Fafrowicz Magdalena, Tutajewski Marcin, Sieradzki Igor, Ochab Jeremi K, Ceglarek-Sroka Anna, Lewandowska Koryna, Marek Tadeusz, Sikora-Wachowicz Barbara, Podolak Igor T, Oświęcimka Paweł
Department of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, Kraków, Poland.
Institute of Theoretical Physics, Jagiellonian University, Kraków, Poland.
Front Neuroinform. 2024 Dec 20;18:1480366. doi: 10.3389/fninf.2024.1480366. eCollection 2024.
Understanding brain function relies on identifying spatiotemporal patterns in brain activity. In recent years, machine learning methods have been widely used to detect connections between regions of interest (ROIs) involved in cognitive functions, as measured by the fMRI technique. However, it's essential to match the type of learning method to the problem type, and extracting the information about the most important ROI connections might be challenging. In this contribution, we used machine learning techniques to classify tasks in a working memory experiment and identify the brain areas involved in processing information. We employed classical discriminators and neural networks (convolutional and residual) to differentiate between brain responses to distinct types of visual stimuli (visuospatial and verbal) and different phases of the experiment (information encoding and retrieval). The best performance was achieved by the LGBM classifier with 1-time point input data during memory retrieval and a convolutional neural network during the encoding phase. Additionally, we developed an algorithm that took into account feature correlations to estimate the most important brain regions for the model's accuracy. Our findings suggest that from the perspective of considered models, brain signals related to the resting state have a similar degree of complexity to those related to the encoding phase, which does not improve the model's accuracy. However, during the retrieval phase, the signals were easily distinguished from the resting state, indicating their different structure. The study identified brain regions that are crucial for processing information in working memory, as well as the differences in the dynamics of encoding and retrieval processes. Furthermore, our findings indicate spatiotemporal distinctions related to these processes. The analysis confirmed the importance of the basal ganglia in processing information during the retrieval phase. The presented results reveal the benefits of applying machine learning algorithms to investigate working memory dynamics.
理解大脑功能依赖于识别大脑活动中的时空模式。近年来，机器学习方法已被广泛用于检测通过功能磁共振成像（fMRI）技术测量的、参与认知功能的感兴趣区域（ROI）之间的连接。然而，将学习方法的类型与问题类型相匹配至关重要，并且提取关于最重要的ROI连接的信息可能具有挑战性。在本论文中，我们使用机器学习技术对工作记忆实验中的任务进行分类，并识别参与信息处理的脑区。我们采用经典判别器和神经网络（卷积和残差网络）来区分大脑对不同类型视觉刺激（视觉空间和语言）以及实验不同阶段（信息编码和检索）的反应。在记忆检索期间，使用具有1个时间点输入数据的LightGBM分类器，在编码阶段使用卷积神经网络，取得了最佳性能。此外，我们开发了一种算法，该算法考虑了特征相关性，以估计对模型准确性最重要的脑区。我们的研究结果表明，从所考虑的模型角度来看，与静息状态相关的脑信号与与编码阶段相关的脑信号具有相似的复杂程度，这并不能提高模型的准确性。然而，在检索阶段，信号很容易与静息状态区分开来，表明它们具有不同的结构。该研究确定了对工作记忆中的信息处理至关重要的脑区，以及编码和检索过程动态的差异。此外，我们的研究结果表明了与这些过程相关的时空差异。分析证实了基底神经节在检索阶段处理信息中的重要性。所呈现的结果揭示了应用机器学习算法来研究工作记忆动态的益处。
