An Effective Brain Imaging Biomarker for AD and aMCI: ALFF in Slow-5 Frequency Band

Abstract

Background: As a potential brain imaging biomarker, amplitude of low frequency fluc-tuation (ALFF) has been used as a feature to distinguish patients with Alzheimer’s disease (AD) and amnestic mild cognitive impairment (aMCI) from normal controls (NC). However, it remains unclear whether the frequency-dependent pattern of ALFF alterations can effectively distinguish the different phases of the disease. Methods: In the present study, 52 AD and 50 aMCI patients were enrolled together with 43 NC in total. The ALFF values were calculated in the following three frequency bands: classical (0.01-0.08 Hz), slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) for the three different groups. Subsequently, the local functional abnormalities were employed as features to examine the effect of classification among AD, aMCI and NC using a support vector machine (SVM). Results: We found that the among-group differences of ALFF in the different frequency bands were mainly located in the left hippocampus (HP), right HP, bilateral posterior cingulate cortex (PCC) and bilateral precuneus (PCu), left angular gyrus (AG) and left medial prefrontal cortex (mPFC). When the local functional abnormalities were employed as features, we identified that the ALFF in the slow-5 frequency band showed the highest accuracy to distinguish among the three groups. Conclusion: These findings may deepen our understanding of the pathogenesis of AD and suggest that slow-5 frequency band may be helpful to explore the pathogenesis and distinguish the phases of this disease.

The Predictive Value of Dynamic Intrinsic Local Metrics in Transient Ischemic Attack

“…To evaluate the classification ability of the SVM model, the accuracy, sensitivity, specificity and precision were calculated, and the receiver operating characteristic (ROC) curve of the classifier is shown in Figure 2 . The curve was drawn using Receiver Operating Characteristic Assistant software ( Wang et al, 2021 ). The performance of the classifier achieved an accuracy of 80.90%, sensitivity of 77.08%, specificity of 85.37%, precision of 86.05%, and area under curve (AUC) of 0.8501 for TIA vs. HCs.…”

Textural features reflecting local activity of the hippocampus improve the diagnosis of Alzheimer’s disease and amnestic mild cognitive impairment: A radiomics study based on functional magnetic resonance imaging

“…More importantly, these ALFF changes were found to exhibit a frequency-dependent pattern ( Han et al, 2011 ; Liu et al, 2014 ). In our previous study, we observed the difference in ALFF in the slow-5 frequency band between groups, mainly corresponding to the bilateral hippocampus as well as regions within the default mode network, with the highest accuracy in discriminating the three groups ( Wang et al, 2021 ). Our findings indicated that ALFF in the slow-5 frequency band might serve as a promising functional indicator to aid the diagnosis of AD and aMCI ( Wang et al, 2021 ).…”

“…In our previous study, we observed the difference in ALFF in the slow-5 frequency band between groups, mainly corresponding to the bilateral hippocampus as well as regions within the default mode network, with the highest accuracy in discriminating the three groups ( Wang et al, 2021 ). Our findings indicated that ALFF in the slow-5 frequency band might serve as a promising functional indicator to aid the diagnosis of AD and aMCI ( Wang et al, 2021 ). Recently, ALFF combined with structural features has been investigated for the diagnosis of AD and aMCI ( Khatri and Kwon, 2022 ; Liu et al, 2022 ).…”

“…2012KY002) and was conducted according to the Declaration of Helsinki. The inclusion and exclusion criteria have been described at our previous study ( Wang et al, 2021 ). All participants underwent medical history collection, physical examinations, laboratory examinations, routine brain magnetic resonance scans and the Mini-Mental State Test (MMSE).…”

“…As an advanced non-invasive neuroimaging technique, resting-state functional magnetic resonance imaging (fMRI) is an important imaging modality to understand the neurodegenerative course of aMCI and early AD ( Wu et al, 2022 ), because the memory dysfunction may occur before the structural degeneration ( Jin et al, 2012 ). The amplitude of low frequency fluctuation (ALFF) is proposed to characterize the local properties of rs-fMRI signals ( Zang et al, 2007 ), showing frequency-dependent pattern ( Zuo et al, 2010 ) and temporal variability (dynamics) ( Liao et al, 2019 ), and thus has been widely used to detect functional abnormalities in brain disorders ( Li et al, 2020 ; Wang et al, 2021 ). As for AD and aMCI patients, previous studies have consistently observed the alterations of ALFF value in the hippocampus ( Liu et al, 2014 ; Cha et al, 2015 ; Yang et al, 2018 ; Yuan et al, 2021 ).…”