Different topological organization of human brain functional networks with eyes open versus eyes closed

Xu, Pengfei; Huang, Ruiwang; Wang, Jinhui; Dam, Nicholas T. Van; Xie, Tian; Dong, Zhang-Ye; Chen, Chun-Ping; Gu, Ruolei; Zang, Yu‐Feng; He, Yong; Fan, Jin; Luo, Yuejia

doi:10.1016/j.neuroimage.2013.12.060

Cited by 147 publications

(145 citation statements)

References 93 publications

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“…Previous studies have been able to find all networks in both states (Xu et al, 2014). Patriat et al (2013) reported differences between states, but comparison to this study is difficult as they used correlation between regions of interest rather than per-voxel metrics in defined networks.…”

Section: Comparison With Previous Studiesmentioning

confidence: 85%

“…However, unlike this study, those studies were interested in local differences so justifiably discarded the mean of each R-fMRI metric with each conditions through performing statistical parametric mapping using the general linear model (Bianciardi et al, 2009;Brandt, 2006;Gonzalez-Hernandez et al, 2005;Marx et al, 2004;Qin et al, 2012;Riedl et al, 2014;Wicker et al, 2003), performing global signal regression to find local significance (Xu et al, 2014;Yuan et al, 2014;Zou et al, 2009), or using paired T-tests on a per voxel basis (which can hypothetically detect a global change if region-to-region variation is small, but will not necessarily as it only compares per-voxel changes and global means are never calculated) (Patriat et al, 2013). All of these methods will amplify local region to region, voxel to voxel variation, but obscure the relations to the global change observed in FDG (Fig.…”

Section: Comparison With Previous Studiesmentioning

confidence: 99%

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The Whole-Brain “Global” Signal from Resting State fMRI as a Potential Biomarker of Quantitative State Changes in Glucose Metabolism

et al. 2016

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The evolution of functional magnetic resonance imaging to resting state (R-fMRI) allows measurement of changes in brain networks attributed to state changes, such as in neuropsychiatric diseases versus healthy controls. Since these networks are observed by comparing normalized R-fMRI signals, it is difficult to determine the metabolic basis of such group differences. To investigate the metabolic basis of R-fMRI network differences within a normal range, eyes open versus eyes closed in healthy human subjects was used. R-fMRI was recorded simultaneously with fluoro-deoxyglucose positron emission tomography (FDG-PET). Higher baseline FDG was observed in the eyes open state. Variance-based metrics calculated from R-fMRI did not match the baseline shift in FDG. Functional connectivity density (FCD)-based metrics showed a shift similar to the baseline shift of FDG, however, this was lost if R-fMRI ''nuisance signals'' were regressed before FCD calculation. Average correlation with the mean R-fMRI signal across the whole brain, generally regarded as a ''nuisance signal,'' also showed a shift similar to the baseline of FDG. Thus, despite lacking a baseline itself, changes in whole-brain correlation may reflect changes in baseline brain metabolism. Conversely, variance-based metrics may remain similar between states due to inherent region-to-region differences overwhelming the differences between normal physiological states. As most previous studies have excluded the spatial means of R-fMRI metrics from their analysis, this work presents the first evidence of a potential R-fMRI biomarker for baseline shifts in quantifiable metabolism between brain states.

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Section: Comparison With Previous Studiesmentioning

confidence: 85%

Section: Comparison With Previous Studiesmentioning

confidence: 99%

The Whole-Brain “Global” Signal from Resting State fMRI as a Potential Biomarker of Quantitative State Changes in Glucose Metabolism

et al. 2016

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“…In addition, the use of global connectivity measures with 266 nodes could miss more focused abnormalities. An additional limitation may be the instructions provided to the subjects in that 'eyes open' vs 'eyes closed' have yielded different findings in the literature (Xu et al, 2014;Patriat et al, 2013), although in this study all subjects were provided with the same instructions. An additional limitation is that our findings may be influenced by factors such as substance use or smoking history.…”

Section: Discussionmentioning

confidence: 95%

Abnormal Resting State fMRI Activity Predicts Processing Speed Deficits in First-Episode Psychosis

Gallego

Robinson

Ikuta

et al. 2015

Neuropsychopharmacol

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Little is known regarding the neuropsychological significance of resting state functional magnetic resonance imaging (rs-fMRI) activity early in the course of psychosis. Moreover, no studies have used different approaches for analysis of rs-fMRI activity and examined gray matter thickness in the same cohort. In this study, 41 patients experiencing a first-episode of psychosis (including N ¼ 17 who were antipsychotic drug-naive at the time of scanning) and 41 individually age-and sex-matched healthy volunteers completed rs-fMRI and structural MRI exams and neuropsychological assessments. We computed correlation matrices for 266 regions-of-interest across the brain to assess global connectivity. In addition, independent component analysis (ICA) was used to assess group differences in the expression of rs-fMRI activity within 20 predefined publicly available templates. Patients demonstrated lower overall rs-fMRI global connectivity compared with healthy volunteers without associated group differences in gray matter thickness assessed within the same regions-of-interest used in this analysis. Similarly, ICA revealed worse rs-fMRI expression scores across all 20 networks in patients compared with healthy volunteers, with posthoc analyses revealing significant (po0.05; corrected) abnormalities within the caudate nucleus and planum temporale. Worse processing speed correlated significantly with overall lower global connectivity using the region-of-interest approach and lower expression scores within the planum temporale using ICA. Our findings implicate dysfunction in rs-fMRI activity in first-episode psychosis prior to extensive antipsychotic treatment using different analytic approaches (in the absence of concomitant gray matter structural differences) that predict processing speed.

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“…We chose the closed-eyes modality to avoid highly noisy signals coming from the visual cortex [53] and to facilitate attention during interoceptive processing [54,55]. Moreover, this time window enabled us to obtain enough signal points for data analysis and to ensure that the patients would go through the whole protocol (see electronic supplementary material, §1.3.1).…”

Section: (C) Image Analysis (I) Image Acquisitionmentioning

confidence: 99%

Feeling, learning from and being aware of inner states: interoceptive dimensions in neurodegeneration and stroke

García‐Cordero

Sedeño

Fuente

et al. 2016

Phil. Trans. R. Soc. B

135

259

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Different topological organization of human brain functional networks with eyes open versus eyes closed

Cited by 147 publications

References 93 publications

The Whole-Brain “Global” Signal from Resting State fMRI as a Potential Biomarker of Quantitative State Changes in Glucose Metabolism

The Whole-Brain “Global” Signal from Resting State fMRI as a Potential Biomarker of Quantitative State Changes in Glucose Metabolism

Abnormal Resting State fMRI Activity Predicts Processing Speed Deficits in First-Episode Psychosis

Feeling, learning from and being aware of inner states: interoceptive dimensions in neurodegeneration and stroke

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