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"Dynamic functional connectivity"

Martin Lamos, Radek Marecek, Tomáš Slavíček, Michal Mikl, Ivan Rektor, Jiri Jan
Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during EEG data analysis may leave part of the neural activity unrecognized. We propose an approach that blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity.
 Approach. The blind decomposition of EEG spectrogram by Parallel Factor Analysis has been shown to be a useful technique for uncovering patterns of neural activity...
March 14, 2018: Journal of Neural Engineering
María Díez-Cirarda, Antonio P Strafella, Jinhee Kim, Javier Peña, Natalia Ojeda, Alberto Cabrera-Zubizarreta, Naroa Ibarretxe-Bilbao
The objective was to assess dynamic functional connectivity (FC) and local/global connectivity in Parkinson's disease (PD) patients with mild cognitive impairment (PD-MCI) and with normal cognition (PD-NC). The sample included 35 PD patients and 26 healthy controls (HC). Cognitive assessment followed an extensive neuropsychological battery. For resting-state functional MRI (rs-fMRI) analysis, independent component analysis (ICA) was performed and components were located in 7 networks: Subcortical (SC), Auditory (AUD), Somatomotor (SM), visual (VI), cognitive-control (CC), default-mode (DMN), and cerebellar (CB)...
2018: NeuroImage: Clinical
Huaze Xu, Jianpo Su, Jian Qin, Ming Li, Ling-Li Zeng, Dewen Hu, Hui Shen
Recently, resting-state functional magnetic resonance imaging (fMRI) studies have been extended to explore fluctuations in correlations over shorter timescales, referred to as dynamic functional connectivity (dFC). However, the impact of global signal regression (GSR) on dFC is not well established, despite the intensive investigations of the influence of GSR on static functional connectivity (sFC). This study aimed to examine the effect of GSR on the performance of the sliding-window correlation, a commonly used method for capturing functional connectivity (FC) dynamics based on resting-state fMRI and simultaneous electroencephalograph (EEG)-fMRI data...
February 21, 2018: NeuroImage
Xu Han, Xiaowei Wu, Yao Wang, Yawen Sun, Weina Ding, Mengqiu Cao, Yasong Du, Fuchun Lin, Yan Zhou
Internet gaming disorder (IGD), a major behavior disorder, has gained increasing attention. Recent studies indicate altered resting-state static functional connectivity (FC) of the dorsolateral prefrontal cortex (DLPFC) in subjects with IGD. Whereas static FC often provides information on functional changes in subjects with IGD, investigations of temporal changes in FC between the DLPFC and the other brain regions may shed light on the dynamic characteristics of brain function associated with IGD. Thirty subjects with IGD and 30 healthy controls (HCs) matched for age, gender and education status were recruited...
2018: Frontiers in Human Neuroscience
Lixia Tian, Qizhuo Li, Chaomurilige Wang, Jian Yu
Despite numerous studies on age-related changes in static functional connections (FCs), the available literature on the changes in dynamic FCs with aging is lacking. This study investigated the changes in dynamic FCs with aging based on resting state fMRI data of 61 healthy adults aged 30-85 years. The time-resolved FCs among 160 pre-defined regions of interest (ROIs) were first estimated using sliding-window correlation. Based on the dynamic FC matrices, we then analyzed the dynamic switches between different FC states using k-means clustering, and correlated age with the dwell time of each FC state across subjects...
January 27, 2018: NeuroImage
Amy L Proskovec, Elizabeth Heinrichs-Graham, Alex I Wiesman, Timothy J McDermott, Tony W Wilson
The ability to reorient attention within the visual field is central to daily functioning, and numerous fMRI studies have shown that the dorsal and ventral attention networks (DAN, VAN) are critical to such processes. However, despite the instantaneous nature of attentional shifts, the dynamics of oscillatory activity serving attentional reorientation remain poorly characterized. In this study, we utilized magnetoencephalography (MEG) and a Posner task to probe the dynamics of attentional reorienting in 29 healthy adults...
February 6, 2018: Human Brain Mapping
D Arnone, T Wise, C Walker, P J Cowen, O Howes, S Selvaraj
BACKGROUND: Static and dynamic functional connectivity are being increasingly used to measure the effects of disease and a range of different interventions on brain networks. While preliminary evidence suggests that static connectivity can be modulated by chronic antidepressants administration in healthy individuals and in major depression, much less is known about the acute effects of antidepressants especially on dynamic functional connectivity changes. Here we examine acute effects of antidepressants on dynamic functional connectivity within the default mode network...
