Introduction: In recent years, resting-state functional magnetic resonance imaging (rsfMRI)
has been increasingly used as a noninvasive and practical method in different areas of neuroscience
and psychology for recognizing brain’s mechanism as well as diagnosing neurological
diseases. In this work, we use rs-fMRI data for diagnosing Alzheimer's disease.
Materials and Methods: To do that, by using the rs-fMRI of a patient, we computed the time series
of some anatomical regions and then applied the Latent Low Rank Representation method to
extract suitable features. Next, based on the extracted features, we apply a Support Vector Machine
(SVM) classifier to determine whether the patient belongs to a healthy category, mild stage
of the disease or Alzheimer's stage.
Results: The obtained classification accuracy for the proposed method is more than 97.5%.
Conclusion: We performed different experiments on a database of rs-fMRI data containing the
images of 43 healthy subjects, 36 mild cognitive impairment patients and 32 Alzheimer’s patients
and the obtained results demonstrated that the best performance is achieved when the SVM with
Gaussian kernel and the features of only 7 regions were used.