Region-based conversion of neural activity across sessions
Region-based conversion of neural activity across sessions
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DOI：
10.1109/ner52421.2023.10123786
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发表时间：
2023-04
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影响因子：
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通讯作者：
Woohyun Eum;Carlton Smith;S. Saxena
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作者：
Woohyun Eum;Carlton Smith;S. Saxena
增进我们对大脑处理的理解的一种常见方法是从记录的神经信号中解码行为。为了研究学习任务的神经关联，我们希望解码整个学习时间跨度的行为，这可能需要多天的多次记录。然而，由于神经记录中会话间的大量变化，跨会话的解码受到阻碍。在这里，我们建议利用来自局部半非负矩阵分解处理（LocaNMF）的多维神经信号以及跨会话的高行为相关性，以及一种新颖的数据增强方法和基于区域的转换器，以最佳地对齐神经记录。当小鼠学习决策任务时，我们将我们的方法应用于多个会话的宽场钙活动。我们首先将每个会话的神经活动分解为基于区域的空间和时间分量，这些分量可以重建具有高方差的数据。接下来，我们对神经数据进行数据增强，以平滑试验之间的变异性。最后，我们设计了一个跨会话的基于区域的神经转换器，将一个会话的神经信号转换为另一个会话的神经信号，同时保留其维度。我们通过解码小鼠在决策任务中的行为来测试我们的方法，发现我们的方法在分析跨会话的神经活动时优于使用纯粹解剖信息的方法。通过在跨会话转换神经活动的同时保留神经数据的高维性，我们的方法可用于进一步分析跨会话的神经数据和学习的神经相关性。
A common way to advance our understanding of brain processing is to decode behavior from recorded neural signals. In order to study the neural correlates of learning a task, we would like to decode behavior across the entire timespan of learning, which can take multiple recording sessions across many days. However, decoding across sessions is hindered due to a high amount of session-to-session variability in neural recordings. Here, we propose utilizing multidimensional neural signals from Localized semi-non negative matrix factorization processing (LocaNMF) with high behavioral correlations across sessions, as well as a novel data augmentation method and region-based converter, to optimally align neural recordings. We apply our method to widefield calcium activity across many sessions while a mouse learns a decision-making task. We first decompose each session's neural activity into region-based spatial and temporal components that can reconstruct the data with high variance. Next, we perform data augmentation of the neural data to smooth the variability across trials. Finally, we design a region-based neural converter across sessions that transforms one session's neural signals into another while preserving its dimensionality. We test our approach by decoding the mouse's behavior in the decision-making task, and find that our method outperforms approaches that use purely anatomical information while analyzing neural activity across sessions. By preserving the high dimensionality in the neural data while converting neural activity across sessions, our method can be used towards further analyses of neural data across sessions and the neural correlates of learning.