze and interpret a brain network. Furthermore, edges can associate into collectives and higher order arrangements, describe time series, and form edge communities that provide insights into brain network topology complementary to the traditional node-centric perspective. Focusing on the edges, and the higher order or dynamic information they can provide, discloses previously underappreciated aspects of structural and functional network organization.
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Affiliation(s)
- Joshua Faskowitz
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Richard F. Betzel
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Indiana University Network Science Institute, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
| | - Olaf Sporns
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Indiana University Network Science Institute, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
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Hu D, Wang F, Zhang H, Wu Z, Zhou Z, Li G, Wang L, Lin W, Li G. Existence of Functional Connectome Fingerprint during Infancy and Its Stability over Months. J Neurosci 2022; 42:377-389. [PMID: 34789554 PMCID: PMC8802925 DOI: 10.1523/jneurosci.0480-21.2021] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 11/01/2021] [Accepted: 11/07/2021] [Indexed: 11/21/2022] Open
Abstract
The functional connectome fingerprint is a cluster of individualized brain functional connectivity patterns that are capable of distinguishing one individual from others. Although its existence has been demonstrated in adolescents and adults, whether such individualized patterns exist during infancy is barely investigated despite its importance in identifying the origin of the intrinsic connectome patterns that potentially mirror distinct behavioral phenotypes. To fill this knowledge gap, capitalizing on a longitudinal high-resolution structural and resting-state functional MRI dataset with 104 human infants (53 females) with 806 longitudinal scans (age, 16-876 d) and infant-specific functional parcellation maps, we observe that the brain functional connectome fingerprint may exist since infancy and keeps stable over months during early brain development. Specifically, we achieve an ∼78% individual identification rate by using ∼5% selected functional connections, compared with the best identification rate of 60% without connection selection. The frontoparietal networks recognized as the most contributive networks in adult functional connectome fingerprinting retain their superiority in infants despite being widely acknowledged as rapidly developing systems during childhood. The existence and stability of the functional connectome fingerprint are further validated on adjacent age groups. Moreover, we show that the infant frontoparietal networks can reach similar accuracy in predicting individual early learning composite scores as the whole-brain connectome, again resembling the observations in adults and highlighting the relevance of functional connectome fingerprint to cognitive performance. For the first time, these results suggest that each individual may retain a unique and stable marker of functional connectome during early brain development.SIGNIFICANCE STATEMENT Functional connectome fingerprinting during infancy featuring rapid brain development remains almost uninvestigated even though it is essential for understanding the early individual-level intrinsic pattern of functional organization and its relationship with distinct behavioral phenotypes. With an infant-tailored functional connection selection and validation strategy, we strive to provide the delineation of the infant functional connectome fingerprint by examining its existence, stability, and relationship with early cognitive performance. We observe that the brain functional connectome fingerprint may exist since early infancy and remains stable over months during the first 2 years. The identified key contributive functional connections and networks for fingerprinting are also verified to be highly predictive for cognitive score prediction, which reveals the association between infant connectome fingerprint and cognitive performance.
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Affiliation(s)
- Dan Hu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Fan Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Han Zhang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Zhen Zhou
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Guoshi Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, North Carolina 27599
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Matsui T, Pham TQ, Jimura K, Chikazoe J. On co-activation pattern analysis and non-stationarity of resting brain activity. Neuroimage 2022; 249:118904. [PMID: 35031473 DOI: 10.1016/j.neuroimage.2022.118904] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 01/06/2022] [Accepted: 01/11/2022] [Indexed: 11/30/2022] Open
Abstract
The non-stationarity of resting-state brain activity has received increasing attention in recent years. Functional connectivity (FC) analysis with short sliding windows and coactivation pattern (CAP) analysis are two widely used methods for assessing the dynamic characteristics of brain activity observed with functional magnetic resonance imaging (fMRI). However, the statistical nature of the dynamics captured by these techniques needs to be verified. In this study, we found that the results of CAP analysis were similar for real fMRI data and simulated stationary data with matching covariance structures and spectral contents. We also found that, for both the real and simulated data, CAPs were clustered into spatially heterogeneous modules. Moreover, for each of the modules in the real data, a spatially similar module was found in the simulated data. The present results suggest that care needs to be taken when interpreting observations drawn from CAP analysis as it does not necessarily reflect non-stationarity or a mixture of states in resting brain activity.
