Reliable identification of the auditory thalamus using multi-modal structural analyses.
Reliable identification of the auditory thalamus using multi-modal structural analyses.
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DOI：
10.1016/j.neuroimage.2005.11.025
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发表时间：
2006-05-01
期刊：
影响因子：
5.7
通讯作者：
Johansen-Berg H
中科院分区：
文献类型：
--
作者：
Devlin JT;Sillery EL;Hall DA;Hobden P;Behrens TE;Nunes RG;Clare S;Matthews PM;Moore DR;Johansen-Berg H
丘脑内侧膝状体（MGB）是听觉系统的重要组成部分。它参与将听觉信息传递和转换到皮层，并参与中脑、脑干和耳朵中自上而下的处理调制。然而，在人类这一地区的功能成像研究，一直受到限制，从其他丘脑核团区分MGB的困难。在这里，我们介绍了两种方法，可靠地划定MGB解剖在个人的基础上，常规和扩散MRI数据。第一种使用高分辨率质子密度加权扫描，针对皮质下灰白对比度进行优化。第二种方法使用弥散加权成像和概率纤维束成像，根据其与大脑其余部分的独特连接模式，自动将内侧和外侧膝状体核与周围结构分割开来。这两种方法都产生了与出版的地图集一致的高度可重复的结果。重要的是，这两种方法都依赖于常用的成像序列和标准硬件，这是优于先前描述的方法的显著优势。除了提供有用的方法来识别MGB和LGN在体内，我们的研究提供了进一步的验证扩散纤维束成像的基础上，其连接模式的灰质区域的parcellation。
The medial geniculate body (MGB) of the thalamus is a key component of the auditory system. It is involved in relaying and transforming auditory information to the cortex and in top-down modulation of processing in the midbrain, brainstem, and ear. Functional imaging investigations of this region in humans, however, have been limited by the difficulty of distinguishing MGB from other thalamic nuclei. Here we introduce two methods for reliably delineating MGB anatomically in individuals based on conventional and diffusion MRI data. The first uses high resolution proton-density weighted scanning optimised for subcortical grey-white contrast. The second uses diffusion-weighted imaging and probabilistic tractography to automatically segment the medial and lateral geniculate nuclei from surrounding structures based on their distinctive patterns of connectivity to the rest of the brain. Both methods produce highly replicable results that are consistent with published atlases. Importantly, both methods rely on commonly available imaging sequences and standard hardware, a significant advantage over previously described approaches. In addition to providing useful approaches for identifying the MGB and LGN in vivo, our study offers further validation of diffusion tractography for the parcellation of grey matter regions on the basis of their connectivity patterns.
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影响因子：
5.7
作者：
Budd, TW;Hall, DA;Summerfield, AQ
通讯作者：
Summerfield, AQ
影响因子：
3.3
作者：
Deoni, SCL;Peters, TM;Rutt, BK
通讯作者：
Rutt, BK
DOI：
10.1073/pnas.0403743101
发表时间：
2004-09-07
影响因子：
11.1
作者：
Johansen-Berg, H;Behrens, TEJ;Matthews, PM
通讯作者：
Matthews, PM
影响因子：
25
作者：
Beauchamp, MS;Argall, BD;Martin, A
通讯作者：
Martin, A
影响因子：
14.5
作者：
CELESIA, GG
通讯作者：
CELESIA, GG