Common and distinct BOLD correlates of Simon and flanker conflicts which can(not) be reduced to time-on-task effects.

The ability to identify and resolve conflicts between standard, well-trained behaviors and behaviors required by the current context is an essential feature of cognitive control. To date, no consensus has been reached on the brain mechanisms involved in exerting such control: while some studies identified diverse patterns of activity across different conflicts, other studies reported common resources across conflict tasks or even across simple tasks devoid of the conflict component. The latter reports attributed the entire activity observed in the presence of conflict to longer time spent on the task (i.e., to the so-called time-on-task effects). Here, we used an extended Multi-Source Interference Task (MSIT) which combines Simon and flanker types of interference to determine shared and conflict-specific mechanisms of conflict resolution in fMRI and their separability from the time-on-task effects. Large portions of the activity in the dorsal attention network and decreases of activity in the default mode network were shared across the tasks and scaled in parallel with increasing reaction times. Importantly, the activity in the sensory and sensorimotor cortices, as well as in the posterior medial frontal cortex (pMFC) - a key region implicated in conflict processing - could not be exhaustively explained by the time-on-task effects.

Pylabianca: comprehensive and user­friendly Python package for single­neuron data analysis.

In the area of electrophysiology, the availability of comprehensive and user­friendly tools for single-neuron data processing, statistical analysis, and fast, intuitive data visualization is limited. To address this gap, we introduce pylabianca, a Python library tailored for robust single and multi­unit data processing. Pylabianca leverages the power of standard Python packages and adopts the application programming interface of MNE­Python, one of the most widely used electrophysiology packages. One of pylabianca's primary objectives is to provide a low entry threshold for scientists, requiring only basic Python programming skills. Pylabianca was designed to streamline most common analyses of single neuron data, and provide convenient data structures to serve as a foundation for building custom analysis pipelines. We believe that pylabianca will contribute to enhancing researchers' capabilities and efficiency in the field of single-neuron electrophysiology.