Gradual Disturbances of the Amplitude of Low-Frequency Fluctuations (ALFF) and Fractional ALFF in Alzheimer Spectrum
Open accessAbstract
Background: Alzheimer’s disease (AD) is a common neurodegenerative disease in which the brain undergoes alterations for decades before symptoms become obvious. Subjective cognitive decline (SCD) have self-complain of persistent decline in cognitive function especially in memory but perform normally on standard neuropsychological tests. SCD with the presence of AD pathology is the transitional stage 2 of Alzheimer’s continuum, earlier than the prodromal stage, mild cognitive impairment (MCI), which seems to be the best target to research AD. In this study, we aimed to detect the transformational patterns of the intrinsic brain activity as the disease burden got heavy. Method: In this study, we enrolled 44 SCD, 55 amnestic MCI (aMCI), 47 AD dementia (d-AD) patients and 57 normal controls (NC) in total. A machine learning classification was utilized to detect identification accuracies between groups by using ALFF, fALFF, and fusing ALFF with fALFF features. Then, we measured the amplitude of the low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) levels in three frequency bands (classic: 0.01-0.1 Hz; slow-5: 0.01–0.027 Hz; and slow-4: 0.027–0.073 Hz) and compared alterations in patients with NC. Results: In the machine learning verification, the identification accuracy of SCD, aMCI, d-AD from NC was higher when fused ALFF and fALFF features (76.44%, 81.94%, and 91.83%, respectively) than only using ALFF or fALFF features. Several brain regions showed significant differences in ALFF/fALFF within these bands among four groups: brain regions presented decreasing trend of values, including the Cingulum_Mid_R (aal), bilateral inferior cerebellum lobe, bilateral precuneus, and the Cingulum_Ant_R (aal); increasing trend of values were detected in the Hippocampus_L (aal), Frontal_Mid_Orb_R (aal), Frontal_Sup_R (aal) and Paracentral_Lobule_R (aal) as disease progressed. The normalized ALFF/fALFF values of these features were significantly correlated with the neuropsychological test scores. Conclusions: This study revealed gradual disturbances in intrinsic brain activity as the disease progressed: the normal objective performance in SCD may be dependent on compensation; as disease advanced, the cognitive function gradually impaired and decompensated in aMCI, severer in d-AD. Our results indicated that the ALFF and fALFF may help detect the underlying pathological mechanism in AD continuum.
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In subjects with subjective cognitive decline, the amplitude of low-frequency fluctuations is higher in the left inferior parietal lobule and right middle occipital gyrus and lower in the precuneus and cerebellum than in healthy controls.
“For subjects with SCD, our group reported a higher amplitude of low-frequency fluctuations (ALFF) of spontaneous brain activity in the left inferior parietal lobule and right middle occipital gyrus and a lower ALFF in the precuneus and cerebellum than in HCs [122, 135].”
From Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer's disease (2020) · cites this paper as [135]
The Discussion (page unavailable) reports lower mALFF in the bilateral precuneus in SCD than in normal controls and lower cerebellar mALFF across the patient groups, including SCD. It does not report higher ALFF in SCD in the left inferior parietal lobule or right middle occipital gyrus; its Results identify different regions with increasing values. (LLM, conf=0.88)
Experimental — accuracy not yet measuredCombining ALFF and fractional ALFF features discriminates individuals with subjective cognitive decline from healthy controls with higher accuracy than using single features.
“Yang et al. observed a higher accuracy of discriminating individuals with SCD from HCs when ALFF and fractional ALFF features were combined than when only single features were used [135].”
From Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer's disease (2020) · cites this paper as [135]
Until recently, neuroimaging-based studies for classifying subjective cognitive decline have been scarce.
“However, until recently, neuroimaging-based studies for classifying SCD have been scarce [111, 126, 135, 204–206].”
From Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer's disease (2020) · cites this paper as [135]
The Introduction discusses earlier imaging findings in SCD, and the Methods (Classifiers) describe this paper’s own neuroimaging-based classification of SCD. Neither passage says that such classification studies had been scarce; searches for that assertion and close paraphrases found no supporting passage in our copy. (LLM, conf=0.75)
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Self-citation
- Authors
- Yang, L., Yan, Y., Wang, Y., Hu, X., Lu, J., Chan, P., Yan, T., & Han, Y.
- Affiliations
- Beijing Institute of Technology (CN)
- University of Cologne (DE)
- Capital Medical University (CN)
- Corresponding author
- Yan, Tianyi
- Han, Ying
- Venue
- Frontiers in Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2018-12-20
- Volume
- 12
- Pages
- 975-975
- ISSN
- 1662-453X (ISSN-L)
- PMC
- PMC6306691
- OpenAlex
- W2904195085
- Cited by
- 182
- References
- 74
- Citations by year
- 2026: 9 · 2025: 22 · 2024: 16 · 2023: 27 · 2022: 36 · 2021: 37 · 2020: 20 · 2019: 14
- FWCI
- 10.57
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Neuroscience (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Kölner Universitäts PublikationsServer (Universität zu Köln) (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (61633018, 81430037, 81471731, 31371007, 81671776, 61727807, 81522021)
- Beijing Municipal Natural Science Foundation (7132147)
- Beijing Municipal Science and Technology Commission (Z161100002616020)
- Beijing Nova Program (Z171100001117057)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Keywords
- Alzheimer's disease, subjective cognitive decline, amnestic mild cognitive impairment, Alzheimer's dementia, amplitude of low-frequency fluctuation, fractional ALFF, intrinsic brain activity, machine learning classification, disease progression, hippocampus, precuneus, cingulum, neuropsychological test performance, early detection of Alzheimer's disease, mild cognitive impairment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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