Abstract
The blood-oxygen label dependent (BOLD) signal obtained from functional magnetic resonance images (fMRI) varies significantly among populations. Yet, there is some agreement among researchers over the pace of the blood flow within several brain regions relative to the subject’s age and cognitive ability. Our analysis further suggested that regional coherence among the BOLD fMRI voxels belonging to the individual region of the brain has some correlation with underlying pathology as well as cognitive performance, which can suggest potential biomarkers to the early onset of the disease. To capitalise on this we propose a method, called Regional Optimum Frequency Analysis (ROFA), which is based on finding the optimum synchrony frequency observed at each brain region for each of the resting-state BOLD frequency bands (Slow 5 (0.01–0.027 Hz), Slow 4 (0.027–0.073 Hz) and slow 3 (0.073 to 0.198 Hz)), and the whole frequency band (0.01–0.167 Hz) respectively. The ROFA is carried out on fMRI data of total 310 scans, i.e., 26, 175 and 109 scans from 21 young-healthy (YH), 69 elderly-healthy (EH) and 33 Alzheimer’s disease (AD) patients respectively, where these scans include repeated scans from some subjects acquired at 3 to 6 months intervals. A 10-fold cross-validation procedure evaluated the performance of ROFA for classification between the YH vs EH, YH vs AD and EH vs AD subjects. Based on the confusion-matrix parameters; accuracy, precision, sensitivity and Matthew’s correlation coefficient (MCC), the proposed ROFA classification outperformed the state-of-the-art Group-independent component analysis (Group-ICA), Functional-connectivity, Graph metrics, Eigen-vector centrality, Amplitude of low-frequency fluctuation (ALFF) and fractional amplitude of low-frequency fluctuations (fALFF) based methods with more than 94.99% precision and 95.67% sensitivity for different subject groups. The results demonstrate the effectiveness of the proposed ROFA parameters (frequencies) as adequate biomarkers of Alzheimer’s disease.
| Original language | English |
|---|---|
| Pages (from-to) | 41953-41977 |
| Number of pages | 25 |
| Journal | Multimedia Tools and Applications |
| Volume | 81 |
| DOIs | |
| Publication status | Published - 1 Dec 2022 |
Bibliographical note
Funding Information: The Computational Neuroscience Research Team supported this work under the N. Ireland Department for Education and Learning – “Strengthening the All-island Research Base” project. Alzheimer’s disease data used in this article’s preparation were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu).Alzheimer’s disease data used in this article’s preparation were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu).
Keywords
- Functional MRI (fMRI)
- Resting State fMRI
- Frequency-domain analysis
- Regional optimum frequency analysis
- clustering
- Gaussian mixture model
- Alzheimer's disease biomarkers
- Alzheimer’s disease biomarkers
- Resting-state fMRI
- regional optimum frequency analysis
- Clustering
- Functional-MRI (fMRI)