scholarly journals Globally Aging Cortical Spontaneous Activity Revealed by Multiple Metrics and Frequency Bands Using Resting-State Functional MRI

2021 ◽  
Vol 13 ◽  
Author(s):  
Xiu-Xia Xing
Keyword(s):  
Spontaneous Activity ◽  
Functional Mri ◽  
Resting State ◽  
Resting State Fmri ◽  
Cross Sectional ◽  
Frequency Bands ◽  
Age Related ◽  
Additive Mixed Models ◽  
Specific Frequency ◽  
Multiple Metrics

Most existing aging studies using functional MRI (fMRI) are based on cross-sectional data but misinterpreted their findings (i.e., age-related differences) as longitudinal outcomes (i.e., aging-related changes). To delineate aging-related changes the of human cerebral cortex, we employed the resting-state fMRI (rsfMRI) data from 24 healthy elders in the PREVENT-AD cohort, obtaining five longitudinal scans per subject. Cortical spontaneous activity is measured globally with three rsfMRI metrics including its amplitude, homogeneity, and homotopy at three different frequency bands (slow-5: 0.02–0.03 Hz, slow-4: 0.03–0.08 Hz, and slow-3 band: 0.08–0.22 Hz). General additive mixed models revealed a universal pattern of the aging-related changes for the global cortical spontaneous activity, indicating increases of these rsfMRI metrics during aging. This aging pattern follows specific frequency and spatial profiles where higher slow bands show more non-linear curves and the amplitude exhibits more extensive and significant aging-related changes than the connectivity. These findings provide strong evidence that cortical spontaneous activity is aging globally, inspiring its clinical utility as neuroimaging markers for neruodegeneration disorders.

scholarly journals Neuroimaging Anomalies in Community-Dwelling Asymptomatic Adults With Very Early-Stage White Matter Hyperintensity

2021 ◽  
Vol 13 ◽  
Author(s):  
Shuai Guan ◽  
Xiangyu Kong ◽  
Shifei Duan ◽  
Qingguo Ren ◽  
Zhaodi Huang ◽  
...  
Keyword(s):  
White Matter ◽  
Functional Mri ◽  
Resting State ◽  
Early Stage ◽  
Structural Mri ◽  
Resting State Fmri ◽  
Community Dwelling ◽  
White Matter Hyperintensity ◽  
Age Related ◽  
Comparative Results

White matter hyperintensity (WMH) is common in healthy adults in their 60s and can be seen as early as in their 30s and 40s. Alterations in the brain structural and functional profiles in adults with WMH have been repeatedly studied but with a focus on late-stage WMH. To date, structural and functional MRI profiles during the very early stage of WMH remain largely unexplored. To address this, we investigated multimodal MRI (structural, diffusion, and resting-state functional MRI) profiles of community-dwelling asymptomatic adults with very early-stage WMH relative to age-, sex-, and education-matched non-WMH controls. The comparative results showed significant age-related and age-independent changes in structural MRI-based morphometric measures and resting-state fMRI-based measures in a set of specific gray matter (GM) regions but no global white matter changes. The observed structural and functional anomalies in specific GM regions in community-dwelling asymptomatic adults with very early-stage WMH provide novel data regarding very early-stage WMH and enhance understanding of the pathogenesis of WMH.

scholarly journals The effect of global signal regression on DCM estimates of noise and effective connectivity from resting state fMRI

2019 ◽  
Author(s):  
Hannes Almgren ◽  
Frederik Van de Steen ◽  
Adeel Razi ◽  
Karl Friston ◽  
Daniele Marinazzo
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Effective Connectivity ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Measurement Noise ◽  
Resting State Networks ◽  
Cross Sectional ◽  
Global Signal ◽  
Leibler Divergence

