Seemingly unrelated regression empowers detection of network failure in dementia

NEUROBIOLOGY OF AGING

卷:36

Article

Jahanshad, Neda^1,8  Nir, Talia M.¹  Toga, Arthur W.¹  Jack, Clifford R., Jr.²  Bernstein, Matt A.²  Weiner, Michael W.^3,4,5,6  Thompson, Paul M.^{1,7,8,9,10,11,12} 

[1] Univ So Calif, Keck Sch Med, Inst Neuroimaging & Informat, Imaging Genet Ctr, Los Angeles, CA 90033 USA

[2] Mayo Clin, Dept Radiol, Rochester, MN USA

[3] Univ Calif San Francisco, Dept Radiol, San Francisco, CA 94143 USA

[4] Univ Calif San Francisco, Dept Med, San Francisco, CA 94143 USA

[5] Univ Calif San Francisco, Dept Psychiat, San Francisco, CA 94143 USA

[6] Dept Vet Affairs Med Ctr, San Francisco, CA USA

[7] Univ So Calif, Dept Neurol, Los Angeles, CA 90033 USA

[8] Univ So Calif, Dept Psychiat, Los Angeles, CA 90033 USA

[9] Univ So Calif, Dept Radiol, Los Angeles, CA 90033 USA

[10] Univ So Calif, Dept Engn, Los Angeles, CA 90033 USA

[11] Univ So Calif, Dept Pediat, Los Angeles, CA 90033 USA

[12] Univ So Calif, Dept Ophthalmol, Los Angeles, CA 90033 USA

关键词: Brain connectivity;    Neuroimaging genetics;    HARDI tractography;    Seemingly unrelated regression (SUR);    APOE4;    Multivariate analysis;   

DOI  :  10.1016/j.neurobiolaging.2014.02.032

来源: Elsevier

【 摘 要 】

Brain connectivity is progressively disrupted in Alzheimer's disease (AD). Here, we used a seemingly unrelated regression (SUR) model to enhance the power to identify structural connections related to cognitive scores. We simultaneously solved regression equations with different predictors and used correlated errors among the equations to boost power for associations with brain networks. Connectivity maps were computed to represent the brain's fiber networks from diffusion-weighted magnetic resonance imaging scans of 200 subjects from the Alzheimer's Disease Neuroimaging Initiative. We first identified a pattern of brain connections related to clinical decline using standard regressions powered by this large sample size. As AD studies with a large number of diffusion tensor imaging scans are rare, it is important to detect effects in smaller samples using simultaneous regression modeling like SUR. Diagnosis of mild cognitive impairment or AD is well known to be associated with ApoE genotype and educational level. In a subsample with no apparent associations using the general linear model, power was boosted with our SUR model-combining genotype, educational level, and clinical diagnosis. (C) 2015 Elsevier Inc. All rights reserved.

【 授权许可】

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