January 31, 2018: Progress in Neuro-psychopharmacology & Biological Psychiatry
Yuanyuan Chen, Xin Zhao, Xiong Zhang, Ya'nan Liu, Peng Zhou, Hongyan Ni, Jianguo Ma, Dong Ming
PURPOSE: Many questions remain regarding how the brain develops, matures, and ages across the lifespan. The functional connectivity networks in the resting-state brain can reflect many of the characteristic changes in the brain that are associated with increasing age. Functional connectivity has been shown to be time-dependent over the course of a lifespan and even over the course of minutes. The lifespan strategies of all cognitive networks and how dynamic functional connectivity is associated with age are unclear...
January 30, 2018: Neuroradiology
Ashish Kaul Sahib, Michael Erb, Justus Marquetand, Pascal Martin, Adham Elshahabi, Silke Klamer, Serge Vulliemoz, Klaus Scheffler, Thomas Ethofer, Niels K Focke
The human brain is known to contain several functional networks that interact dynamically. Therefore, it is desirable to analyze the temporal features of these networks by dynamic functional connectivity (dFC). A sliding window approach was used in an event-related fMRI (visual stimulation using checkerboards) to assess the impact of repetition time (TR) and window size on the temporal features of BOLD dFC. In addition, we also examined the spatial distribution of dFC and tested the feasibility of this approach for the analysis of interictal epileptiforme discharges...
2018: PloS One
Frigyes Samuel Racz, Peter Mukli, Zoltan Nagy, Andras Eke
Brain function is organized as a network of functional connections between different neuronal populations with connection strengths dynamically changing in time and space. Studies investigating functional connectivity (FC) usually follow a static approach when describing FC by considering the connectivity strengths constant, however a dynamic approach seems more reasonable, as this way the spatio-temporal dynamics of the underlying system can also be captured. Objective: The scale-free, i.e. fractal nature of neural dynamics is an inherent property of the nervous system...
January 19, 2018: Physiological Measurement
Sharon Chiang, Emilian R Vankov, Hsiang J Yeh, Michele Guindani, Marina Vannucci, Zulfi Haneef, John M Stern
Estimation of functional connectivity (FC) has become an increasingly powerful tool for investigating healthy and abnormal brain function. Static connectivity, in particular, has played a large part in guiding conclusions from the majority of resting-state functional MRI studies. However, accumulating evidence points to the presence of temporal fluctuations in FC, leading to increasing interest in estimating FC as a dynamic quantity. One central issue that has arisen in this new view of connectivity is the dramatic increase in complexity caused by dynamic functional connectivity (dFC) estimation...
2018: PloS One
David Clewett, Ringo Huang, Rico Velasco, Tae-Ho Lee, Mara Mather
Recent models posit that bursts of locus coeruleus (LC) activity amplify neural gain, such that limited attention and encoding resources focus even more on prioritized mental representations under arousal. Here, we tested this hypothesis in human males and females using functional magnetic resonance imaging (fMRI), neuromelanin MRI, and pupil dilation, a biomarker of arousal and LC activity. During scanning, participants performed a monetary incentive encoding task in which threat of punishment motivated participants to prioritize encoding of scene images over superimposed objects...
January 4, 2018: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
Mahshid Najafi, Joshua Kinnison, Luiz Pessoa
How do large-scale brain networks reorganize during the waxing and waning of anxious anticipation? Here, threat was dynamically modulated during human functional MRI as two circles slowly meandered on the screen; if they touched, an unpleasant shock was delivered. We employed intersubject correlation analysis, which allowed the investigation of network-level functional connectivity across brains, and sought to determine how network connectivity changed during periods of approach (circles moving closer) and periods of retreat (circles moving apart)...
