Neural timescales reflect behavioral demands in freely moving rhesus macaques

Previous work demonstrated a highly reproducible cortical hierarchy of neural timescales at rest, with sensory areas displaying fast, and higher-order association areas displaying slower timescales. The question arises as to how such stable hierarchies give rise to adaptive behavior that requires flexible adjustment of temporal coding and integration demands. Potentially, this lack of variability in the hierarchical organization of neural timescales could reflect the structure of the laboratory contexts. We posit that unconstrained paradigms are ideal to test whether the dynamics of neural timescales reflect behavioral demands. Here we measured timescales of local field potential activity while male rhesus macaques foraged in an open space. We found a hierarchy of neural timescales that differs from previous work. Importantly, although the magnitude of neural timescales expanded with task engagement, the brain areas’ relative position in the hierarchy was stable. Next, we demonstrated t..., , , # Neural timescales reflect behavioral demands in freely moving rhesus macaques

[https://doi.org/10.5061/dryad.8sf7m0cx1](https://doi.org/10.5061/dryad.8sf7m0cx1)

This dataset consists of the neural timescales (in ms) estimated from local field potential activity in freely moving macaques.

## Description of the data and file structure

Session-wide neural timescales (Figure 2): session_wide_timescales.mat

Baseline neural timescales (Figure 3): resting_baseline_timescales.mat

Neural timescales before and after the first (LP1) and final (LPf) lever presses (Figure 3): LP_timescales.mat

The change in neural timescales from the baseline before and after the first (LP1) and final (LPf) lever presses (Figure 4): LP_change.mat

Before-after change in neural timescales for first lever press (LP1), final lever presses (LPf), stay lever presses, and leave lever presses (Figure 5): BA_change.mat

## Code/Software

Neural timescales were estimated using the FOOOF toolbox.

Donoghue T, Hall...

既往研究已证实，静息状态下皮层神经时间尺度存在高度可重复的层级结构：感觉脑区呈现较快的时间尺度，而高级联合脑区的时间尺度则相对更慢。由此产生一个核心问题：这类稳定的层级结构，如何支撑需要灵活调节时间编码与整合需求的适应性行为？现有研究中神经时间尺度层级组织缺乏变异性，这可能反映了实验室实验环境的固有结构局限。我们提出，无约束实验范式是检验神经时间尺度动态变化是否匹配行为需求的理想手段。本研究中，我们对在开放空间中觅食的雄性恒河猴的局部场电位（local field potential）活动进行了神经时间尺度测量。我们发现了与既往研究不同的神经时间尺度层级结构。值得注意的是，尽管神经时间尺度的幅值随任务参与程度扩展，但各脑区在层级中的相对位置保持稳定。随后，我们证实了……，，，# 自由活动恒河猴的神经时间尺度反映行为需求
[https://doi.org/10.5061/dryad.8sf7m0cx1](https://doi.org/10.5061/dryad.8sf7m0cx1)
本数据集包含从自由活动恒河猴局部场电位活动中估算得到的神经时间尺度（单位：毫秒）。

## 数据与文件结构说明
全会话神经时间尺度（图2）：session_wide_timescales.mat
基线神经时间尺度（图3）：resting_baseline_timescales.mat
首次（LP1）与末次（LPf）压杆前后的神经时间尺度（图3）：LP_timescales.mat
首次（LP1）与末次（LPf）压杆前后相对于基线的神经时间尺度变化量（图4）：LP_change.mat
首次压杆（LP1）、末次压杆（LPf）、停留压杆与离开压杆对应的神经时间尺度前后变化量（图5）：BA_change.mat

## 代码与软件
神经时间尺度通过FOOOF工具箱估算得到。
Donoghue T, Hall...