Investigating univariate temporal patterns for intrinsic connectivity networks based on complexity and low-frequency oscillation: A test–retest reliability study

Wang, X.; Yun, Jimmy; Tang, Tianyu; Wang, H.; Zhang, Lu

doi:10.1016/j.neuroscience.2013.09.009

Cited by 27 publications

(32 citation statements)

References 61 publications

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“…The variances of ALFF/LFO amplitude could provide the intrinsic information base on bias input selection, temporally link neurons into assemblies, and facilitate synaptic plasticity [12], [13], while task related fMRI mainly reflect the response capability of neuron. As an important aspect of neural activity, some studies have confirmed that the ALFF method directly measures the LFO amplitude with high reliability and reproducibility both in the inter-session and intra-session fMRI scans [12], [14], [15].…”

Section: Discussionmentioning

confidence: 95%

Increased Low-Frequency Oscillation Amplitude of Sensorimotor Cortex Associated with the Severity of Structural Impairment in Cervical Myelopathy

et al. 2014

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Decreases in metabolites and increased motor-related, but decreased sensory-related activation of the sensorimotor cortex (SMC) have been observed in patients with cervical myelopathy (CM) using advanced MRI techniques. However, the nature of intrinsic neuronal activity in the SMC, and the relationship between cerebral function and structural damage of the spinal cord in patients with CM are not fully understood. The purpose of this study was to assess intrinsic neuronal activity by calculating the regional amplitude of low frequency fluctuations (ALFF) using resting-state functional MRI (rs-fMRI), and correlations with clinical and imaging indices. Nineteen patients and 19 age- and sex-matched healthy subjects underwent rs-fMRI scans. ALFF measurements were performed in the SMC, a key brain network likely to impaired or reorganized patients with CM. Compared with healthy subjects, increased amplitude of cortical low-frequency oscillations (LFO) was observed in the right precentral gyrus, right postcentral gyrus, and left supplementary motor area. Furthermore, increased z-ALFF values in the right precentral gyrus and right postcentral gyrus correlated with decreased fractional anisotropy values at the C2 level, which indicated increased intrinsic neuronal activity in the SMC corresponding to the structural impairment in the spinal cord of patients with CM. These findings suggest a complex and diverging relationship of cortical functional reorganization and distal spinal anatomical compression in patients with CM and, thus, add important information in understanding how spinal cord integrity may be a factor in the intrinsic covariance of spontaneous low-frequency fluctuations of BOLD signals involved in cortical plasticity.

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Section: Discussionmentioning

confidence: 95%

Increased Low-Frequency Oscillation Amplitude of Sensorimotor Cortex Associated with the Severity of Structural Impairment in Cervical Myelopathy

et al. 2014

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“…In the context of other published findings, there have been a few studies that have assessed dual regression for back-reconstructing SSTC and SSSM for test-retest reliability and to assess performance in the presence of shape and amplitude differences (Zuo et al, 2010; Allen et al, 2012; Wang et al, 2013; Wisner et al, 2013). In the study by Zuo et al (2010), they found that GICA-DR produced subject-specific brain network spatial patterns that had moderate-to-high reliability and reproducibility.…”

Section: Discussionmentioning

confidence: 99%

Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses

et al. 2017

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Independent Component Analysis (ICA) is one of the most popular techniques for the analysis of resting state FMRI data because it has several advantageous properties when compared with other techniques. Most notably, in contrast to a conventional seed-based correlation analysis, it is model-free and multivariate, thus switching the focus from evaluating the functional connectivity of single brain regions identified a priori to evaluating brain connectivity in terms of all brain resting state networks (RSNs) that simultaneously engage in oscillatory activity. Furthermore, typical seed-based analysis characterizes RSNs in terms of spatially distributed patterns of correlation (typically by means of simple Pearson's coefficients) and thereby confounds together amplitude information of oscillatory activity and noise. ICA and other regression techniques, on the other hand, retain magnitude information and therefore can be sensitive to both changes in the spatially distributed nature of correlations (differences in the spatial pattern or “shape”) as well as the amplitude of the network activity. Furthermore, motion can mimic amplitude effects so it is crucial to use a technique that retains such information to ensure that connectivity differences are accurately localized. In this work, we investigate the dual regression approach that is frequently applied with group ICA to assess group differences in resting state functional connectivity of brain networks. We show how ignoring amplitude effects and how excessive motion corrupts connectivity maps and results in spurious connectivity differences. We also show how to implement the dual regression to retain amplitude information and how to use dual regression outputs to identify potential motion effects. Two key findings are that using a technique that retains magnitude information, e.g., dual regression, and using strict motion criteria are crucial for controlling both network amplitude and motion-related amplitude effects, respectively, in resting state connectivity analyses. We illustrate these concepts using realistic simulated resting state FMRI data and in vivo data acquired in healthy subjects and patients with bipolar disorder and schizophrenia.

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“…The correlations between network-wise ALFF and negative node strengths also confirmed that certain ICNs should behave in an anti-correlated manner. Therefore, the influences of univariate ALFF on bivariate node strengths could provide additional information to investigate the temporal organizations of ICNs [17]. Formal testing found that the impacts of network-wise ALFF on inter-network positive node strengths in ADHD were enhanced in occipital visual network (OVN), which also exhibited increased activities measured by voxel-wise indices [26].…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, the dynamical attributes within ICNs have drawn increasing interests in the last few years [16,17]. Most of the current functional indices considered the time-courses within ICNs as stationary signals.…”

Section: Discussionmentioning

confidence: 99%

“…Altered amplitude of low frequency The temporal patterns of ICNs could be investigated by two categories of features: (1) univariate temporal patterns (e.g., Hurst exponent, ALFF/fALFF, and complexity measures) based on individual time-course of ICN [16,17]; (2) bivariate temporal patterns based on functional connectivity between each pairs of ICNs [12,18]. However, the relationships between univariate and bivariate temporal features of ICNs in ADHD remain unexplored.…”

Section: Introductionmentioning

confidence: 99%

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Altered temporal features of intrinsic connectivity networks in boys with combined type of attention deficit hyperactivity disorder

Wang

2015

European Journal of Radiology

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Investigating univariate temporal patterns for intrinsic connectivity networks based on complexity and low-frequency oscillation: A test–retest reliability study

Cited by 27 publications

References 61 publications

Increased Low-Frequency Oscillation Amplitude of Sensorimotor Cortex Associated with the Severity of Structural Impairment in Cervical Myelopathy

Increased Low-Frequency Oscillation Amplitude of Sensorimotor Cortex Associated with the Severity of Structural Impairment in Cervical Myelopathy

Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses

Altered temporal features of intrinsic connectivity networks in boys with combined type of attention deficit hyperactivity disorder

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