Different computations over the same inputs produce selective behavior in algorithmic brain networks
This dataset includes raw data, manuscript codes and analysed results involved in the research Different Computations over the Same Inputs Produce Selective Behavior in Algorithmic Brain Networks published at eLife. Specifically, raw data is in the folder RawData, sub-named by participant ID, containing the MEG and behavior data per participant per trial; analysed data is in the AnalysedData, sub-named by participant ID (for per participant ) or "group" (at group level), sub-named by the measurement & participant ID, containing results of all the measurements mentioned by the research paper; manuscript codes is in the folder ManuscriptCode, sub-named by measurements, containing the manuscript code for data analysis. Here is the abstract of this research: 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.
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