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Affiliation(s)
- Teppei Matsui
- Department of Biology, Okayama University, Okayama, Japan; JST-PRESTO, Japan Science and Technology Agency, Tokyo, Japan.
| | - Trung Quang Pham
- Section of Brain Function Information, Supportive Center for Brain Research, National Institute for Physiological Sciences, Okazaki, Japan
| | | | - Junichi Chikazoe
- Section of Brain Function Information, Supportive Center for Brain Research, National Institute for Physiological Sciences, Okazaki, Japan; Araya Inc., Tokyo, Japan
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Cai L, Xu X, Fan X, Ma J, Fan M, Wang Q, Wu Y, Pan N, Yin Z, Li X. Differences in Brain Functional Networks of Executive Function Between Cantonese-Mandarin Bilinguals and Mandarin Monolinguals. Front Hum Neurosci 2021; 15:748919. [PMID: 34867242 PMCID: PMC8638783 DOI: 10.3389/fnhum.2021.748919] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 10/28/2021] [Indexed: 01/19/2023] Open
Abstract
It remains controversial whether long-term logographic-logographic bilingual experience shapes the special brain functional subnetworks underlying different components of executive function (EF). To address this question, this study explored the differences in the functional connections underlying EF between the Cantonese-Mandarin bilinguals and Mandarin monolinguals. 31 Cantonese-Mandarin bilinguals and 31 Mandarin monolinguals were scanned in a 3-T magnetic resonance scanner at rest. 4 kinds of behavioral tasks of EF were tested. Network-based statistics (NBS) was performed to compare the connectomes of fronto-parietal (FP) and cingulo-opercular (CO) network between groups. The results showed that the bilinguals had stronger connectivity than monolinguals in a subnetwork located in the CO network rather than the FP network. The identified differential subnetwork referred to as the CO subnetwork contained 9 nodes and 10 edges, in which the center node was the left mid-insula with a degree centrality of 5. The functional connectivity of the CO subnetwork was significantly negatively correlated with interference effect in bilinguals. The results suggested that long-term Cantonese-Mandarin bilingual experience was associated with stronger functional connectivity underlying inhibitory control in the CO subnetwork.
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Affiliation(s)
- Lei Cai
- Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Xiaoyu Xu
- Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Xiaoxuan Fan
- Wuhan Children's Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jingwen Ma
- Guangdong Provincial Maternal and Child Health Care Hospital, Guangzhou, China
| | - Miao Fan
- The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Qingxiong Wang
- Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Yujia Wu
- Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Ning Pan
- Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Zhixin Yin
- Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Xiuhong Li
- Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou, China
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Duda M, Koutra D, Sripada C. Validating dynamicity in resting state fMRI with activation-informed temporal segmentation. Hum Brain Mapp 2021; 42:5718-5735. [PMID: 34510647 PMCID: PMC8559473 DOI: 10.1002/hbm.25649] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 08/11/2021] [Accepted: 08/24/2021] [Indexed: 12/18/2022] Open
Abstract
Confirming the presence (or absence) of dynamic functional connectivity (dFC) states during rest is an important open question in the field of cognitive neuroscience. The prevailing dFC framework aims to identify dynamics directly from connectivity estimates with a sliding window approach, however this method suffers from several drawbacks including sensitivity to window size and poor test-retest reliability. We hypothesize that time-varying changes in functional connectivity are mirrored by significant temporal changes in functional activation, and that this coupling can be leveraged to study dFC without the need for a predefined sliding window. Here, we introduce a data-driven dFC framework, which involves informed segmentation of fMRI time series at candidate FC state transition points estimated from changes in whole-brain functional activation, rather than a fixed-length sliding window. We show our approach reliably identifies true cognitive state change points when applied on block-design working memory task data and outperforms the standard sliding window approach in both accuracy and computational efficiency in this context. When applied to data from four resting state fMRI scanning sessions, our method consistently recovers five reliable FC states, and subject-specific features derived from these states show significant correlation with behavioral phenotypes of interest (cognitive ability, personality). Overall, these results suggest abrupt whole-brain changes in activation can be used as a marker for changes in connectivity states and provides new evidence for the existence of time-varying FC in rest.
