Tehran University Medical Journal

Background: Early diagnosis of patients in the early stages of Alzheimer's, known as mild cognitive impairment, is of great importance in the treatment of this disease. If a patient can be diagnosed at this stage, it is possible to treat or delay Alzheimer's disease. Resting-state functional magnetic resonance imaging (fMRI) is very common in the process of diagnosing Alzheimer's disease. In this study, we intend to separate subjects with mild cognitive impairment from healthy control based on fMRI data using brain functional connectivity and graph theory.
Methods: In this article, which was done from April to November 2020 in Tehran, after pre-processing the fMRI data, 116 brain regions were extracted using an Automated Anatomical Labeling atlas. Then, the functional connectivity matrix between the time signals of 116 brain regions was calculated using Pearson correlation and mutual information methods. Using functional connectivity calculations, the brain graph network was formed, followed by thresholding of the brain connectivity network to keep significant and strong edges while eliminating weaker edges that were likely noise. Finally, 11 global features were extracted from the graph network and after performing statistical analyses and selecting optimal features; the classification of 14 healthy individuals and 11 patients with mild cognitive impairment was performed using a support vector machine classifier.

Conclusion: Combining the features of brain graph and functional connectivity by the mutual information method with a machine learning approach, based on fMRI imaging analysis, is very effective in diagnosing mild cognitive impairment in the early stages of Alzheimer’s which consequently allows treating or delaying this disease.

Background: Atrophy of hippocampal subfields is one of the diagnostic biomarkers of Alzheimer's disease, which has also been observed in many patients with mild cognitive impairment. There is still no clear understanding of the atrophy pattern of hippocampal subfields in Alzheimer's disease and its differentiation from mild cognitive impairment. In this cross-sectional study, hippocampal subfield atrophy in Alzheimer's patients were compared with patients with early (EMCI) and late (LMCI) cognitive impairment and the control group.
Methods: This was a cross-sectional study conducted from September 2021 to September 2022 in the radiology department of Tabriz Paramedical Faculty. MRI images of Alzheimer's patients, EMCI patients, LMCI patients, and normal controls (NCs) were obtained from the ADNI database. Different hippocampus subfields of hippocampal fissure, dentate gyrus head, dentate gyrus body, first cornu ammonis body, cornu ammonis head, subiculum body, and subiculum head were isolated using the hippocampus segmentation tool in FreeSurfer 7.0 software. The volume of all subfields was calculated bilaterally and normalized. The volume difference of each hippocampus subfield between the groups participating in the study and the pair volume difference between the groups was analyzed using the Kruskal-Wallis H Test and post-hoc Dunn's test. The P<0.05 was considered as the significance level.

Results: The most significant volume difference between the four groups participating in the study was related to the whole hippocampus, DG body, subiculum body, and subiculum head subfields (P<0.0001). Also, when examining pairs, the most significant difference was observed between the NC/AD pair (P<0.0001) and the least significant difference between the pair of LMCI/AD group (P<0.05) and in the subfield subiculum body showing the progressive course of hippocampal subfield atrophy with cognitive progress towards Alzheimer's disease. Conclusion: In most subfields of the hippocampus, a significant difference in atrophy can be seen, increasing the severity of atrophy as the disorder progresses toward Alzheimer's. Such findings can help guide future studies to improve diagnostic performance to identify individuals at high risk of Alzheimer's disease.