Predictors for cortical gray matter volume in stroke patients with confluent white matter changes.
OBJECTIVE
Our previous study found that cortical gray matter (cGM) volume predicted vascular cognitive impairment independent of age-related white matter changes (WMC). We aimed to investigate predictors for cGM volume in ischemic stroke patients with confluent WMC.


METHODS
One-hundred post-stroke patients with confluent WMC were recruited into the study. All volumetric measures were standardized by intracranial volume as volume ratio. Univariate analyses and multivariate linear regression models were used to test relationship of cGM volume with basic demography, vascular risk factors, APOE status, WMC volume (periventricular and deep WMC), infarct measures (volume, number and location) and microbleed (number, presence and location).


RESULTS
After controlling for significant variables in the univariate analyses, multivariate linear regression models found that old age (β=-0.288, p=0.001), low triglyceride (β=0.194, p=0.027), periventricular WMC (PVWMC) (β=-0.392, p<0.001) and presence of thalamic microbleed (β=-0.197, p=0.041) were independently predictive of less cGM volume ratio.


CONCLUSIONS
Age, PVWMC and left thalamic microbleed predict less cGM volume.
Department of Neurology, Jinling Hospital, Nanjing University School of Medicine, 305# East Zhongshan Road, Nanjing, Jiangsu Province, People's Republic of China; Department of Psychological Studies and Center for Psychosocial Health and Aging, The Hong Kong Institute of Education, China.
Yunyun
Y
Xiong
Department of Psychological Studies and Center for Psychosocial Health and Aging, The Hong Kong Institute of Education, China.
Adrian
A
Wong
Bioinformatics and Imaging Programmatic Cores, The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, TX, USA.
Kelvin
K
Wong
Department of Radiology and Organ Imaging, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region, China.
Winnie C W
WC
Chu
Bioinformatics and Imaging Programmatic Cores, The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, TX, USA.
Xintao
X
Hu
Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region, China.
Xiangyan
X
Chen
Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region, China.
Ka Sing
KS
Wong
Bioinformatics and Imaging Programmatic Cores, The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, TX, USA.
Stephen T C
ST
Wong
Department of Neurology, Jinling Hospital, Nanjing University School of Medicine, 305# East Zhongshan Road, Nanjing, Jiangsu Province, People's Republic of China.
Xinfeng
X
Liu
Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region, China. Electronic address: vctmok@cuhk.edu.hk.
Vincent
V
Mok
D000368:Aged; D000369:Aged, 80 and over; D000704:Analysis of Variance; D001786:Blood Glucose; D001921:Brain / Q000473:pathology*; D002784:Cholesterol / Q000097:blood; D003072:Cognition Disorders / Q000209:etiology; D004311:Double-Blind Method; D005260:Female; D006801:Humans; D056784:Leukoencephalopathies / Q000209:etiology*; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D008297:Male; D011237:Predictive Value of Tests; D020521:Stroke / Q000150:complications* / Q000473:pathology*
D001786:Blood Glucose; D002784:Cholesterol
Brain atrophy; Cortical gray matter; Microbleed; Small vessel disease; Triglyceride; White matter changes