Amplitude of low frequency fluctuations

This article is about the fMRI metrics. For the frequency-domain statistic, see Power spectral density.

Amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF) are resting-state functional MRI (rs-fMRI) metrics that quantify the power of spontaneous, low-frequency (~0.01–0.10 Hz) fluctuations of the BOLD signal within a voxel or region of interest. ALFF measures the square root of the power spectrum within a predefined low-frequency band (commonly 0.01–0.08 or 0.01–0.10 Hz). The exact upper cutoff depends on the sampling rate (repetition time, TR) and must be below the Nyquist frequency (1/(2·TR)).[1] fALFF is the ratio of power within that low-frequency band to the total power across a broader band (often 0–0.25 Hz), which reduces nonspecific physiological noise relative to ALFF.[2]

Electrophysiological studies suggest that low-frequency BOLD oscillations partly reflect spontaneous neuronal activity: simultaneous EEG–fMRI demonstrates that canonical resting-state networks exhibit distinct electrophysiological signatures whose power covaries with BOLD fluctuations.[3] Early rs-fMRI work also showed that frequencies below ~0.1 Hz dominate functional connectivity patterns.[4]

History

The ALFF metric was popularized in early rs-fMRI studies in the mid‑2000s and applied to clinical populations. A widely cited report applied ALFF to children with attention-deficit/hyperactivity disorder (ADHD), describing regional increases and decreases relative to typically developing peers.[5] The fALFF variant was introduced to improve specificity to gray-matter neuronal fluctuations by normalizing low-frequency power to the whole-band power.[2]

Computation

ALFF and fALFF are typically computed as follows: (1) preprocess rs-fMRI time series (slice timing correction, realignment, nuisance regression, spatial normalization, and—optionally—temporal filtering);[6] (2) transform each voxel's time series to the frequency domain with a FFT and obtain the power spectrum; (3) define a low-frequency band (commonly 0.01–0.08 or 0.01–0.10 Hz, constrained by the Nyquist frequency 1/(2·TR)) and compute ALFF as the square root of the average power within that band;[1] and (4) compute fALFF as the ratio of power in the low-frequency band to the power across a broader band (e.g., up to the Nyquist frequency set by TR; often 0–0.25 Hz for TR≈2 s).[2]

Frequency choices and sub-bands

Some studies partition the spectrum into sub‑bands—commonly slow‑5 (0.01–0.027 Hz) and slow‑4 (0.027–0.073 Hz)—to probe frequency‑specific effects; frequency‑dependent differences in ALFF/fALFF topography are robust across datasets.[1][7]

Physiological considerations

Low-frequency BOLD power is influenced by neuronal and non‑neuronal factors. Non‑neuronal contributors include cardiac and respiratory cycles and cerebrospinal fluid pulsations, which can contaminate signals near large vessels and ventricles.[4] fALFF reduces some of this sensitivity by normalizing low‑frequency power to total power, though it does not fully remove physiological noise.[2]

Topography and the default mode network

Whole‑brain ALFF/fALFF maps show relatively high values in regions associated with the default mode network (DMN), including the posterior cingulate cortex, precuneus, medial prefrontal cortex, and bilateral inferior parietal lobule, consistent with the prominence of low‑frequency activity in these areas.[2]

Clinical and research applications

ALFF and fALFF are descriptive markers used in case–control comparisons and biomarker discovery across many conditions. Examples include:

  • Attention‑deficit/hyperactivity disorder: altered ALFF in frontal, sensorimotor, brainstem, and cerebellar regions in children with ADHD.[5]
  • Schizophrenia: voxelwise analyses report regional increases and decreases in ALFF/fALFF; findings vary across cohorts.[8] Reviews and multi‑site analyses highlight heterogeneity and the importance of harmonized preprocessing.[9]
  • Major depressive disorder: quantitative syntheses indicate abnormal intrinsic activity, with meta‑analyses reporting altered ALFF in cingulo‑prefrontal and posterior midline regions; effect directions vary with clinical features and methods.[10]
  • Alzheimer's disease and mild cognitive impairment: meta‑analyses report alterations involving the posterior cingulate/precuneus and parahippocampal regions; frequency‑specific effects have been described in aMCI and AD.[11][12]

