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A common framework for the problem of deriving estimates of dynamic functional brain connectivity

William Hedley Thompson, Peter Fransson
2018 NeuroImage  
Abstract The research field of dynamic functional connectivity explores the temporal properties of brain connectivity.  ...  Our overall intention was to derive a theoretical framework that was constructed such that a wide variety of dynamic functional connectivity techniques could be expressed and evaluated within the same  ...  W consists of T number of weight vectors. Finally, Y contains the resulting estimate of dynamic functional connectivity for each time-point and all channels.  ... 
doi:10.1016/j.neuroimage.2017.12.057 pmid:29292136 fatcat:nhkdzoovcrc2vlrjqcidw4ckty

A common framework for the problem of deriving estimates of dynamic functional brain connectivity [article]

William Hedley Thompson, Peter Fransson
2017 bioRxiv   pre-print
The research field of dynamic functional connectivity explores the temporal properties of brain connectivity. To date, many methods have been proposed, which are based on quite different assumptions.  ...  Our overall intention was to derive a theoretical framework that was constructed such that a wide variety of dynamic functional connectivity techniques could be expressed and evaluated within the same  ...  W consists of T number of weight vectors. Finally, Y contains the resulting estimate of dynamic functional connectivity for each time-point and all channels.  ... 
doi:10.1101/215772 fatcat:razmeqtmxbgh5njhhzfuyfb5ru

A Tensor Statistical Model for Quantifying Dynamic Functional Connectivity [chapter]

Yingying Zhu, Xiaofeng Zhu, Minjeong Kim, Jin Yan, Guorong Wu
2017 Lecture Notes in Computer Science  
temporal dynamics.  ...  Then we obtain dynamic functional connectivity (dFC) for each individual subject by simultaneously estimating the within-sliding-window functional connectivity and characterizing the across-sliding-window  ...  Third, in order to learn the statistical model of intrinsic feature representations derived from the dynamic functional connectivity (dFC) in the population, we arrange the estimated functional dynamics  ... 
doi:10.1007/978-3-319-59050-9_32 pmid:29657509 pmcid:PMC5896766 fatcat:pq7eui2ryjbutl2ehn43kre6ue

Graph Signal Processing of Low and High-Order Dynamic Functional Connectivity Networks Using EEG Resting-State for Schizophrenia: A Whole Brain Breakdown [article]

Stavros I Dimitriadis
2019 bioRxiv   pre-print
In the present study, a dynamic functional connectivity graph (DFCG) has been estimated using the imaginary part of phase lag value (iPLV).  ...  Conventional static or dynamic functional connectivity graph (FCG/DFCG) referred to as low-order FCG focusing on temporal correlation estimates of the resting-state electroencephalography (rs-EEG) time  ...  In the present study, a dynamic functional connectivity graph (DFCG) has been estimated using the imaginary part of phase lag value (iPLV).  ... 
doi:10.1101/551671 fatcat:xuzbplj4argzzn5wa5lbzhoq4i

Macroscopic cortical dynamics: Spatially uncorrelated but temporally coherent rich-club organisations in source-space resting-state EEG [article]

Steve Mehrkanoon
2020 arXiv   pre-print
Recent studies indicate that resting-state functional connectivity is not static, but exhibits complex dynamics.  ...  revealed bilateral functional connectivity between fronto-parietal and posterior cortices.  ...  Network theory has been used to investigate the structural connectivity of the brain (wiring diagram) and its functional connectivity (FC) − the temporal correlation structure of spatially distributed  ... 
arXiv:2007.15092v1 fatcat:epzzlcwo6vcthf5couy3tohobq

Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification

Chong-Yaw Wee, Sen Yang, Pew-Thian Yap, Dinggang Shen
2015 Brain Imaging and Behavior  
Dynamic changes of neural interactions can be reflected by variations of topology and correlation strength in temporally correlated functional connectivity networks.  ...  To preserve temporal smoothness of R-fMRI sub-series, we suggest to jointly estimate the temporal networks by maximizing a penalized log likelihood using a fused sparse learning algorithm.  ...  ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles.  ... 
doi:10.1007/s11682-015-9408-2 pmid:26123390 pmcid:PMC4692725 fatcat:jics2l5mbrfsrm3zq2m2q3s5ku

