Altered Dynamic Functional Network Connectivity in Frontal Lobe Epilepsy

Klugah‐Brown, Benjamin; Luo, Cheng; He, Hong; Jiang, Sisi; Armah, Gabriel Kofi; Wu, Yu-Chien; Li, Jianfu; Yin, Wenjie; Yao, Dezhong

doi:10.1007/s10548-018-0678-z

Cited by 41 publications

(30 citation statements)

References 41 publications

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“…While considerable research has used temporal coupling of BOLD signals (i.e., FC) to reveal information exchange among brain areas (Jiang et al, ; Jiang, Luo, Gong, Peng, et al, ; Zhu et al, ), rich high‐order information interactions such as dynamic (He et al, ; Hutchison et al, ; Klugah‐Brown et al, ), causal (Chen, Jiang, et al, ; Jiang, Luo, Li, Duan, et al, ), hierarchical (Smith et al, ), and many‐to‐many (Pessoa, ) have not been fully explored yet. To characterize a complex relationship between white‐matter and gray‐matter regions, this study measured the functional resemblance between any two white‐matter networks based on their connectivity profiles with gray‐matter regions.…”

Section: Discussionmentioning

confidence: 99%

Dysfunctional white‐matter networks in medicated and unmedicated benign epilepsy with centrotemporal spikes

Jiang

et al. 2019

Human Brain Mapping

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Benign epilepsy with centrotemporal spikes (BECT) is the most common childhood idiopathic focal epilepsy syndrome, which characterized with white‐matter abnormalities in the rolandic cortex. Although diffusion tensor imaging research could characterize white‐matter structural architecture, it cannot detect neural activity or white‐matter functions. Recent studies demonstrated the functional organization of white‐matter by using functional magnetic resonance imaging (fMRI), suggesting that it is feasible to investigate white‐matter dysfunctions in BECT. Resting‐state fMRI data were collected from 24 new‐onset drug‐naive (unmedicated [NMED]), 21 medicated (MED) BECT patients, and 27 healthy controls (HC). Several white‐matter functional networks were obtained using a clustering analysis on voxel‐by‐voxel correlation profiles. Subsequently, conventional functional connectivity (FC) was calculated in four frequency sub‐bands (Slow‐5:0.01–0.027, Slow‐4:0.027–0.073, Slow‐3:0.073–0.198, and Slow‐2:0.198–0.25 Hz). We also employed a functional covariance connectivity (FCC) to estimate the covariant relationship between two white‐matter networks based on their correlations with multiple gray‐matter regions. Compared with HC, the NMED showed increased FC and/or FCC in rolandic network (RN) and precentral/postcentral network, and decreased FC and/or FCC in dorsal frontal network, while these alterations were not observed in the MED group. Moreover, the changes exhibited frequency‐specific properties. Specifically, only two alterations were shared in at least two frequency bands. Most of these alterations were observed in the frequency bands of Slow‐3 and Slow‐4. This study provided further support on the existence of white‐matter functional networks which exhibited frequency‐specific properties, and extended abnormalities of rolandic area from the perspective of white‐matter dysfunction in BECT.

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Section: Discussionmentioning

confidence: 99%

Dysfunctional white‐matter networks in medicated and unmedicated benign epilepsy with centrotemporal spikes

Jiang

et al. 2019

Human Brain Mapping

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“…Thus, the thousand level template consisted of 1,000 cortical regions from the Schaefer‐1,000 atlas and 217 subcortical and cerebellar regions from the Craddock‐950 atlas. Similar to our previous studies (Dong et al, ; Klugah‐Brown et al, ), the temporal variability of FC was evaluated, and the same calculation pipeline was carried out in the two‐scale brain templates.…”

Section: Methodsmentioning

confidence: 99%

“…In consideration of the controversy how to define the window length, we selected a range of window lengths (20, 21, 22, …, 50 TR) (Hutchison, Womelsdorf, Gati, Everling, & Menon, ). And the typical window step between sliding windows adopted in previous studies ranges from one TR to 50% of the window length (Chang, Liu, Chen, Liu, & Duyn, ; Klugah‐Brown et al, ; Liu et al, ). The main calculation about window step in this study was set as 40% of window length.…”

