Association of brain network efficiency with aging, depression, and cognition.
Association of brain network efficiency with aging, depression, and cognition.
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DOI：
10.1016/j.jagp.2013.10.004
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发表时间：
2014-02
影响因子：
7.2
通讯作者：
Kumar, Anand
中科院分区：
文献类型：
--
作者：
Ajilore, Olusola;Lamar, Melissa;Kumar, Anand
新开发的技术来理解大脑的连通性已经出现了应用图论为基础的措施，神经成像方式。然而，这些措施的认知相关性，特别是在临床诊断的背景下，如重度抑郁症，仍然知之甚少。本研究的目的是比较四种网络效率的措施，从新的技术来理解白色物质连接与衰老，抑郁症和认知的关联。横断面神经影像学研究一般社区我们招募了43名健康对照受试者和40名重度抑郁症受试者，他们自愿对广告做出反应。脑网络效率测量是使用脑连接性分析仪（www.brain-connectivity-toolbox.net）从扩散张量成像衍生的结构连接性矩阵生成的。信息加工速度和决策能力分别采用连线测验和目标交替任务进行评估。所有四个网络效率指标与年龄呈负相关。在抑郁组中，正常化的整体效率与抑郁的严重程度呈负相关，而整体效率的增加与对象交替的表现较差。脑网络效率的测量可能代表不同方面的潜在网络组织取决于人口和行为的问题。
Newly developed techniques for understanding brain connectivity have emerged with the application of graph theory-based measures to neuroimaging modalities. However, the cognitive correlates of these measures, particularly in the context of clinical diagnoses like major depression, are still poorly understood. The purpose of this study was to compare four measures of network efficiency derived from novel techniques for understanding white matter connectivity on their associations with aging, depression, and cognition. Cross-sectional neuroimaging study General community We recruited 43 healthy comparison subjects and 40 subjects with major depressive disorder who volunteered in response to advertisements. Brain network efficiency measures were generated from diffusion tensor imaging-derived structural connectivity matrices using the Brain Connectivity Toolbox (www.brain-connectivity-toolbox.net). Information processing speed and decision making were assessed with the Trail Making Test and the Object Alternation task, respectively. All four network efficiency measures correlated negatively with age. In the depressed group, normalized global efficiency was negatively correlated with depression severity while increasing global efficiency was associated with poorer performance on Object Alternation. Brain network efficiency measures may represent different aspects of underlying network organization depending on the population and behaviors in question.
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影响因子：
4.3
作者：
Achard S;Bullmore E
通讯作者：
Bullmore E
DOI：
10.1007/978-3-642-33418-4_25
发表时间：
2012
期刊：
LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
影响因子：
--
作者：
GadElkarim, Johnson J.;Schonfeld, Dan;Ajilore, Olusola;Zhan, Liang;Zhang, Aifeng F.;Feusner, Jamie D.;Thompson, Paul M.;Simon, Tony J.;Kumar, Anand;Leow, Alex D.
通讯作者：
Leow, Alex D.
DOI：
10.1523/jneurosci.4494-11.2012
发表时间：
2012-02-08
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Park J;Carp J;Kennedy KM;Rodrigue KM;Bischof GN;Huang CM;Rieck JR;Polk TA;Park DC
通讯作者：
Park DC
影响因子：
5.3
作者：
van den Heuvel, Martijn P.;Stam, Cornelis J.;Pol, Hilleke E. Hulshoff
通讯作者：
Pol, Hilleke E. Hulshoff
影响因子：
2.6
作者：
Lamar M;Charlton R;Zhang A;Kumar A
通讯作者：
Kumar A