Resting-state "physiological networks"

JingyuanE. Chen, LauraD. Lewis, Catie Chang, Qiyuan Tian, NinaE. Fultz, NedA. Ohringer, BruceR. Rosen, JonathanR. Polimeni
2020 NeuroImage  
Further, we show that such physiologically-relevant connectivity estimates appear to dominate the overall connectivity observations in multiple HCP subjects, and that this apparent "physiological connectivity  ...  " cannot be removed by the use of a single nuisance regressor for the entire brain (such as global signal regression) due to the clear regional heterogeneity of the physiologically-coupled responses.  ...  This work was supported in part by the National Institute of  ... 
doi:10.1016/j.neuroimage.2020.116707 pmid:32145437 pmcid:PMC7165049 fatcat:nrxulmwaunc6bee6vqzdfq66xu

Time-resolved structure-function coupling in brain networks [article]

Zhen-Qi Liu, Bertha Vazquez-Rodriguez, R. Nathan Spreng, Boris Bernhardt, Richard F. Betzel, Bratislav Misic
2021 bioRxiv   pre-print
We use a temporal unwrapping procedure to identify moment-to-moment co-fluctuations in neural activity, and reconstruct time-resolved structure-function coupling patterns.  ...  Finally, we show that the variability of structure-function coupling is shaped by the distribution of connection lengths.  ...  (a) Top: static structure-function coupling is estimated using the functional connectivity matrix derived from the whole resting-state time-series [79], and compared with dynamic coupling.  ... 
doi:10.1101/2021.07.08.451672 fatcat:z7hczhnil5btnekjmoxv4ju6le

Topographic gradients of intrinsic dynamics across neocortex

Golia Shafiei, Ross D Markello, Reinder Vos de Wael, Boris C Bernhardt, Ben D Fulcher, Bratislav Misic
2020 eLife  
Applying massive temporal feature extraction to regional haemodynamic activity, we systematically estimate over 6,000 statistical properties of individual brain regions' time-series across the neocortex  ...  axis and dominated by measures of dynamic range.  ...  Spatial gradients of intrinsic dynamics support distinct functional activations | We used Neurosynth to derive probability maps for multiple psychological terms [122] .  ... 
doi:10.7554/elife.62116 pmid:33331819 pmcid:PMC7771969 fatcat:63twpz6fdra4dm3a67oxcuhsda

Reliability of Static and Dynamic Network Metrics in the Resting-State: A MEG-Beamformed Connectivity Analysis

Stavros I. Dimitriadis, Bethany Routley, David E. Linden, Krish D. Singh
2018 Frontiers in Neuroscience  
Here, we first estimated both static (SFC) and dynamic functional connectivity (DFC) after beamforming source reconstruction using the imaginary part of the phase locking index (iPLV) and the correlation  ...  Although many algorithms for the analysis of brain connectivity have been proposed, the reliability of network metrics derived from both static and dynamic functional connectivity is still unknown.  ...  The concept of chronnectome is the incorporation of a dynamic view of functional brain connectivity networks and the evolution of revisiting distinct spatio-temporal brain states (functional connectivity  ... 
doi:10.3389/fnins.2018.00506 pmid:30127710 pmcid:PMC6088195 fatcat:mpxnhkrhnfe75h3bd67zfukec4

Reliability of Static and Dynamic Network Metrics in the Resting-State: A MEG-beamformed Connectivity Analysis [article]

