Detecting the subtle shape differences in hemodynamic responses at the group level.
Detecting the subtle shape differences in hemodynamic responses at the group level.
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DOI：
10.3389/fnins.2015.00375
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发表时间：
2015
影响因子：
4.3
通讯作者：
Cox RW
中科院分区：
文献类型：
--
作者：
Chen G;Saad ZS;Adleman NE;Leibenluft E;Cox RW
由于涉及多方面的过程，血流动力学反应 (HDR) 的本质仍未完全了解。除了总体幅度之外，响应可能会因认知状态、任务、大脑区域和受试者的不同而有所不同，例如上升和下降速度、峰值持续时间、下冲形状和总体持续时间等特征。在这里，我们证明固定形状（FSM）或调整形状（ASM）方法可能无法检测某些形状的细微差别（例如，上升或恢复的速度，或下冲）。相比之下，通过多个基函数的估计形状方法（ESM）可以提供识别一些细微形状差异的机会，并在个体和群体层面上实现更高的统计功效。此前，一些降维方法侧重于峰值幅度，或根据曲线下面积 (AUC) 或交互作用进行推断，这可能会导致潜在的错误识别。通过采用多元建模 (MVM) 的通用框架，我们展示了一种通过模拟和真实数据验证的混合方法。通过将整个 HDR 形状完整性保持为组级别的输入，该方法允许研究人员通过独特的 HDR 形状特征来证实这些更细微的效果。与少数仅限于主效应、双向或三向交互作用的分析不同，我们将建模方法扩展到比传统 GLM 更具适应性的包容性平台。通过 ESM 对每种情况的多重效应估计，当只有一组受试者且没有任何其他解释变量时，应在群体层面使用线性混合效应 (LME) 模型。在其他情况下，可以采用 MVM 框架内降维的近似方法来实现表示、误报控制、统计能力和建模灵活性之间的实际平衡。相关程序 3dMVM 作为 AFNI 套件的一部分公开提供。
The nature of the hemodynamic response (HDR) is still not fully understood due to the multifaceted processes involved. Aside from the overall amplitude, the response may vary across cognitive states, tasks, brain regions, and subjects with respect to characteristics such as rise and fall speed, peak duration, undershoot shape, and overall duration. Here we demonstrate that the fixed-shape (FSM) or adjusted-shape (ASM) methods may fail to detect some shape subtleties (e.g., speed of rise or recovery, or undershoot). In contrast, the estimated-shape method (ESM) through multiple basis functions can provide the opportunity to identify some subtle shape differences and achieve higher statistical power at both individual and group levels. Previously, some dimension reduction approaches focused on the peak magnitude, or made inferences based on the area under the curve (AUC) or interaction, which can lead to potential misidentifications. By adopting a generic framework of multivariate modeling (MVM), we showcase a hybrid approach that is validated by simulations and real data. With the whole HDR shape integrity maintained as input at the group level, the approach allows the investigator to substantiate these more nuanced effects through the unique HDR shape features. Unlike the few analyses that were limited to main effect, two- or three-way interactions, we extend the modeling approach to an inclusive platform that is more adaptable than the conventional GLM. With multiple effect estimates from ESM for each condition, linear mixed-effects (LME) modeling should be used at the group level when there is only one group of subjects without any other explanatory variables. Under other situations, an approximate approach through dimension reduction within the MVM framework can be adopted to achieve a practical equipoise among representation, false positive control, statistical power, and modeling flexibility. The associated program 3dMVM is publicly available as part of the AFNI suite.