Topic: Neurodegenerative Diseases and Mental Health in the Elderly
Moderator: Qiao Wen, Post.doc
Date: 25/11/2024, 14:00
Location: The lab of HMRRC (10011, the 8th Teaching Building)
Speaker 1: Baoshuai Zhang, Ph.D. Candidate
Title: Multimodal brain connectome-based prediction of suicide risk in people with late-life depression
Keypoints：
Question: Could multimodal brain connectivity capture individual differences in suicide risk among latelife depression patients? Hypothesis: The functional and structural network would predict individual suicide risk scores among LLD patients using several suicide behavior questionnaires. Methods: To predict the suicide risk using functional connectivity (FC) and structural connectivity (SC) , the connectome-based predictive modelling (CPM) method was used. To formally test the predictive strength of the identified FC and SC profiles, a machine learning method was employed to derive the prediction accuracy of the models. Meaning: The brain connectome models can predict suicide risk in LLD patients. The FC and SC profiles improved the classification accuracy when distinguishing LLD patients with higher suicide risk from those with lower suicide risk.
Speaker 2: Shihan Tian, M.Med. Candidate
Title: Imaging beta amyloid aggregation and iron accumulation in Alzheimer's disease using quantitative susceptibility mapping MR
Keypoints：
Question: Can QSM MR imaging noninvasively monitor beta-amyloid aggregation and iron accumulation and aid in the diagnosis of Alzheimer's disease?
Findings: The study demonstrated in a phantom that beta amyloid was diamagnetic and can generate strong contrast on susceptibility maps. Based on this, it is further shown both in vivo and ex vivo that magnetic susceptibility mapping could be used to detect accumulation of amyloid plaques in AD mouse models. Finally, the authors demonstrated that the diamagnetic susceptibility of beta amyloid can also be observed in brain specimens of AD patients.
Meaning: The ability to assess beta amyloid aggregation non-invasively with QSM MR imaging may aid the diagnosis of Alzheimer's disease.