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

Zou, Qihong; Zhu, Chaozhe; Yang, Yihong; Zuo, Xi‐Nian; Long, Xiangyu; Cao, Qingjiu; Wang, Yufeng; Zang, Yu‐Feng

doi:10.1016/j.jneumeth.2008.04.012

Cited by 1,795 publications

(1,827 citation statements)

References 34 publications

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“…Results show that the raw BOLD signal is outperformed by the normalized BOLD signal in this regard, as exemplified by the RSFA. Alternative definitions of the ALFF, such as the fractional ALFF (Zou et al, 2008), were not tested in this study, but may offer improved performance, as is seen in the RSFA.…”

Section: Cvr Estimation Based On Bold Fluctuation Amplitudementioning

confidence: 99%

“…This is similar in nature to the RSFA, except for the fact that there is no voxelwise normalization by the BOLD signal mean, thus allowing the ALFF to take into account regional differences in quantitative BOLD amplitude. We chose ALFF over fractional ALFF (fALFF) in our comparison, since fALFF is the signal power in [0.01, 0.08 Hz] normalized by the power of the whole spectrum (Zou et al, 2008), and the normalization would likely reduce the vascular contribution to the fALFF, thereby compromising its vascular link.…”

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confidence: 99%

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Quantitative mapping of cerebrovascular reactivity using resting-state BOLD fMRI: Validation in healthy adults

2016

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In conventional neuroimaging, cerebrovascular reactivity (CVR) is quantified primarily using the blood-oxygenation level-dependent (BOLD) functional MRI (fMRI) signal, specifically, as the BOLD response to intravascular carbon dioxide (CO 2 ) modulations, in units of [%ΔBOLD/ mmHg]. While this method has achieved wide appeal and clinical translation, the tolerability of CO 2 -related tasks amongst patients and the elderly remains a challenge in more routine and largescale applications. In this work, we propose an improved method to quantify CVR by exploiting intrinsic fluctuations in CO 2 and corresponding changes in the resting-state BOLD signal (rsqCVR). Our rs-qCVR approach requires simultaneous monitoring of PETCO 2 , cardiac pulsation and respiratory volume. In 16 healthy adults, we compare our quantitative CVR estimation technique to the prospective CO 2 -targeting based CVR quantification approach (qCVR, the "standard"). We also compare our rs-CVR to non-quantitative alternatives including the restingstate fluctuation amplitude (RSFA), amplitude of low-frequency fluctuation (ALFF) and globalsignal regression. When all subjects were pooled, only RSFA and ALFF were significantly associated with qCVR. However, for characterizing regional CVR variations within each subject, only the PETCO 2 -based rs-qCVR measure is strongly associated with standard qCVR in 100% of the subjects (p<=0.1). In contrast, for the more qualitative CVR measures, significant withinsubject association with qCVR was only achieved in 50-70% of the subjects. Our work establishes the feasibility of extracting quantitative CVR maps using rs-fMRI, opening the possibility of mapping functional connectivity and qCVR simultaneously.

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Section: Cvr Estimation Based On Bold Fluctuation Amplitudementioning

confidence: 99%

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confidence: 99%

Quantitative mapping of cerebrovascular reactivity using resting-state BOLD fMRI: Validation in healthy adults

2016

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“…We calculated voxel-wise BOLD-ALFF over a frequency range of 0.01-0.083 Hz. The BOLD-ALFF maps were standardized by normalization with the mean BOLD-ALFF of the entire brain voxels (Zou et al, 2008;Zuo et al, 2010a). Prior to FC analysis, the residual BOLD time series were band-pass filtered (0.01-0.083 Hz) and a 10-mm diameter spherical seed located in the posterior cingulate cortex (PCC) was selected (Van Dijk et al, 2012;Yan et al, 2013b).…”

Section: Bold-alff and Fcmentioning

confidence: 99%

“…We calculated the Pearson's correlation coefficient between the seed time series and every other voxel in the brain and transformed the correlation coefficient values into z values using Fisher's r-to-z transformation. To evaluate the effect of data denoising, we calculated BOLD-ALFF with standard preprocessing, i.e., with neither ICA denoising nor regression of covariates of no interest (Zou et al, 2008;Zuo et al, 2010a); and BOLD-FC with standard preprocessing, i.e., without ICA denoising but with regression of covariates of no interest (Patriat et al, 2013).…”

Section: Bold-alff and Fcmentioning

confidence: 99%

“…In contrast to functional connectivity, amplitude of low-frequency fluctuation (ALFF) is a metric that measures the fluctuation amplitude of each single voxel (Liu et al, 2013;Zang et al, 2007;Zou et al, 2008;Zuo and Xing, 2014) and could be applied to both BOLD and ASL techniques. ALFF has been widely used to examine moment-to-moment brain signal variability (Garrett et al, 2011;Garrett et al, 2013), to correlate with task activations and behavior , and to characterize disease stages (Han et al, 2011;Lui et al, 2009;Wang et al, 2011b;Yu et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Reliability comparison of spontaneous brain activities between BOLD and CBF contrasts in eyes-open and eyes-closed resting states

