Selection of Seeds for Resting-State fMRI-Based Prediction of Individual Brain Maturity

被引：1
|
作者：
Scheel, Norman [1 ,2 ]
Essenwanger, Andrea [1 ]
Muente, Thomas F. [2 ]
Heldmann, Marcus [2 ]
Kraemer, Ulrike M. [2 ]
Mamlouk, Amir Madany [1 ]
机构：
[1] Univ Lubeck, Inst Neuro & Bioinformat, Lubeck, Germany
[2] Univ Lubeck, Dept Neurol, Lubeck, Germany
来源：
BILDVERARBEITUNG FUR DIE MEDIZIN 2015: ALGORITHMEN - SYSTEME - ANWENDUNGEN | 2015年
关键词：
D O I：
10.1007/978-3-662-46224-9_64
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The analysis of resting-state brain connectivity allows unraveling the fundamentals of functional brain organization. Especially changes of network connectivity related to age or diseases promise to serve as early biomarkers. After control of subject movement, we found that, when reaching a critical number of subjects, age prediction is reproducible for all seed selection strategies tested here (functional, anatomical and random based seeds). On the Enhanced Rockland Community Sample, we use support vector regression (SVR) and intense permutation testing for statistical validation.
引用
收藏
页码：371 / 376
页数：6
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