A novel stroke lesion network mapping approach: improved accuracy yet still low deficit prediction.
A novel stroke lesion network mapping approach: improved accuracy yet still low deficit prediction.
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DOI：
10.1093/braincomms/fcab259
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发表时间：
2021
影响因子：
4.8
通讯作者：
Corbetta M
中科院分区：
文献类型：
--
作者：
Pini L;Salvalaggio A;De Filippo De Grazia M;Zorzi M;Thiebaut de Schotten M;Corbetta M
病变网络映射估计由局灶性脑病变引起的功能网络异常。该方法需要将病变体积嵌入规范的功能连接组中，并使用该体积的平均功能磁共振成像信号来计算与所有其他大脑位置的时间相关性。病变网络映射产生潜在功能断开区域的图。尽管很有希望，但这种方法并不能很好地预测行为缺陷。我们通过使用从病变区域内的体素计算出的功能磁共振成像信号的第一主成分来修改病变网络映射，以实现时间相关性。我们测量了一大群首次中风患者在受伤后 2 周时的连接强度、受损网络的解剖特异性和行为预测的潜在改善 (n = 123)。这种主成分功能断开方法主要定位于高信噪比的皮质体素；与标准方法相比，它产生的网络具有更高的解剖特异性和更强的行为相关性。然而，当使用严格的留一法机器学习方法进行检查时，主成分功能断开方法在预测神经缺陷方面的表现并不比标准病变网络映射更好。总之，尽管我们的新方法提高了断开网络的特异性并与中风后的行为缺陷相关，但它并没有改善临床预测。需要进一步的工作来捕获与行为相关的焦点损伤所产生的功能网络的复杂调整。皮尼等人。开发了一种中风“病变网络映射”的新方法。这种新方法能够更准确地估计中风时断开的功能性大脑网络。然而，该方法不能很好地预测神经损伤，限制了其在临床问题上的应用。
Lesion network mapping estimates functional network abnormalities caused by a focal brain lesion. The method requires embedding the volume of the lesion into a normative functional connectome and using the average functional magnetic resonance imaging signal from that volume to compute the temporal correlation with all other brain locations. Lesion network mapping yields a map of potentially functionally disconnected regions. Although promising, this approach does not predict behavioural deficits well. We modified lesion network mapping by using the first principal component of the functional magnetic resonance imaging signal computed from the voxels within the lesioned area for temporal correlation. We measured potential improvements in connectivity strength, anatomical specificity of the lesioned network and behavioural prediction in a large cohort of first-time stroke patients at 2-weeks post-injury (n = 123). This principal component functional disconnection approach localized mainly cortical voxels of high signal-to-noise; and it yielded networks with higher anatomical specificity, and stronger behavioural correlation than the standard method. However, when examined with a rigorous leave-one-out machine learning approach, principal component functional disconnection approach did not perform better than the standard lesion network mapping in predicting neurological deficits. In summary, even though our novel method improves the specificity of disconnected networks and correlates with behavioural deficits post-stroke, it does not improve clinical prediction. Further work is needed to capture the complex adjustment of functional networks produced by focal damage in relation to behaviour. Pini et al. developed a new methodology for ‘lesion network mapping’ in stroke. This new methodology enabled a more accurate estimation of functional brain network disconnected in stroke. However, the method did not predict well neurological impairment limiting its application to clinical questions.
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DOI：
10.1073/pnas.0504136102
发表时间：
2005-07-05
影响因子：
11.1
作者：
Fox, MD;Snyder, AZ;Raichle, ME
通讯作者：
Raichle, ME
影响因子：
16.2
作者：
Corbetta M;Ramsey L;Callejas A;Baldassarre A;Hacker CD;Siegel JS;Astafiev SV;Rengachary J;Zinn K;Lang CE;Connor LT;Fucetola R;Strube M;Carter AR;Shulman GL
通讯作者：
Shulman GL
影响因子：
16.6
作者：
Ferguson, Michael A.;Lim, Chun;Fox, Michael D.
通讯作者：
Fox, Michael D.
影响因子：
14.5
作者：
Baldassarre, Antonello;Ramsey, Lenny;Corbetta, Maurizio
通讯作者：
Corbetta, Maurizio
DOI：
10.1093/cercor/bhp120
发表时间：
2010-03
期刊：
Cerebral cortex (New York, N.Y. : 1991)
影响因子：
--
作者：
Brownsett SL;Wise RJ
通讯作者：
Wise RJ