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.

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