Fusion analysis of resting-state networks and its application to Alzheimer's disease

Abstract

Functional networks are extracted from resting-state functional magnetic resonance imaging data to explore the biomarkers for distinguishing brain disorders in disease diagnosis. Previous works have primarily focused on using a single Resting-State Network (RSN) with various techniques. Here, we apply fusion analysis of RSNs to capturing biomarkers that can combine the complementary information among the RSNs. Experiments are carried out on three groups of subjects, i.e., Cognition Normal (CN), Early Mild Cognitive Impairment (EMCI), and Alzheimer's Disease (AD) groups, which correspond to the three progressing stages of AD; each group contains 18 subjects. First, we apply group Independent Component Analysis (ICA) to extracting the Default Mode Network (DMN) and Dorsal Attention Network (DAN) for each subject group. Then, by obtaining the common DMN and DAN as templates for each group, we employ the individual ICA to extract the DMN and DAN for each subject.Finally, we fuse the DMNs and DANs to explore the biomarkers. The results show that (1) the templates generated by group ICA can extract the RSN for each subject by individual ICA effectively; (2) the RSNs combined with the fusion analysis can obtain more informative biomarkers than without fusion analysis; (3) the most different regions of DMN and DAN are found between CN and EMCI and between EMCI and AD, which show differences. For the DMN, the difference in the medial prefrontal cortex between the EMCI and AD is smaller than that between CN and EMCI, whereas that in the posterior cingulate between EMCI and AD is larger. As for the DAN, the difference in the intraparietal sulcus is smaller than that between CN and EMCI; (4) extracting DMN and DAN for each subject via the back reconstruction of group ICA is invalid.

Combining residual attention mechanisms and generative adversarial networks for hippocampus segmentation

“…It is mainly responsible for the storage, conversion, and orientation of longterm memory. The hippocampus is closely related to many neurological diseases, such as Alzheimer's disease, schizophrenia, and dementia [1] . However, the shape of the hippocampus is irregular, its volume is small, its edges have no clear boundaries, and individual differences are large.…”

“…In recent years, deep learning methods have been widely used in computer vision [7] , and convolutional neural networks have made some progress in medical image processing. Alaoui et al [8] proposed a method based on machine learning to classify tumors.…”