Relation to ReHo and PerAF

Regional homogeneity (ReHo)

ReHo measures the similarity (Kendall's coefficient of concordance) of a voxel's time series with its neighbors, indexing local synchrony rather than amplitude. It is often complementary to ALFF/fALFF in mapping local coherence versus power.[13]

Percent amplitude of fluctuation (PerAF)

PerAF expresses the average absolute BOLD fluctuation at each voxel as a percentage of its mean signal across time. Unlike ALFF/fALFF, which are band‑limited spectral measures, PerAF is a time‑domain, scale‑independent metric analogous to percent signal change in task fMRI.[14]

Limitations

ALFF/fALFF are sensitive to preprocessing choices (e.g., filtering, motion regression), physiological noise, and scanner/sequence parameters. Interpretability at the single‑subject level is limited; these metrics are most reliable for group‑level comparisons.[15][16]

See also

References

  1. ^ a b c Han, Yang; Zhan, Yijun; Zheng, Bo; Ma, Yang; Zhang, Zhiqiang (2011). "Frequency-dependent changes in the amplitude of low-frequency fluctuations in amnestic mild cognitive impairment: a resting-state fMRI study". NeuroImage. 55 (1): 287–295. doi:10.1016/j.neuroimage.2010.11.059. PMID 21110915.
  2. ^ a b c d e Zou, Qiang-Hua; Zhu, Chao-Zhe; Yang, Yihong; Zuo, Xin-Nian; Long, Xiao-Yan; Cao, Qiu-Jin; Wang, Yan-Fang (2008). "An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF". Journal of Neuroscience Methods. 172 (1): 137–141. doi:10.1016/j.jneumeth.2008.04.012. PMC 3902859. PMID 18501969.
  3. ^ Mantini, Dante; Perrucci, M. G.; Del Gratta, C.; Romani, G. L.; Corbetta, M. (2007). "Electrophysiological signatures of resting-state networks in the human brain". Proceedings of the National Academy of Sciences of the United States of America. 104 (32): 13170–13175. Bibcode:2007PNAS..10413170M. doi:10.1073/pnas.0700668104. PMC 1941791. PMID 17670949.
  4. ^ a b Cordes, Dietmar; Haughton, Victor M.; Arfanakis, Konstantinos; Carew, Jennifer D.; Turski, Paul A.; Moritz, Charles H.; Quigley, Maria A.; Meyerand, Mary E. (2001). "Frequencies contributing to functional connectivity in the cerebral cortex in "resting-state" data". AJNR. American Journal of Neuroradiology. 22 (7): 1326–1333. PMC 7975218. PMID 11498421.
  5. ^ a b Zang, Yu-Feng; He, Yong; Zhu, Chao-Zhe; Cao, Qiu-Jin; Sui, Man-Qiu; Liang, Ming; Tian, Li-Xia; Jiang, Tian-Zi; Wang, Yan-Fang (2007). "Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI". Brain and Development. 29 (2): 83–91. doi:10.1016/j.braindev.2006.07.002. PMID 16919409.
  6. ^ Murphy, Kevin (2017). "Band-pass filtering of fMRI data confounds analyses of ALFF and fALFF". NeuroImage. 152 (4): 221–231. doi:10.1016/j.neuroimage.2017.02.078. PMID 28219616.