AbstractThe influence of the global BOLD signal on resting state functional connectivity in fMRI data remains a topic of debate, with little consensus. In this study, we assessed the effects of global signal regression (GSR) on effective connectivity within and between resting-state networks – as estimated with dynamic causal modelling (DCM) for resting state fMRI (rsfMRI). DCM incorporates a forward (generative) model that quantifies the contribution of different types of noise (including global measurement noise), effective connectivity, and (neuro)vascular processes to functional connectivity measurements. DCM analyses were applied to two different designs; namely, longitudinal and cross-sectional designs. In the modelling of longitudinal designs, we included four extensive longitudinal resting state fMRI datasets with a total number of 20 subjects. In the analysis of cross-sectional designs, we used rsfMRI data from 361 subjects from the Human Connectome Project. We hypothesized that (1) GSR would have no discernible impact on effective connectivity estimated with DCM, and (2) GSR would be reflected in the parameters representing global measurement noise. Additionally, we performed comparative analyses of the informative value of data with and without GSR. Our results showed negligible to small effects of GSR on connectivity within small (separately estimated) RSNs. For between-network connectivity, we found two important effects: the effect of GSR on between-network connectivity (averaged over all connections) was negligible to small, while the effect of GSR on individual connections was non-negligible. Contrary to our expectations, we found either no effect (in the longitudinal designs) or a non-specific (cross-sectional design) effect of GSR on parameters representing (global) measurement noise. Data without GSR were found to be more informative than data with GSR; however, in small resting state networks the precision of posterior estimates was greater using data after GSR. In conclusion, GSR is a minor concern in DCM studies; however, individual between-network connections (as opposed to average between-network connectivity) and noise parameters should be interpreted quantitatively with some caution. The Kullback-Leibler divergence of the posterior from the prior, together with the precision of posterior estimates, might offer a useful measure to assess the appropriateness of GSR, when nuancing data features in resting state fMRI.

Altered brain spontaneous activity and connectivity network in irritable bowel syndrome patients: A resting-state fMRI study

2015 ◽  
Vol 126 (6) ◽  
pp. 1190-1197 ◽  
Author(s):  
Xiaofen Ma ◽  
Shumei Li ◽  
Junzhang Tian ◽  
Guihua Jiang ◽  
Hua Wen ◽  
...  
Keyword(s):  
Irritable Bowel Syndrome ◽  
Spontaneous Activity ◽  
Resting State ◽  
Resting State Fmri ◽  
Fmri Study ◽  
Irritable Bowel

Liver transplantation nearly normalizes brain spontaneous activity and cognitive function at 1 month: a resting-state functional MRI study

2015 ◽  
Vol 30 (4) ◽  
pp. 979-988 ◽  
Author(s):  
Yue Cheng ◽  
Lixiang Huang ◽  
Xiaodong Zhang ◽  
Jianhui Zhong ◽  
Qian Ji ◽  
...  
Keyword(s):  
Liver Transplantation ◽  
Cognitive Function ◽  
Spontaneous Activity ◽  
Functional Mri ◽  
Resting State ◽  
Mri Study

scholarly journals Functional MRI, resting state fMRI, and DTI for predicting verbal fluency outcome following resective surgery for temporal lobe epilepsy

2016 ◽  
Vol 124 (4) ◽  
pp. 929-937 ◽  
Author(s):  
Karol Osipowicz ◽  
Michael R. Sperling ◽  
Ashwini D. Sharan ◽  
Joseph I. Tracy
Keyword(s):  
Temporal Lobe Epilepsy ◽  
Functional Mri ◽  
Temporal Lobe ◽  
Resting State ◽  
Verbal Fluency ◽  
Resting State Fmri ◽  
Cognitive Outcome ◽  
Temporal Lobectomy ◽  
Anterior Temporal Lobectomy ◽  
Resective Surgery

OBJECT Predicting cognitive function following resective surgery remains an important clinical goal. Each MRI neuroimaging technique can potentially provide unique and distinct insight into changes that occur in the structural or functional organization of “at-risk” cognitive functions. The authors tested for the singular and combined power of 3 imaging techniques (functional MRI [fMRI], resting state fMRI, diffusion tensor imaging) to predict cognitive outcome following left (dominant) anterior temporal lobectomy for intractable epilepsy. METHODS The authors calculated the degree of deviation from normal, determined the rate of change in this measure across the pre- and postsurgical imaging sessions, and then compared these measures for their ability to predict verbal fluency changes following surgery. RESULTS The data show that the 3 neuroimaging techniques, in a combined model, can reliably predict cognitive outcome following anterior temporal lobectomy for medically intractable temporal lobe epilepsy. CONCLUSIONS These findings suggest that these 3 imaging modalities can be used effectively, in an additive fashion, to predict functional reorganization and cognitive outcome following anterior temporal lobectomy.

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