Kia Seyed Mostafa, Pedregosa Fabian, Blumenthal Anna, Passerini Andrea
Doctoral School in Information and Communication Technology, Via Sommarive, 9 I-38123 Povo, Trento, Italy.
Doctoral School in Information and Communication Technology, Via Sommarive, 9 I-38123 Povo, Trento, Italy.
J Neurosci Methods. 2017 Jun 15;285:97-108. doi: 10.1016/j.jneumeth.2017.05.004. Epub 2017 May 8.
The use of machine learning models to discriminate between patterns of neural activity has become in recent years a standard analysis approach in neuroimaging studies. Whenever these models are linear, the estimated parameters can be visualized in the form of brain maps which can aid in understanding how brain activity in space and time underlies a cognitive function. However, the recovered brain maps often suffer from lack of interpretability, especially in group analysis of multi-subject data.
To facilitate the application of brain decoding in group-level analysis, we present an application of multi-task joint feature learning for group-level multivariate pattern recovery in single-trial magnetoencephalography (MEG) decoding. The proposed method allows for recovering sparse yet consistent patterns across different subjects, and therefore enhances the interpretability of the decoding model.
Our experimental results demonstrate that the mutli-task joint feature learning framework is capable of recovering more meaningful patterns of varying spatio-temporally distributed brain activity across individuals while still maintaining excellent generalization performance.
We compare the performance of the multi-task joint feature learning in terms of generalization, reproducibility, and quality of pattern recovery against traditional single-subject and pooling approaches on both simulated and real MEG datasets.
These results can facilitate the usage of brain decoding for the characterization of fine-level distinctive patterns in group-level inference. Considering the importance of group-level analysis, the proposed approach can provide a methodological shift towards more interpretable brain decoding models.
近年来，使用机器学习模型来区分神经活动模式已成为神经影像学研究中的一种标准分析方法。只要这些模型是线性的，估计参数就可以以脑图谱的形式可视化，这有助于理解大脑活动在空间和时间上如何构成认知功能的基础。然而，恢复的脑图谱往往缺乏可解释性，尤其是在多主体数据的组分析中。
为了促进脑解码在组水平分析中的应用，我们提出了一种多任务联合特征学习在单试次脑磁图（MEG）解码中用于组水平多变量模式恢复的应用。所提出的方法允许在不同主体间恢复稀疏但一致的模式，从而提高了解码模型的可解释性。
我们的实验结果表明，多任务联合特征学习框架能够在个体间恢复更有意义的、时空分布各异的大脑活动模式，同时仍保持出色的泛化性能。
我们在模拟和真实的MEG数据集上，将多任务联合特征学习在泛化、可重复性和模式恢复质量方面的性能与传统的单主体和合并方法进行了比较。
这些结果有助于在组水平推断中使用脑解码来表征精细水平的独特模式。考虑到组水平分析的重要性，所提出的方法可以为更具可解释性的脑解码模型提供一种方法学上的转变。