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Affiliation(s)
- Marlena Duda
- Department of Computational Medicine and BioinformaticsUniversity of MichiganAnn ArborMichiganUSA
| | - Danai Koutra
- Department of Computer Science and EngineeringUniversity of MichiganAnn ArborMichiganUSA
| | - Chandra Sripada
- Department of PsychiatryUniversity of MichiganAnn ArborMichiganUSA
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Spadone S, Perrucci MG, Di Cosmo G, Costantini M, Della Penna S, Ferri F. Frontal and parietal background connectivity and their dynamic changes account for individual differences in the multisensory representation of peripersonal space. Sci Rep 2021; 11:20533. [PMID: 34654814 PMCID: PMC8520015 DOI: 10.1038/s41598-021-00048-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 10/05/2021] [Indexed: 11/22/2022] Open
Abstract
Functional connectivity (FC) of brain networks dynamically fluctuates during both rest and task execution. Individual differences in dynamic FC have been associated with several cognitive and behavioral traits. However, whether dynamic FC also contributes to sensorimotor representations guiding body-environment interactions, such as the representation of peripersonal space (PPS), is currently unknown. PPS is the space immediately surrounding the body and acts as a multisensory interface between the individual and the environment. We used an audio-tactile task with approaching sounds to map the individual PPS extension, and fMRI to estimate the background FC. Specifically, we analyzed FC values for each stimulus type (near and far space) and its across-trial variability. FC was evaluated between task-relevant nodes of two fronto-parietal networks (the Dorsal Attention Network, DAN, and the Fronto-Parietal Network, FPN) and a key PPS region in the premotor cortex (PM). PM was significantly connected to specific task-relevant nodes of the DAN and the FPN during the audio-tactile task, and FC was stronger while processing near space, as compared to far space. At the individual level, less PPS extension was associated with stronger premotor-parietal FC during processing of near space, while the across-trial variability of premotor-parietal and premotor-frontal FC was higher during the processing of far space. Notably, only across-trial FC variability captured the near-far modulation of space processing. Our findings indicate that PM connectivity with task-relevant frontal and parietal regions and its dynamic changes participate in the mechanisms that enable PPS representation, in agreement with the idea that neural variability plays a crucial role in plastic and dynamic sensorimotor representations.
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Affiliation(s)
- Sara Spadone
- Department of Neuroscience, Imaging and Clinical Sciences - and ITAB, Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy.
| | - Mauro Gianni Perrucci
- Department of Neuroscience, Imaging and Clinical Sciences - and ITAB, Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Giulio Di Cosmo
- Department of Neuroscience, Imaging and Clinical Sciences - and ITAB, Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Marcello Costantini
- Department of Psychological, Health and Territorial Sciences - and ITAB, Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Stefania Della Penna
- Department of Neuroscience, Imaging and Clinical Sciences - and ITAB, Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Francesca Ferri
- Department of Neuroscience, Imaging and Clinical Sciences - and ITAB, Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
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50
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Signorelli CM, Meling D. Towards new concepts for a biological neuroscience of consciousness. Cogn Neurodyn 2021; 15:783-804. [PMID: 34603542 PMCID: PMC8448820 DOI: 10.1007/s11571-020-09658-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 11/17/2020] [Accepted: 12/11/2020] [Indexed: 02/07/2023] Open
Abstract
In the search for a sound model of consciousness, we aim at introducing new concepts: closure, compositionality, biobranes and autobranes. This is important to overcome reductionism and to bring life back into the neuroscience of consciousness. Using these definitions, we conjecture that consciousness co-arises with the non-trivial composition of biological closure in the form of biobranes and autobranes: conscious processes generate closed activity at various levels and are, in turn, themselves, supported by biobranes and autobranes. This approach leads to a non-reductionist biological and simultaneously phenomenological theory of conscious experience, giving new perspectives for a science of consciousness. Future works will implement experimental definitions and computational simulations to characterize these dynamical biobranes interacting.
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Affiliation(s)
- Camilo Miguel Signorelli
- Department of Computer Science, University of Oxford, Oxford, UK
- Cognitive Neuroimaging Unit, INSERM U992, NeuroSpin, Gif-sur-Yvette, France
- Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Daniel Meling
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zurich, Zurich, Switzerland
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