MR connectomics: Principles and challenges

被引：205

作者：

Hagmann, Patric ^{[1
,2
,3
,4
]}

Cammoun, Leila ^{[2
]}

Gigandet, Xavier ^{[2
]}

Gerhard, Stephan ^{[2
]}

Grant, P. Ellen ^{[3
,4
,5
]}

Wedeen, Van ^{[5
]}

Meuli, Reto ^{[1
]}

Thiran, Jean-Philippe ^{[2
]}

Honey, Christopher J. ^{[6
]}

Sporns, Olaf ^{[7
]}

机构：

[1] Univ Lausanne CHUV UNIL, Lausanne, Switzerland

[2] Ecole Polytech Fed Lausanne, Signal Proc Lab LTS5, Lausanne, Switzerland

[3] Childrens Hosp Boston, Div Newborn Med, Dept Med, Boston, MA USA

[4] Childrens Hosp Boston, Dept Radiol, Boston, MA USA

[5] Athinoula A Martinos Ctr Biomed Imaging, Charlestown, MA USA

[6] Princeton Univ, Dept Psychol, Princeton, NJ 08544 USA

[7] Indiana Univ, Dept Psychol & Brain Sci, Bloomington, IN USA

来源：

JOURNAL OF NEUROSCIENCE METHODS | 2010年 / 194卷 / 01期

关键词：

Diffusion MRI; Tractography; Networks; Connectivity; Validation; Resting-state fMRI; Database; DIFFUSION-WEIGHTED MRI; HUMAN CEREBRAL-CORTEX; INTRINSIC FUNCTIONAL CONNECTIVITY; PERSISTENT ANGULAR STRUCTURE; AUTISM SPECTRUM DISORDERS; HUMAN BRAIN CONNECTIVITY; DEFAULT MODE; SMALL-WORLD; STRUCTURAL CONNECTIVITY; WATER DIFFUSION;

D O I：

10.1016/j.jneumeth.2010.01.014

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

MR connectomics is an emerging framework in neuro-science that combines diffusion MRI and whole brain tractography methodologies with the analytical tools of network science. In the present work we review the current methods enabling structural connectivity mapping with MRI and show how such data can be used to infer new information of both brain structure and function. We also list the technical challenges that should be addressed in the future to achieve high-resolution maps of structural connectivity. From the resulting tremendous amount of data that is going to be accumulated soon, we discuss what new challenges must be tackled in terms of methods for advanced network analysis and visualization, as well data organization and distribution. This new framework is well suited to investigate key questions on brain complexity and we try to foresee what fields will most benefit from these approaches. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：34 / 45

页数：12

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