et al. 2015

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Blood oxygenation level dependent (BOLD) and arterial spin labeling (ASL) are two predominant resting-state fMRI techniques in mapping spontaneous brain activities. At single voxel level, cerebral blood flow (CBF) measured by ASL and amplitude of low frequency fluctuations (ALFF) of BOLD have been recognized as useful indices to characterize brain function in health and disease. However, no study has directly compared the test-retest reliability between BOLD and CBF contrasts on the same group of subjects at single voxel level. Moreover, both eyes-open and eyes-closed conditions have been employed as resting states, but it is still not clear which state is more reliable. Here we collected BOLD and ASL data during eyes-open and eyes-closed states across three scanning visits on twenty-two healthy young subjects. CBF-mean, BOLD-and CBF-ALFF were computed to characterize corresponding brain activities at single voxel level. Seed-based functional connectivity (FC) with the posterior cingulate cortex (PCC) was further calculated for both BOLD and CBF data. Intra-class correlation was used as the index of long-term reliability between visits 1 and 2 (two months apart) and short-term reliability between visits 2 and 3 (on the same day). Both short-and long-term reliabilities for CBF-mean and BOLD-ALFF were high, but were lower for CBF-ALFF, BOLD-and CBF-FC in both eyes-open and eyes-closed states. Direct comparisons showed that brain regions with the highest reliability of CBF-mean were mainly in the gray matter. The reliability of CBF-ALFF and BOLD-FC was lower than that of BOLD-ALFF, and the reliability of CBF-FC was lower than those of both CBF-ALFF and BOLD-FC. Furthermore, we observed that reliabilities of the eyes-open state were higher than those of the eyes-closed state for both imaging contrasts, though the effect size was small. Voxel-wise comparisons demonstrated that the long-term reliability of BOLD-ALFF was significantly higher with eyes open in the visual system, and both the short-and long-term reliability of BOLD-FC was slightly higher with eyes open in the default mode network. Moreover, we showed that denoising decreased the reliability of both ALFF and FC of both BOLD and ASL contrasts. In conclusion, our results indicated that CBF-mean and BOLD-ALFF could both be used as reliable indices for characterizing resting-state brain activities at single voxel level and recommended the eyesopen state for resting-state studies, especially for those targeting the visual system and default mode network.

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Lifetime Exposure to Depression and Neuroimaging Measures of Brain Structure and Function

Wang,

Hoffstaedter,

Kasper

et al. 2024

JAMA Netw Open

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ImportanceDespite decades of neuroimaging studies reporting brain structural and functional alterations in depression, discrepancies in findings across studies and limited convergence across meta-analyses have raised questions about the consistency and robustness of the observed brain phenotypes.ObjectiveTo investigate the associations between 6 operational criteria of lifetime exposure to depression and functional and structural neuroimaging measures.Design, Setting, and ParticipantsThis cross-sectional study analyzed data from a UK Biobank cohort of individuals aged 45 to 80 years who were enrolled between January 1, 2014, and December 31, 2018. Participants included individuals with a lifetime exposure to depression and matched healthy controls without indications of psychosis, mental illness, behavior disorder, and disease of the nervous system. Six operational criteria of lifetime exposure to depression were evaluated: help seeking for depression; self-reported depression; antidepressant use; depression definition by Smith et al; hospital International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnosis codes F32 and F33; and Composite International Diagnostic Interview Short Form score. Six increasingly restrictive depression definitions and groups were defined based on the 6 depression criteria, ranging from meeting only 1 criterion to meeting all 6 criteria. Data were analyzed between January and October 2022.Main Outcomes and MeasuresFunctional measures were calculated using voxel-wise fractional amplitude of low-frequency fluctuation (fALFF), global correlation (GCOR), and local correlation (LCOR). Structural measures were calculated using gray matter volume (GMV).ResultsThe study included 20 484 individuals with lifetime depression (12 645 females [61.7%]; mean [SD] age, 63.91 [7.60] years) and 25 462 healthy controls (14 078 males [55.3%]; mean [SD] age, 65.05 [7.8] years). Across all depression criteria, individuals with lifetime depression displayed regionally consistent decreases in fALFF, LCOR, and GCOR (Cohen d range, −0.53 [95% CI, −0.88 to −0.15] to −0.04 [95% CI, −0.07 to −0.01]) but not in GMV (Cohen d range, −0.47 [95 % CI, −0.75 to −0.12] to 0.26 [95% CI, 0.15-0.37]). Hospital ICD-10 diagnosis codes F32 and F33 (median [IQR] difference in effect sizes, −0.14 [−0.17 to −0.11]) and antidepressant use (median [IQR] difference in effect sizes, −0.12 [−0.16 to −0.10]) were criteria associated with the most pronounced alterations.Conclusions and RelevanceResults of this cross-sectional study indicate that lifetime exposure to depression was associated with robust functional changes, with a more restrictive depression definition revealing more pronounced alterations. Different inclusion criteria for depression may be associated with the substantial variation in imaging findings reported in the literature.

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

Cited by 1,795 publications

References 34 publications

Quantitative mapping of cerebrovascular reactivity using resting-state BOLD fMRI: Validation in healthy adults

Quantitative mapping of cerebrovascular reactivity using resting-state BOLD fMRI: Validation in healthy adults

Reliability comparison of spontaneous brain activities between BOLD and CBF contrasts in eyes-open and eyes-closed resting states

Lifetime Exposure to Depression and Neuroimaging Measures of Brain Structure and Function

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