Neighborhood disadvantage is associated with worse health and cognitive outcomes. Morphological similarity network (MSN) is a promising approach to elucidate cortical network patterns underlying complex cognitive functions. We hypothesized that MSNs could capture changes in cortical patterns related to neighborhood disadvantage and cognitive function. This cross-sectional study included cognitively unimpaired participants from two large Alzheimers studies at University of Wisconsin-Madison. Neighborhood disadvantage status was obtained using the Area Deprivation Index (ADI). Cognitive performance was assessed on memory, processing speed and executive function. Morphological Similarity Networks (MSN) were constructed for each participant based on the similarity in distribution of cortical thickness of brain regions, followed by computation of local and global network features. Association of ADI with cognitive scores and MSN features were examined using linear regression and mediation analysis. ADI showed negative association with category fluency,implicit learning speed, story recall and modified pre-clinical Alzheimers cognitive composite scores, indicating worse cognitive function among those living in more disadvantaged neighborhoods. Local network features of frontal and temporal regions differed based on ADI status. Centrality of left lateral orbitofrontal region showed a partial mediating effect between association of neighborhood disadvantage and story recall performance. Our preliminary findings suggest differences in local cortical organization by neighborhood disadvantage, which partially mediated the relationship between ADI and cognitive performance, providing a possible network-based mechanism to, in-part, explain the risk for poor cognitive functioning associated with disadvantaged neighborhoods.


翻译:暂无翻译

0
下载
关闭预览

相关内容

Cognition:Cognition:International Journal of Cognitive Science Explanation:认知:国际认知科学杂志。 Publisher:Elsevier。 SIT: http://www.journals.elsevier.com/cognition/
Keras François Chollet 《Deep Learning with Python 》, 386页pdf
专知会员服务
163+阅读 · 2019年10月12日
【SIGGRAPH2019】TensorFlow 2.0深度学习计算机图形学应用
专知会员服务
41+阅读 · 2019年10月9日
灾难性遗忘问题新视角:迁移-干扰平衡
CreateAMind
17+阅读 · 2019年7月6日
disentangled-representation-papers
CreateAMind
26+阅读 · 2018年9月12日
STRCF for Visual Object Tracking
统计学习与视觉计算组
15+阅读 · 2018年5月29日
IJCAI | Cascade Dynamics Modeling with Attention-based RNN
KingsGarden
13+阅读 · 2017年7月16日
国家自然科学基金
0+阅读 · 2015年12月31日
国家自然科学基金
0+阅读 · 2014年12月31日
国家自然科学基金
6+阅读 · 2014年12月31日
VIP会员
相关资讯
灾难性遗忘问题新视角:迁移-干扰平衡
CreateAMind
17+阅读 · 2019年7月6日
disentangled-representation-papers
CreateAMind
26+阅读 · 2018年9月12日
STRCF for Visual Object Tracking
统计学习与视觉计算组
15+阅读 · 2018年5月29日
IJCAI | Cascade Dynamics Modeling with Attention-based RNN
KingsGarden
13+阅读 · 2017年7月16日
相关基金
国家自然科学基金
0+阅读 · 2015年12月31日
国家自然科学基金
0+阅读 · 2014年12月31日
国家自然科学基金
6+阅读 · 2014年12月31日
Top
微信扫码咨询专知VIP会员