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1. Temporal and spectral characteristics of dynamic functional connectivity between resting-state networks reveal information beyond static connectivity

   https://doi.org/10.1371/journal.pone.0190220  

   Chiang, Sharon ; Vankov, Emilian R. ; Yeh, Hsiang J. ; Guindani, Michele ; Vannucci, Marina ; Haneef, Zulfi ; Stern, John M. ; Hayasaka, Satoru ( January 2018 , PLOS ONE)
2. Transient states of network connectivity are atypical in autism: A dynamic functional connectivity study

   https://doi.org/10.1002/hbm.24529  

   Mash, Lisa E. ; Linke, Annika C. ; Olson, Lindsay A. ; Fishman, Inna ; Liu, Thomas T. ; Müller, Ralph‐Axel ( January 2019 , Human Brain Mapping)

   There is ample evidence of atypical functional connectivity (FC) in autism spectrum disorders (ASDs). However, transient relationships between neural networks cannot be captured by conventional static FC analyses. Dynamic FC (dFC) approaches have been used to identify repeating, transient connectivity patterns (“states”), revealing spatiotemporal network properties not observable in static FC. Recent studies have found atypical dFC in ASDs, but questions remain about the nature of group differences in transient connectivity, and the degree to which states persist or change over time. This study aimed to: (a) describe and relate static and dynamic FC in typical development and ASDs, (b) describe group differences in transient states and compare them with static FC patterns, and (c) examine temporal stability and flexibility between identified states. Resting‐state functional magnetic resonance imaging (fMRI) data were collected from 62 ASD and 57 typically developing (TD) children and adolescents. Whole‐brain, data‐driven regions of interest were derived from group independent component analysis. Sliding window analysis and k‐means clustering were used to explore dFC and identify transient states. Across all regions, static overconnnectivity and increased variability over time in ASDs predominated. Furthermore, significant patterns of group differences emerged in two transient states that were not observed in the static FC matrix, with group differences in one state primarily involving sensory and motor networks, and in the other involving higher‐order cognition networks. Default mode network segregation was significantly reduced in ASDs in both states. Results highlight that dynamic approaches may reveal more nuanced transient patterns of atypical FC in ASDs.

    

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3. Aberrant Dynamic Functional Connectivity of Default Mode Network in Schizophrenia and Links to Symptom Severity

   https://doi.org/10.3389/fncir.2021.649417  

   Sendi, Mohammad S. ; Zendehrouh, Elaheh ; Ellis, Charles A. ; Liang, Zhijia ; Fu, Zening ; Mathalon, Daniel H. ; Ford, Judith M. ; Preda, Adrian ; van Erp, Theo G. ; Miller, Robyn L. ; et al ( March 2021 , Frontiers in Neural Circuits)

   Background: Schizophrenia affects around 1% of the global population. Functional connectivity extracted from resting-state functional magnetic resonance imaging (rs-fMRI) has previously been used to study schizophrenia and has great potential to provide novel insights into the disorder. Some studies have shown abnormal functional connectivity in the default mode network (DMN) of individuals with schizophrenia, and more recent studies have shown abnormal dynamic functional connectivity (dFC) in individuals with schizophrenia. However, DMN dFC and the link between abnormal DMN dFC and symptom severity have not been well-characterized. Method: Resting-state fMRI data from subjects with schizophrenia (SZ) and healthy controls (HC) across two datasets were analyzed independently. We captured seven maximally independent subnodes in the DMN by applying group independent component analysis and estimated dFC between subnode time courses using a sliding window approach. A clustering method separated the dFCs into five reoccurring brain states. A feature selection method modeled the difference between SZs and HCs using the state-specific FC features. Finally, we used the transition probability of a hidden Markov model to characterize the link between symptom severity and dFC in SZ subjects. Results: We found decreases in the connectivity of the anterior cingulate cortex (ACC) and increases in the connectivity between the precuneus (PCu) and the posterior cingulate cortex (PCC) (i.e., PCu/PCC) of SZ subjects. In SZ, the transition probability from a state with weaker PCu/PCC and stronger ACC connectivity to a state with stronger PCu/PCC and weaker ACC connectivity increased with symptom severity. Conclusions: To our knowledge, this was the first study to investigate DMN dFC and its link to schizophrenia symptom severity. We identified reproducible neural states in a data-driven manner and demonstrated that the strength of connectivity within those states differed between SZs and HCs. Additionally, we identified a relationship between SZ symptom severity and the dynamics of DMN functional connectivity. We validated our results across two datasets. These results support the potential of dFC for use as a biomarker of schizophrenia and shed new light upon the relationship between schizophrenia and DMN dynamics. 

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4. Spatial Dynamic Functional Connectivity Analysis Identifies Distinctive Biomarkers in Schizophrenia

   https://doi.org/10.3389/fnins.2019.01006  

   Bhinge, Suchita ; Long, Qunfang ; Calhoun, Vince D. ; Adali, Tülay ( September 2019 , Frontiers in Neuroscience)
5. A Deep Learning Framework to Localize the Epileptogenic Zone from Dynamic Functional Connectivity Using a Combined Graph Convolutional and Transformer Network

   https://doi.org/10.1109/ISBI53787.2023.10230831  

   Nandakumar, Naresh ; Hsu, David ; Ahmed, Raheel ; Venkataraman, Archana ( April 2023 , IEEE)