We collected brain magnetic resonance image (MRI) of 2,000 healthy Japanese and constructed a large scale database combined with their personal and medical history. Using anatomical standardization technique and voxel based statistics of the images, we analyzed age-related structural change of the brain. Simple volumetry or voxel based volumetry analyses of the database revealed the following results. 1) Gray matter volume linearly decreased with age, while the white matter did not show significant changes. 2) Hypertension, alcohol intake, and smoking correlated with gray matter volume decrease in regional specific brain regions. 3) Gray matter volume decreased in the subjects with subthreshold depression in the medial part of bilateral frontal lobes and the right precentral gyros than that of the normal male subjects. 4) We developed an algorism for automatic detection of ischemic changes in the white matter. 5) By using anatomical standardization technique, we defined a standard brain shape for each age group. We also constructed a database for cerebral blood flow image (CBF) of the normal subjects, as well as the subject with degenerative brain diseased and developed an automated diagnosis system for brain CBF using anatomical standardization technique and machine learning system.
机构:
Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China
Beijing Normal Univ, IDG McGovern Inst Brain Res, Dev Populat Neurosci Res Ctr, Beijing 100875, Peoples R ChinaBeijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China
Chen, Li-Zhen
Zuo, Xi-Nian
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机构:
Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China
Beijing Normal Univ, IDG McGovern Inst Brain Res, Dev Populat Neurosci Res Ctr, Beijing 100875, Peoples R China
Natl Basic Sci Data Ctr, Beijing 100190, Peoples R ChinaBeijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100875, Peoples R China
Zuo, Xi-Nian
CHINESE SCIENCE BULLETIN-CHINESE,
2024,
69
(24):
: 3651
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3665