Ito Takuya, Murray John D
Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA.
Thomas J. Watson Research Center, IBM Research, Yorktown Heights, NY, USA.
Netw Neurosci. 2024 Dec 10;8(4):1331-1354. doi: 10.1162/netn_a_00402. eCollection 2024.
State-dependent neural correlations can be understood from a neural coding framework. Noise correlations-trial-to-trial or moment-to-moment covariability-can be interpreted only if the underlying signal correlation-similarity of task selectivity between pairs of neural units-is known. Despite many investigations in local spiking circuits, it remains unclear how this coding framework applies to large-scale brain networks. Here, we investigated relationships between large-scale noise correlations and signal correlations in a multitask human fMRI dataset. We found that task-state noise correlation changes (e.g., functional connectivity) did not typically change in the same direction as their underlying signal correlation (e.g., tuning similarity of two regions). Crucially, noise correlations that changed in the opposite direction as their signal correlation (i.e., anti-aligned correlations) improved information coding of these brain regions. In contrast, noise correlations that changed in the same direction (aligned noise correlations) as their signal correlation did not. Interestingly, these aligned noise correlations were primarily correlation increases, suggesting that most functional correlation increases across fMRI networks actually degrade information coding. These findings illustrate that state-dependent noise correlations shape information coding of functional brain networks, with interpretation of correlation changes requiring knowledge of underlying signal correlations.
状态依赖的神经相关性可以从神经编码框架的角度来理解。噪声相关性——逐次试验或瞬间的协变性——只有在潜在的信号相关性（神经单元对之间任务选择性的相似性）已知时才能得到解释。尽管对局部脉冲发放回路进行了许多研究，但尚不清楚这种编码框架如何应用于大规模脑网络。在这里，我们在一个多任务人类功能磁共振成像（fMRI）数据集中研究了大规模噪声相关性与信号相关性之间的关系。我们发现，任务状态噪声相关性的变化（例如，功能连接性）通常与其潜在的信号相关性（例如，两个区域的调谐相似性）变化方向不同。至关重要的是，与信号相关性变化方向相反的噪声相关性（即反对齐相关性）改善了这些脑区的信息编码。相比之下，与信号相关性变化方向相同的噪声相关性（对齐噪声相关性）则没有。有趣的是，这些对齐噪声相关性主要是相关性增加，这表明功能磁共振成像网络中大多数功能相关性的增加实际上会降低信息编码。这些发现表明，状态依赖的噪声相关性塑造了功能性脑网络的信息编码，对相关性变化的解释需要了解潜在的信号相关性。