2017: Frontiers in Human Neuroscience
Rong Li, Wei Liao, Yangyang Yu, Heng Chen, Xiaonan Guo, Ye-Lei Tang, Huafu Chen
Benign epilepsy with centrotemporal spikes (BECTS) is characterized by abnormal (static) functional interactions among cortical and subcortical regions, regardless of the active or chronic epileptic state. However, human brain connectivity is dynamic and associated with ongoing rhythmic activity. The dynamic functional connectivity (dFC) of the distinct striato-cortical circuitry associated with or without interictal epileptiform discharges (IEDs) are poorly understood in BECTS. Herein, we captured the pattern of dFC using sliding window correlation of putamen subregions in the BECTS (without IEDs, n = 23; with IEDs, n = 20) and sex- and age-matched healthy controls (HCs, n = 28) during rest...
March 2018: Human Brain Mapping
Ashkan Faghiri, Julia M Stephen, Yu-Ping Wang, Tony W Wilson, Vince D Calhoun
Brain maturation through adolescence has been the topic of recent studies. Previous works have evaluated changes in morphometry and also changes in functional connectivity. However, most resting-state fMRI studies have focused on static connectivity. Here we examine the relationship between age/maturity and the dynamics of brain functional connectivity. Utilizing a resting fMRI dataset comprised 421 subjects ages 3-22 from the PING study, we first performed group ICA to extract independent components and their time courses...
March 2018: Human Brain Mapping
Huaze Xu, Hui Shen, Lubin Wang, Qi Zhong, Yu Lei, Liu Yang, Ling-Li Zeng, Zongtan Zhou, Dewen Hu, Zheng Yang
Resting-state functional magnetic resonance imaging (fMRI) studies using static functional connectivity (FC) measures have shown that the brain function is severely disrupted after long-term sleep deprivation (SD). However, increasing evidence has suggested that resting-state FC is dynamic and exhibits spontaneous fluctuation on a smaller timescale. The process by which long-term SD can influence dynamic functional connectivity (dynFC) remains unclear. In this study, 37 healthy subjects participated in the SD experiment, and they were scanned both during rested wakefulness (RW) and after 36 hours of SD...
November 22, 2017: Brain Research
Anees Abrol, Barnaly Rashid, Srinivas Rachakonda, Eswar Damaraju, Vince D Calhoun
Studies featuring multimodal neuroimaging data fusion for understanding brain function and structure, or disease characterization, leverage the partial information available in each of the modalities to reveal data variations not exhibited through the independent analyses. Similar to other complex syndromes, the characteristic brain abnormalities in schizophrenia may be better understood with the help of the additional information conveyed by leveraging an advanced modeling method involving multiple modalities...
2017: Frontiers in Neuroscience
Hae-Jeong Park, Karl J Friston, Chongwon Pae, Bumhee Park, Adeel Razi
Context-sensitive and activity-dependent fluctuations in connectivity underlie functional integration in the brain and have been studied widely in terms of synaptic plasticity, learning and condition-specific (e.g., attentional) modulations of synaptic efficacy. This dynamic aspect of brain connectivity has recently attracted a lot of attention in the resting state fMRI community. To explain dynamic functional connectivity in terms of directed effective connectivity among brain regions, we introduce a novel method to identify dynamic effective connectivity using spectral dynamic causal modelling (spDCM)...
November 20, 2017: NeuroImage
Shuixia Guo, Wei Zhao, Haojuan Tao, Zhening Liu, Lena Palaniyappan
BACKGROUND: The distributed connectivity among brain regions is in a constant state of flux, even when a subject is at rest. This instability (temporal variability), when optimal, may contribute to efficient cross-network communications. We investigate the role of this variability in the genetic diathesis and symptom expression of schizophrenia. METHODS: Resting state functional MRI data acquired from 116 subjects (28 patients with schizophrenia, 28 siblings and 60 matched healthy controls)...
November 15, 2017: Schizophrenia Research
Zack Y Shan, Kevin Finegan, Sandeep Bhuta, Timothy Ireland, Donald R Staines, Sonya M Marshall-Gradisnik, Leighton R Barnden
The chronic fatigue syndrome / myalgic encephalomyelitis (CFS) is a debilitating disease with unknown pathophysiology and no diagnostic test. This study investigated the default mode network (DMN) in order to understand the pathophysiology of CFS and to identify potential biomarkers. Using functional MRI (fMRI) collected from 72 subjects (45 CFS and 27 controls) with a temporal resolution of 0.798s, we evaluated the default mode network using static functional connectivity (FC), dynamic functional connectivity (DFC) and DFC complexity, blood oxygenation level dependent (BOLD) activation maps and complexity of activity...
November 20, 2017: Brain Connectivity
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