Dynamic Brain Connectivity in Resting State Functional MR Imaging

Jalilianhasanpour, Rozita; Ryan, Daniel; Agarwal, Shruti; Beheshtian, Elham; Gujar, Sachin K.; Pillai, Jay J.; Sair, Haris I.

doi:10.1016/j.nic.2020.09.004

Cited by 18 publications

(11 citation statements)

References 62 publications

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“…In particular, increased temporal variability in the ipsilesional precentral gyrus correlated with motor recovery ( Hu et al, 2018 ). Low SD and CV mean the decreased exploratory capacity between the RSNs ( Tahmasebi et al, 2012 ; Jalilianhasanpour et al, 2021 ). Our results suggested that cognitive impairment may be associated with the decline in SD and CV.…”

Section: Discussionmentioning

confidence: 99%

Suboptimal states and frontoparietal network-centered incomplete compensation revealed by dynamic functional network connectivity in patients with post-stroke cognitive impairment

Rao

Wang

et al. 2022

Front. Aging Neurosci.

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BackgroundNeural reorganization occurs after a stroke, and dynamic functional network connectivity (dFNC) pattern is associated with cognition. We hypothesized that dFNC alterations resulted from neural reorganization in post-stroke cognitive impairment (PSCI) patients, and specific dFNC patterns characterized different pathological types of PSCI.MethodsResting-state fMRI data were collected from 16 PSCI patients with hemorrhagic stroke (hPSCI group), 21 PSCI patients with ischemic stroke (iPSCI group), and 21 healthy controls (HC). We performed the dFNC analysis for the dynamic connectivity states, together with their topological and temporal features.ResultsWe identified 10 resting-state networks (RSNs), and the dFNCs could be clustered into four reoccurring states (modular, regional, sparse, and strong). Compared with HC, the hPSCI and iPSCI patients showed lower standard deviation (SD) and coefficient of variation (CV) in the regional and modular states, respectively (p < 0.05). Reduced connectivities within the primary network (visual, auditory, and sensorimotor networks) and between the primary and high-order cognitive control domains were observed (p < 0.01).ConclusionThe transition trend to suboptimal states may play a compensatory role in patients with PSCI through redundancy networks. The reduced exploratory capacity (SD and CV) in different suboptimal states characterized cognitive impairment and pathological types of PSCI. The functional disconnection between the primary and high-order cognitive control network and the frontoparietal network centered (FPN-centered) incomplete compensation may be the pathological mechanism of PSCI. These results emphasize the flexibility of neural reorganization during self-repair.

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Section: Discussionmentioning

confidence: 99%

Suboptimal states and frontoparietal network-centered incomplete compensation revealed by dynamic functional network connectivity in patients with post-stroke cognitive impairment

Rao

Wang

et al. 2022

Front. Aging Neurosci.

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“…Increasing evidence suggests that FC patterns are temporally dynamic rather than stationary throughout the length of the rs-fMRI scanning, even at different levels of consciousness (14)(15)(16). A sliding-window technique followed by k-means clustering has been commonly employed to identify highly replicable subpatterns of dynamic FC, named "brain states" (17,18); where certain brain states can be associated with physiological and pathological brain conditions (19). Comparing between conditions under general anesthesia (GA) and during wakefulness, distinct distributions of the identi ed sub-patterns of FC have been observed in animal studies (20,21), as well as studies of healthy human subjects (2,22).…”

Section: Introductionmentioning

confidence: 99%

Characteristic dynamic functional connectivity during sevoflurane-induced general anesthesia

Miao

Tantawi

Alizadeh

et al. 2023

Preprint

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General anesthesia (GA) during surgery is commonly maintained by inhalational sevoflurane. Previous resting state functional MRI (rs-fMRI) studies have demonstrated suppressed functional connectivity (FC) of the entire brain networks, especially the default mode networks, transitioning from the awake to GA condition. However, accuracy and reliability were limited by previous administration methods (e.g. face mask) and short rs-fMRI scans. Therefore, in this study, a clinical scenario of epilepsy patients undergoing laser interstitial thermal therapy (LITT) was leveraged to acquire 15 minutes of rs-fMRI while under general endotracheal anesthesia to maximize the accuracy of sevoflurane level. Nine recruited patients had fMRI acquired during awake and under GA, of which seven were included in both static and dynamic FC analyses. Group independent component analysis (ICA) and a sliding-window method followed by k-means clustering were applied to identify four dynamic brain states, which characterized subtypes of FC patterns. Our results showed that a low-FC brain state was characteristic of the GA condition as a single featuring state during the entire rs-fMRI session; while a highly synchronized brain state was characteristic of the awake condition, with frequent fluctuations between three brain states. In conclusion, our study revealed remarkable dynamic connectivity changes from awake to GA condition and demonstrated the advantages of dynamic FC analysis for future studies in the assessments of the effects of GA on brain functional activities.

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“…For example, dynamic reconfiguration of functional brain networks was found during executive cognition by using dFC technology (37). Thus, dFC can provide additional information that may promote our understanding of the association between altered brain functions and CBP (38,39). Recent studies have shown that dFC can reflect pain conditions at multiple timescales (e.g., short-term state and long-term trait) rather than just the current state of patients with chronic pain (40), and characterize pain pathophysiology from a dynamic perspective representing oscillations of the FC (41)(42)(43).…”

Section: Introductionmentioning

confidence: 99%

Temporal Grading Index of Functional Network Topology Predicts Pain Perception of Patients With Chronic Back Pain

Zhao

et al. 2022

Front. Neurol.

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Chronic back pain (CBP) is a maladaptive health problem affecting the brain function and behavior of the patient. Accumulating evidence has shown that CBP may alter the organization of functional brain networks; however, whether the severity of CBP is associated with changes in dynamics of functional network topology remains unclear. Here, we generated dynamic functional networks based on resting-state functional magnetic resonance imaging (rs-fMRI) of 34 patients with CBP and 34 age-matched healthy controls (HC) in the OpenPain database via a sliding window approach, and extracted nodal degree, clustering coefficient (CC), and participation coefficient (PC) of all windows as features to characterize changes of network topology at temporal scale. A novel feature, named temporal grading index (TGI), was proposed to quantify the temporal deviation of each network property of a patient with CBP to the normal oscillation of the HCs. The TGI of the three features achieved outstanding performance in predicting pain intensity on three commonly used regression models (i.e., SVR, Lasso, and elastic net) through a 5-fold cross-validation strategy, with the minimum mean square error of 0.25 ± 0.05; and the TGI was not related to depression symptoms of the patients. Furthermore, compared to the HCs, brain regions that contributed most to prediction showed significantly higher CC and lower PC across time windows in the CBP cohort. These results highlighted spatiotemporal changes in functional network topology in patients with CBP, which might serve as a valuable biomarker for assessing the sensation of pain in the brain and may facilitate the development of CBP management/therapy approaches.

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Dynamic Brain Connectivity in Resting State Functional MR Imaging

Cited by 18 publications

References 62 publications

Suboptimal states and frontoparietal network-centered incomplete compensation revealed by dynamic functional network connectivity in patients with post-stroke cognitive impairment

Suboptimal states and frontoparietal network-centered incomplete compensation revealed by dynamic functional network connectivity in patients with post-stroke cognitive impairment

Characteristic dynamic functional connectivity during sevoflurane-induced general anesthesia

Temporal Grading Index of Functional Network Topology Predicts Pain Perception of Patients With Chronic Back Pain

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