Frequency-dependent changes in local intrinsic oscillations in chronic primary insomnia: A study of the amplitude of low-frequency fluctuations in the resting state

Zhou, Fuqing; Huang, Su-Hua; Zhuang, Yi; Gao, Lei; Gong, Honghan

doi:10.1016/j.nicl.2016.05.011

Cited by 74 publications

(71 citation statements)

References 43 publications

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“…Convergent findings based on functional MR imaging support that spontaneous neural activity or FC in the insular cortex, prefrontal cortex and precuneus were disrupted in patients with insomnia or subjects with insomnia symptoms [6][7][8][9][10][11][12]. In line with previous findings, our study firstly showed that these regions also have high classification weights.…”

Section: Discussionsupporting

confidence: 91%

“…Neuroimaging studies for insomnia have made substantial effort to understand the neuromechanisms of insomnia. Previous studies found aberrant brain metabolism and connectivity related to the prefrontal cortex, insular cortex, amygdala, precuneus and caudate in primary insomnia [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. For example, using PET, Nofzinger et al [4] found smaller decrease in relative metabolism from waking to non-REM sleep states in the ascending reticular activating system, hypothalamus, thalamus, insular cortex, amygdala, hippocampus, anterior cingulate and medial prefrontal cortices, which supports the CNS hyperarousal hypothesis.…”

Section: Introductionmentioning

confidence: 74%

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Multivariate Pattern Classification of Primary Insomnia Using Three Types of Functional Connectivity Features

Dong

Yin

et al. 2019

Preprint

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Objective: To investigate whether or not functional connectivity (FC) could be used as a potential biomarker for classification of primary insomnia (PI) at the individual level by using multivariate pattern analysis (MVPA). Methods:Thirty-eight drug-naive patients with PI and 44 healthy controls (HC) underwent resting-state functional MR imaging. Three commonly used FC metrics (voxel-wise functional connectivity strength, large-scale functional connectivity and regional homogeneity) were calculated for each participant. We used the MVPA framework using linear support vector machine (SVM) with the three types of metrics as features separately. Subsequently, an unbiased N-fold cross-validation strategy was used to generate a classification system and was then used to evaluate its classification performances. Finally, FC metrics with significant high classification performance were compared between the two groups and were correlated with clinical characteristics, i.e., Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), Self-rating Anxiety Scale (SAS), Self-rating Depression Scale (SDS). Results:The best classifier could reach up to an accuracy of 81.5%, with sensitivity of 84.9%, specificity of 79.1% and area under the receiver operating characteristic curve (AUC) of 83.0% (all P < 0.001). Right fronto-insular cortex, left precuneus and left middle frontal gyrus showed high classification weights. In addition, right fronto-insular cortex and left middle frontal gyrus were the overlapping regions between MVPA and group comparison. Correlation analysis showed that functional connectivity strength (FCS) in left middle frontal gyrus and head of right caudate nucleus were correlated with PSQI and SDS respectively. Conclusion:The current study suggests abnormal FCS might serve as a potential neuromarkers for PI.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 74%

Multivariate Pattern Classification of Primary Insomnia Using Three Types of Functional Connectivity Features

Dong

Yin

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…One important strength of this study was assessing variability separately at slow4 and slow5. Frequency-dependent variability alterations in region-specific brain areas have been reported at slow4 and slow5 in different disorders [41][42][43][44][45][46] . These results suggest both disease-and frequency-dependent disruptions of variability patterns.…”

Section: Discussionmentioning

confidence: 99%

Distinct BOLD variability changes in the default mode and salience networks in Alzheimer’s disease spectrum and associations with cognitive decline

Zhang

Zuo

et al. 2020

Sci Rep

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However, it remains largely unknown how network-specific and frequency-specific variability changes along the Alzheimer's disease (AD) spectrum and relates to cognitive decline. We hypothesized that cognitive impairment was related to distinct BOLD variability alterations in two brain networks with reciprocal relationship, i.e., the AD-specific default mode network (DMN) and the salience network (SN). We examined variability of resting-state fMRI data at two characteristic slow frequency-bands of slow4 (0.027-0.073 Hz) and slow5 (0.01-0.027 Hz) in 96 AD, 98 amnestic mild cognitive impairment (aMCI), and 48 age-matched healthy controls (HC) using two commonly used pre-processing pipelines. Cognition was measured with a neuropsychological assessment battery. Using both global signal regression (GSR) and independent component analysis (ICA), results generally showed a reciprocal DMN-SN variability balance in aMCI (vs. AD and/or HC), although there were distinct frequency-specific variability patterns in association with different pre-processing approaches. Importantly, lower slow4 posterior-DMN variability correlated with poorer baseline cognition/smaller hippocampus and predicted faster cognitive decline in all patients using both GSR and ICA. Altogether, our findings suggest that reciprocal DMN-SN variability balance in aMCI might represent an early signature in neurodegeneration and cognitive decline along the AD spectrum.Alzheimer's disease (AD) is the major cause of dementia, and increasing attention has been focused on early disease detection/prevention. Therefore, studying brain changes along the AD disease continuum is important, i.e., from normal aging to the prodromal stage (amnestic mild cognitive impairment, aMCI) and finally to dementia stage. Using resting-state functional connectivity methods 1-5 that quantifies the temporal synchrony between brain regions, both AD and aMCI have been found to target large-scale networks, including reduced After ICA-based denoising, there was a main effect of group mainly in the posterior DMN including the precuneus/posterior cingulate cortex and VN (cuneus), and the SN (insula) ( Supplementary Table 1). Group comparisons replicated the GSR findings that aMCI had higher variability in the posterior DMN compared with AD (precuneus; Fig. 1B, bottom panel) and HC (angular gyrus ; Fig. 1A, bottom panel), as well as lower SN variability compared with HC (insula ; Fig. 1C, bottom panel), although the latter did not survive the cluster-level Scientific RepoRtS | (2020) 10:6457 | https://doi.Furthermore, we examined whether the variability related to global cognition decline over time, defined as the difference between baseline and year 2 (year 2 minus baseline).For all correlation analyses, we focused on the brain clusters showing group differences between aMCI and HC or between AD and HC (including clusters surviving the voxel-level threshold consistently using both GSR and ICA-based denoising) (n = 3 for slow4, and n = 6 for slow5 for both data denoising methods). Correlati...

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“…Second, most of alterations were not shared across sub‐bands, which implied a frequency‐dependent dysfunction connectivity of brain white matter in each sub‐band (Wang et al, ). Third, more differences were observed in the frequency band of Slow‐3 and Slow‐4, suggesting a correspondence between specific frequency and certain disease (Zhao, Tang et al, ; Zhou, Huang, Zhuang, Gao, & Gong, ). These inferences still need to be verified in future studies to elucidate frequency‐specific network organization mechanism.…”

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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Frequency-dependent changes in local intrinsic oscillations in chronic primary insomnia: A study of the amplitude of low-frequency fluctuations in the resting state

Cited by 74 publications

References 43 publications

Multivariate Pattern Classification of Primary Insomnia Using Three Types of Functional Connectivity Features

Multivariate Pattern Classification of Primary Insomnia Using Three Types of Functional Connectivity Features

Distinct BOLD variability changes in the default mode and salience networks in Alzheimer’s disease spectrum and associations with cognitive decline

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

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