An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF.

By Qihong Zou, Chaozhe Zhu, Yihong Yang, Xi-Nian Zuo, Xiangyu Long, Qingjiu Cao, Yu-Feng Wang, Yufeng Zang. Journal of Neuroscience Methods, 2008

2,109 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/6755fbd65574fced554288616575b801f8da0e86

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Citations in the corpus

A Correspondence between Individual Differences in the Brain's Intrinsic Functional Architecture and the Content and Form of Self-Generated Thoughts (2014, background): “, [24,25]).”
REST: A Toolkit for Resting-State Functional Magnetic Resonance Imaging Data Processing (2011, methodology): “An improved ALFF approach, namely, fractional ALFF (fALFF) [38], is implemented in REST. fALFF is calculated as the ratio of the power spectrum of low-frequency signals (usually 0.01 , 0.08 Hz) to that of the entire frequency range (e.g., 0 , 0.25 Hz for TR = 2 s).”
Capturing the spatiotemporal dynamics of self-generated, task-initiated thoughts with EEG and fMRI (2019, neutral): “To identify changes in brain activity across different brain regions during the two task-initiated, self-generated processing conditions (math and memory), fALFF values were mapped (Zou et al., 2008), serving as a model-free measure of local BOLD activity (Biswal, 2012).”
The Neuro Bureau ADHD-200 Preprocessed Repository (2016, methodology): “Whole brain fALFF maps were generated by dividing the variance of each voxel’s bandpass-ﬁltered time series by the variance of its unﬁltered time series [29].”
Impact of In-Scanner Head Motion on Multiple Measures of Functional Connectivity: Relevance for Studies of Neurodevelopment in Youth (2012, methodology): “ALFF measures the amplitude of low frequency oscillations, while fALFF measures the relative predominance of low frequency amplitude to the amplitude of all oscillations across the entire power spectrum (Zou et al., 2008).”
Advances and Pitfalls in the Analysis and Interpretation of Resting-State FMRI Data (2010, positive): “With these issues in mind the ALFF approach has been more recently refi ned to account for ‘fractional’ inclusion of information in frequencies outside of the normal range (fALFF; Zou et al., 2008).”
Functional Magnetic Resonance Imaging Phase Synchronization as a Measure of Dynamic Functional Connectivity (2012, positive): “08Hz (Biswal et al., 1995; Buckner et al., 2009; Zou et al., 2008), but even within this frequency range there is noise contamination.”
A Comprehensive Assessment of Regional Variation in the Impact of Head Micromovements on Functional Connectomics (2013, methodology): “This is not surprising, as Z-standardization (i.e., within-subject mean centering and variance normalization) was applied in accord with common practice (e.g., Buckner et al., 2009; Zou et al., 2008; Zuo et al., 2010a, 2012, 2013).”
Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes (2013, positive): “, 2004, 2007); Z-score standardization (Beckmann et al., 2005; Buckner et al., 2009; Calhoun et al., 2001; Zou et al., 2008; Zuo et al., 2010a, 2012, 2013); and Gaussian function fit normalization (Lowe et al.”
Frequency-dependent changes in the amplitude of low-frequency fluctuations in amnestic mild cognitive impairment: A resting-state fMRI study (2011, methodology): “, 2007), and fALFF measures the power within a specific frequency range divided by the total power in the entire detectable frequency range (Zou et al., 2008).”
Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space (2012, positive): “, amplitudes) may also affect ReHo (Zang et al., 2007; Zou et al., 2008; Zuo et al., 2010a).”
The Oscillating Brain: Complex and Reliable (2009, methodology): “white matter distinctions for ALFF and fALFF measures, with low-frequency fluctuations being more detectable within gray matter (Jezzard et al., 1993; Biswal et al., 1995; Zang et al., 2007; Zou et al., 2008).”