  7. ^ Zuo, Xin-Nian; Di Martino, Adriana; Kelly, C. Clare; Shehzad, Zarrar E.; Gee, David G.; Klein, Arno; Castellanos, F. Xavier; Biswal, Bharat B. (2010). "The oscillating brain: complex and reliable". Cerebral Cortex. 20 (5): 1133–1144. doi:10.1093/cercor/bhp145. PMC 2856476. PMID 19819948.
  8. ^ Hoptman, Michael J.; Zuo, Xin-Nian; Butler, Pragya D.; Javitt, Daniel C.; D'Angelo, Domingo; Mauro, Carla J.; Milham, Michael P. (2010). "Amplitude of low-frequency oscillations in schizophrenia: a resting state fMRI study". Schizophrenia Research. 117 (1): 13–20. doi:10.1016/j.schres.2009.09.030. PMC 3388725. PMID 19875103.
  9. ^ Wang, Peng; Zhang, Ying; Zhuo, Chuanjun (2019). "ALFF may be a potential biomarker in psychiatric disorders: a systematic review". BMC Psychiatry. 19 (1): 136. doi:10.1186/s12888-018-1992-4. PMC 6480882. PMID 31036007.
  10. ^ Gong, Junke; Wang, Jie; Qiu, Meng; Chen, Pengfei; Zhang, Rong; Lu, Wenbin; Zhang, Hao; Jia, Zhiyong (2020). "Common and distinct patterns of intrinsic brain activity alterations in major depressive disorder and bipolar disorder: voxel-based meta-analyses". Translational Psychiatry. 10 (1): 353. doi:10.1038/s41398-020-01036-5. PMC 7554061. PMID 33057002.
  11. ^ Zhang, Xia; Liao, Wei; Wang, Zhijian; Xu, Qiang; Qiu, Junming; Zhang, Jianlin (2021). "Altered Patterns of Amplitude of Low‑Frequency Oscillations and Fractional ALFF in Amnestic Mild Cognitive Impairment: A Quantitative Meta‑Analysis". Frontiers in Aging Neuroscience. 13: 641–657. doi:10.3389/fnagi.2021.711023. PMC 8493127. PMID 34629873.
  12. ^ Tang, Xin; Liu, Jinlong; Zhang, Xia; Li, Yongjie (2024). "A Multimodal Meta‑Analytical Evidence of Functional and Structural Alterations in Alzheimer's Disease". Behavioural Brain Research. 447 114545. doi:10.1016/j.bbr.2023.114545. PMID 38395200.
  13. ^ Zang, Yu‑Feng; Jiang, Taosheng; Lu, Ying; He, Yong; Tian, Li‑Xia (2004). "Regional homogeneity approach to fMRI data analysis". NeuroImage. 22 (1): 394–400. doi:10.1016/j.neuroimage.2003.12.030. PMID 15110032.
  14. ^ Jia, Xin‑Zhi; Sun, Jin‑Wen; Ji, Guo‑Jun; Liao, Wei; Lv, Ying‑Tao; Wang, Jun‑Hui; Qin, Wen; Zang, Yu‑Feng (2020). "Percent amplitude of fluctuation: A simple measure for resting‑state fMRI signal at single‑voxel level". PLOS ONE. 15 (1) e0227021. doi:10.1371/journal.pone.0227021. PMC 6993910. PMID 31978190.
  15. ^ Zuo, Xin‑Nian; Xing, Xu‑Lei (2014). "Test–retest reliabilities of resting‑state FMRI measurements in human brain functional connectomics: An overview". Neuroscience & Biobehavioral Reviews. 45: 100–118. doi:10.1016/j.neubiorev.2014.05.009. PMID 24875392.
  16. ^ Murphy, Kevin; Fox, Michael D. (2017). "Towards a consensus regarding global signal regression for resting state functional connectivity MRI". NeuroImage. 154 (3): 169–173. doi:10.1016/j.neuroimage.2016.11.052. PMID 27890737.
This article is issued from Wikipedia. The text is available under Creative Commons Attribution-Share Alike 4.0 unless otherwise noted. Additional terms may apply for the media files.