Section: Methodsmentioning

confidence: 99%

“…The previous study suggested that dynamic fluctuations in the brain's large‐scale organizational properties might minimize metabolic requirements while maintaining the brain in a responsive state (Zalesky, Fornito, Cocchi, Gollo, & Breakspear, ). In dynamic FC analysis in fMRI, overlapping and nonoverlapping sliding window methods have been widely used in several neuropsychiatric disorders such as autism spectrum disorder, attention deficit hyperactivity disorder, schizophrenia and epilepsy (He et al, ; Klugah‐Brown et al, ; Li, Duan, Cui, Chen, & Liao, ; Liao et al, ; Zhang et al, ). In epilepsy, Liao et al found a complex dynamic interaction among functional networks during absence seizures (Liao et al, ).…”

Section: Introductionmentioning

confidence: 99%

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Reconfiguration of dynamic large‐scale brain network functional connectivity in generalized tonic–clonic seizures

Jia

Xie

Dong

et al. 2019

Human Brain Mapping

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An increasing number of studies in patients with generalized tonic–clonic seizures (GTCS) have reported the alteration of functional connectivity (FC) in many brain networks. However, little is known about the underlying temporal variability of FC in large‐scale brain functional networks in patients. Recently, dynamic FC could provide novel insight into the physiological mechanisms in the brain. Here, we recruited 63 GTCS and 65 age‐ and sex‐matched healthy controls. Dynamic FC approaches were used to evaluate alterations in the temporal variability of FC in patients at the region‐ and network‐levels. In addition, two kinds of brain templates (>102 and > 103 regions) and two kinds of temporal variability FC approaches were adopted to verify the stability of the results. Patients showed increased FC variability in regions of the default mode network (DMN), ventral attention network (VAN) and motor‐related areas. The DAN, VAN, and DMN illustrated enhanced FC variability at the within‐network level. In addition, increased FC variabilities between networks were found between the DMN and cognition‐related networks, including the VAN, dorsal attention network and frontal–parietal network in GTCS. Meanwhile, the alterations in FC variability were relatively consistent across different methods and templates. Therefore, the consistent alteration of FC variability would reflect a dynamic restructuring of the large‐scale brain networks in patients with GTCS. Overly frequent information communication among cognition‐related networks, especially in the DMN, might play a role in the epileptic activity and/or cognitive dysfunction in patients.

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“…Meta-state analysis (see Window-based approaches (WBAs) for definition) also show reduced dynamism in CI-MS patients (d'Ambrosio et al, 2019). Atypical dFC patterns present in other cohorts and brain disorders, including migraine (Tu et al, 2019), stroke , epilepsy (Douw et al, 2015;Liu et al, 2017;Klugah-Brown et al, 2019), attention deficit hyperactivity disorder (ADHD) (Ou et al, 2014;de Lacy and Calhoun, 2019), post-traumatic stress disorder Jin et al, 2017), frontotemporal dementia (Premi et al, 2019), and Lewy body dementia (Schumacher et al, 2019).…”

Section: Dynamic Functional Connectivity (Time-varying Functional Patmentioning

confidence: 99%

Tools of the trade: Estimating time-varying connectivity patterns from fMRI data

Iraji¹,

Faghiri²,

Lewis³

et al. 2020

Preprint

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Given the dynamic nature of the brain, there has always been a motivation to move beyond “static” functional connectivity, which characterizes functional interactions over an extended period of time. Progress in data acquisition and advances in analytical neuroimaging methods now allow us to assess the whole brain’s dynamic functional connectivity (dFC) and its network-based analog, dynamic functional network connectivity (dFNC) at the macroscale (mm) using fMRI. This has resulted in the rapid growth of analytical approaches, some of which are very complex, requiring technical expertise that could daunt researchers and neuroscientists. Meanwhile, making real progress toward understanding the association between brain dynamism and brain disorders can only be achieved through research conducted by domain experts, such as neuroscientists and psychiatrists. This article aims to provide a gentle introduction to the application of dFC. We first explain what dFC is and the circumstances under which it can be used. Next, we review two major categories of analytical approaches to capture dFC. We discuss caveats and considerations in dFC analysis. Finally, we walk readers through an openly accessible toolbox to capture dFC properties and briefly review some of the dynamic metrics calculated using this toolbox.

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Altered Dynamic Functional Network Connectivity in Frontal Lobe Epilepsy

Cited by 41 publications

References 41 publications

Dysfunctional white‐matter networks in medicated and unmedicated benign epilepsy with centrotemporal spikes

Dysfunctional white‐matter networks in medicated and unmedicated benign epilepsy with centrotemporal spikes

Reconfiguration of dynamic large‐scale brain network functional connectivity in generalized tonic–clonic seizures

Tools of the trade: Estimating time-varying connectivity patterns from fMRI data

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