Cerliani Leonardo, Thomas Rajat M, Aquino Domenico, Contarino Valeria, Bizzi Alberto
Frontlab, Brain Connectivity and Behaviour, Institut du Cerveau et de la Moelle épinière (ICM), Paris, France; Inserm U1127, GH Pitié-Salpêtrière, Paris, France.
Department of Psychiatry, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
Cortex. 2017 Jan;86:247-259. doi: 10.1016/j.cortex.2016.11.017. Epub 2016 Dec 7.
The spatial pattern of task-related brain activity in fMRI studies might be expected to change according to several variables such as handedness and age. However this spatial heterogeneity might also be due to other unmodeled sources of inter-subject variability. Since group-level results reflect patterns of task-evoked brain activity common to most of the subjects in the sample, they could conceal the presence of subgroups recruiting other brain regions beyond the common pattern. To deal with these issues, data-driven methods can be used to detect the presence of sources of inter-subject variability that might be hard to identify and therefore model a priori. Here we assess the potential of Independent Component Analysis (ICA) to detect the presence of unexpected subgroups of participants. To this end, we acquired task-evoked fMRI data on 45 healthy adults using the verb generation (VGEN) task, in which participants are visually presented with the noun of an object of everyday use, and asked to covertly generate a verb describing the corresponding action. As expected, the task elicited activity in a temporo-parieto-frontal network typically found in previous VGEN experiments. We then quantified the contribution of every subject to nine task-related spatio-temporal processes identified by ICA. A cluster analysis of this quantity yielded three subgroups of participants. Differences between the three identified subgroups were distributed in left and right prefrontal, posterior parietal and extrastriate occipital regions. These results could not be explained by differences in sex, age or handedness across the participants. Furthermore, some regions where a significant difference was found between subgroups were not present in the group-level pattern of task-related activity. We discuss the potential application of this approach for characterizing brain activity in different subgroups of patients with neuropsychiatric or neurological conditions.
功能磁共振成像（fMRI）研究中与任务相关的大脑活动的空间模式可能会根据诸如利手和年龄等几个变量而发生变化。然而，这种空间异质性也可能归因于其他未建模的个体间变异性来源。由于组水平结果反映了样本中大多数受试者共有的任务诱发大脑活动模式，它们可能会掩盖在共同模式之外招募其他脑区的亚组的存在。为了解决这些问题，可以使用数据驱动方法来检测个体间变异性来源的存在，这些来源可能难以识别，因此难以先验建模。在这里，我们评估独立成分分析（ICA）检测意外参与者亚组存在的潜力。为此，我们使用动词生成（VGEN）任务获取了45名健康成年人的任务诱发fMRI数据，在该任务中，向参与者视觉呈现日常用品的名词，并要求他们暗中生成描述相应动作的动词。正如预期的那样，该任务在先前VGEN实验中通常发现的颞顶叶-额叶网络中引发了活动。然后，我们量化了每个受试者对ICA识别的九个与任务相关的时空过程的贡献。对该数量进行聚类分析产生了三个参与者亚组。三个已识别亚组之间的差异分布在左、右前额叶、顶叶后部和枕叶纹外区域。这些结果无法用参与者之间的性别、年龄或利手差异来解释。此外，亚组之间发现显著差异的一些区域在与任务相关活动的组水平模式中并不存在。我们讨论了这种方法在表征神经精神或神经疾病不同亚组患者大脑活动方面的潜在应用。
