Disentangling resting-state BOLD variability and PCC functional connectivity in 22q11.2 deletion syndrome

Zöller, Daniela; Schaer, Marie; Scariati, Elisa; Padula, Maria Carmela; Eliez, Stéphan; Ville, Dimitri Van De

doi:10.1016/j.neuroimage.2017.01.064

Cited by 70 publications

(88 citation statements)

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“…For example, it has been demonstrated that the variance of the task-evoked BOLD response was differentially related to aging as well as cognitive performance (Armbruster-Genc et al, 2016;Garrett et al, 2013a). Similarly, spontaneous signal variability in resting state fMRI (rsfMRI) has been associated with age (Grady and Garrett, 2018;Nomi et al, 2017), emotional state (state anxiety; Labrenz et al, 2018), and mental or neural disorders such as stroke (Kielar et al, 2016), Attention Deficit Hyperactivity Disorder (Nomi et al, 2018) or 22q11.2 deletion syndrome (Zöller et al, 2017). From these studies, it was concluded that reductions in BOLD signal variability might serve as an index for deficits in neural processing and cognitive flexibility (Grady and Garrett, 2014).…”

Section: Introductionmentioning

confidence: 99%

BOLD and EEG Signal Variability at Rest Differently Relate to Aging in the Human Brain

Kumral

Sansal

Cesnaite

et al. 2019

Preprint

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Variability of neural activity is regarded as a crucial feature of healthy brain function, and several neuroimaging approaches have been employed to assess it noninvasively. Studies on the variability of both evoked brain response and spontaneous brain signals have shown remarkable changes with aging but it is unclear if the different measures of brain signal variability -identified with either hemodynamic or electrophysiological methods -reflect the same underlying physiology. In this study, we aimed to explore age differences of spontaneous brain signal variability with two different imaging modalities (EEG, fMRI) in healthy younger (25±3 years, N=135) and older (67±4 years, N=54) adults. Consistent with the previous studies, we found lower blood oxygenation level dependent (BOLD) variability in the older subjects as well as less signal variability in the amplitude of low-frequency oscillations (1-12 Hz), measured in source space. These age-related reductions were mostly observed in the areas that overlap with the default mode network. Moreover, age-related increases of variability in the amplitude of beta-band frequency EEG oscillations (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25) were seen predominantly in fronto-temporal and sensorimotor brain regions. There were significant sex differences in BOLD and EEG signal variability in various brain regions, but no significant interactions between age and sex were observed. Further, both univariate and multivariate correlation analyses revealed no significant associations between these two variability measures. In summary, we show that both BOLD and EEG signal variability reflect aging-related processes but are likely to be dominated by different physiological origins, which relate differentially to age and sex.

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Section: Introductionmentioning

confidence: 99%

BOLD and EEG Signal Variability at Rest Differently Relate to Aging in the Human Brain

Kumral

Sansal

Cesnaite

et al. 2019

Preprint

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“…In a most recent study, increased variability in the SN including the insula and decreased variability in most of the other brain regions have been found across life span 19 . Moreover, generally reduced variability has been found in healthy elderly compared with healthy young individuals 20 , and also in neuropsychiatric disorders (e.g., traumatic brain injury, psychosis and bipolar disorder) [21][22][23] . These results suggest that variability could be a promising and effective measure to reflect disturbed brain functioning.…”

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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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“…These correlation analyses highlight latent genetic relationships between various personality traits. Exploiting the intercorrelation between personality traits using partial least squares 45,46 we observed a robust latent factor of personality and cortical thickness. Notably, this factor was predominantly driven by openness, followed by extraversion.…”

Section: Discussionmentioning

confidence: 95%

“…Exploiting the intercorrelation between personality traits using partial least squares (McIntosh AR and NJ Lobaugh 2004;Zoller D et al 2017) we observed a robust latent factor of personality and cortical thickness. Notably, this factor was predominantly driven by openness, followed by extraversion.…”

Section: Discussionmentioning

confidence: 95%

“…PLS is a multivariate data-driven statistical technique that aims to maximize the covariance between two matrices by deriving latent components (LCs), which are optimal linear combinations of the original matrices (McIntosh AR and NJ Lobaugh 2004;Zoller D et al 2017). We applied PLS to the cortical thickness and personality measures of all participants.…”

Section: Partial Least Squaresmentioning

confidence: 99%

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Personality and local brain structure: their shared genetic basis and reproducibility

Valk

Hoffstaedter

Camilleri

et al. 2019

Preprint

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33Personality traits are key indices of inter-individual variation. Personality is heritable and 34 associated with brain structure and function. To date, it is unknown whether the relation 35 between personality and brain macrostructure can be explained by shared genetic factors. In a 36 large-scale twin sample, we show that key personality traits, e.g. agreeableness and 37 extraversion, were genetically associated with thickness of specific brain regions. Second, we 38 observed that spatial patterns of phenotypic and genetic correlations between personality and 39 cortical thickness were significantly positive, suggesting a genetic basis for phenotypic 40 associations. However, these findings did not replicate in two large-scale datasets of unrelated 41 individuals. We were able to reconcile this discrepancy by probing the latent relation between 42 personality and cortical thickness using a multivariate approach. Here we could uncover a 43 latent association between personality traits and cortical thickness. This component accounted 44 for 30% of the variance shared between personality traits and cortical thickness. The results of 45 our multivariate analysis indicate that personality is associated with regions supporting lower-46 order sensory as well as integrative cognitive processes. Our study provides first evidence of a 47 "Personality" is an individuals' characteristic set of cognitive, emotional and behavioral 52

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Disentangling resting-state BOLD variability and PCC functional connectivity in 22q11.2 deletion syndrome

Cited by 70 publications

References 68 publications

BOLD and EEG Signal Variability at Rest Differently Relate to Aging in the Human Brain

BOLD and EEG Signal Variability at Rest Differently Relate to Aging in the Human Brain

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

Personality and local brain structure: their shared genetic basis and reproducibility

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