Data representing logical computations along four stages in the brain

A key challenge in neuroimaging remains to understand where, when and now particularly how human brain networks compute over sensory inputs to achieve behavior. To study such dynamic algorithms from mass neural signals, we recorded the magnetoencephalographic (MEG) activity of participants who resolved the classic XOR, OR and AND functions as overt behavioral tasks (N = 10 participants/task, N-of-1 replications). Each function requires a different computation over the same inputs to produce the task- specific behavioral outputs. In each task, we found that source-localized MEG activity progresses through four computational stages identified within individual participants: (1) initial contra-lateral representation of each visual input in occipital cortex, (2) a joint linearly combined representation of both inputs in midline occipital cortex and right fusiform gyrus, followed by (3) nonlinear task-dependent input integration in temporal-parietal cortex and finally (4) behavioral response representation in post-central gyrus. We demonstrate the specific dynamics of each computation at the level of individual sources. The spatio-temporal patterns of the first two computations are similar across the three tasks; the last two computations are task specific. Our results therefore reveal where, when and how dynamic network algorithms perform different computations over the same inputs to produce different behaviors.

Methods
This dataset includes source data and the related manuscript codes involved in the research Different Computations over the Same Inputs Produce Selective Behavior in Algorithmic Brain Networks published at eLife. Specifically, we provide all analyzed data reported in the paper including:

Figure 1&3 related – Source Data in FigureSource1.mat.

1_DistancePattern.m

2_LinearRep.m

3_NonLinRep.m

Figure 2 related – Source Data in FigureSource2.mat

4_DynaCoord.m

神经影像学领域的核心挑战之一，仍是探明人类脑网络在何时、何地，尤其是如何对感觉输入进行运算以实现行为表现。为了从大规模神经信号中研究这类动态运算算法，我们记录了完成经典异或（XOR）、或（OR）与与（AND）逻辑运算作为外显行为任务的参与者的脑磁图（magnetoencephalographic, MEG）活动（每项任务包含10名参与者，采用单被试重复实验设计）。每项任务均需对相同的输入执行差异化运算，以生成任务特异性的行为输出。

在每项任务中，我们发现经源定位处理的脑磁图活动会经历四个可在个体参与者中识别的运算阶段：（1）视觉皮层内各视觉输入的初始对侧表征；（2）枕叶中线皮层与右侧梭状回内两种输入的联合线性组合表征；随后（3）颞顶皮层内依赖于任务的非线性输入整合，最终（4）中央后回内的行为反应表征。我们在单个神经源层面展示了每种运算的特定动态过程。前两种运算的时空模式在三项任务中具有一致性，而后两种运算则具备任务特异性。因此，本研究结果揭示了动态网络算法在相同输入上执行不同运算以产生不同行为的具体时空位置、时间进程与运作机制。

### 方法
本数据集包含本研究涉及的源数据与配套手稿代码，对应发表于《eLife》的论文《算法脑网络：相同输入下的差异化运算可产生选择性行为》。具体而言，我们提供了论文中报告的全部分析数据，包括：
与图1、图3相关的数据：FigureSource1.mat
1_DistancePattern.m
2_LinearRep.m
3_NonLinRep.m
与图2相关的数据：FigureSource2.mat
4_DynaCoord.m