Stavros I Dimitriadis, Bethany Routley, David E Linden, Krish D Singh
2018 biorxiv/medrxiv   pre-print
Here, we first estimated both static (SFC) and dynamic functional connectivity (DFC) after beamforming source reconstruction using the imaginary part of the phase locking index (iPLV) and the correlation  ...  Although many algorithms for the analysis of brain connectivity have been proposed, the reliability of network metrics derived from both static and dynamic functional connectivity is still unknown.  ...  The concept of chronnectome is the incorporation of a dynamic view of functional brain connectivity networks and the evolution of revisiting distinct spatio-temporal brain states (functional connectivity  ... 
doi:10.1101/358192 fatcat:xcvdd5kmkbcajptxxtnaylsnpe

Unified Framework for Robust Estimation of Brain Networks From fMRI Using Temporal and Spatial Correlation Analyses

Y.M. Wang, Jing Xia
2009 IEEE Transactions on Medical Imaging  
connectivity closer to the characterization of direct functional interactions of the brain.  ...  This paper presents a general and novel statistical framework for robust and more complete estimation of brain functional connectivity from fMRI based on correlation analyses and hypothesis testing.  ...  Dynamic connections in fMRI are thought to be reflected by high temporal correlations of the time series.  ... 
doi:10.1109/tmi.2009.2014863 pmid:19237342 pmcid:PMC3378991 fatcat:wwo4eq56zjehpd63clztby3jpi

Evaluation of confound regression strategies for the mitigation of micromovement artifact in studies of dynamic resting-state functional connectivity and multilayer network modularity

David M. Lydon-Staley, Rastko Ciric, Theodore D. Satterthwaite, Danielle S. Bassett
2019 Network Neuroscience  
Dynamic functional connectivity reflects the spatiotemporal organization of spontaneous brain activity in health and disease.  ...  Dynamic functional connectivity may be susceptible to artifacts induced by participant motion.  ...  Pipeline 9 (wmLOCAL) used a voxelwise, localized WM regressor in addition to motion estimates, their temporal derivatives, and despiking. Pipeline 10.  ... 
doi:10.1162/netn_a_00071 pmid:30793090 pmcid:PMC6370491 fatcat:yuze35t3k5fujn32q5swflvrc4

Characterizing Whole Brain Temporal Variation of Functional Connectivity via Zero and First Order Derivatives of Sliding Window Correlations

Flor A. Espinoza, Victor M. Vergara, Eswar Damaraju, Kyle G. Henke, Ashkan Faghiri, Jessica A. Turner, Aysenil A. Belger, Judith M. Ford, Sarah C. McEwen, Daniel H. Mathalon, Bryon A. Mueller, Steven G. Potkin (+4 others)
2019 Frontiers in Neuroscience  
Our approach, referred to as temporal variation of functional network connectivity (tvFNC), estimates the derivative of dFNC, and then searches for reoccurring patterns of concurrent dFNC states and their  ...  However, estimation of states and their temporal dynamicity still suffers from noisy and imperfect estimations.  ...  Schizophrenia and Healthy Control Group Differences in Temporal Variation of Functional Network Connectivity All presented results were corrected for multiple testing.  ... 
doi:10.3389/fnins.2019.00634 pmid:31316333 pmcid:PMC6611425 fatcat:gxxqwjv35rco5cmdcqz4uyjyzm

Epileptic network activity revealed by dynamic functional connectivity in simultaneous EEG-fMRI

Maria Giulia Preti, Nora Leonardi, F. Isik Karahanoglu, Frederic Grouiller, Melanie Genetti, Margina Seeck, Serge Vulliemoz, Dimitri Van De Ville
2014 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)  
Recent findings highlighted the non-stationarity of brain functional connectivity (FC) during resting-state functional magnetic resonance imaging (fMRI), encouraging the development of methods allowing  ...  a dynamic analysis for a better understanding of network organisation in epilepsy.  ...  linking the EEG-derived information with the dFC data, by the use of the EEG as regressor in the estimation of the dFC.  ... 
doi:10.1109/isbi.2014.6867796 dblp:conf/isbi/PretiLKGGSVV14 fatcat:5ugyxojtlbhanfmm53pob6hdui
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