In recent years, a wealth of Drosophila neuroscience data have become available including cell type, connectome/synaptome datasets for both the larva and adult fly. To facilitate integration across data modalities and to accelerate the understanding of the functional logic of the fly brain, we have developed FlyBrainLab, a unique open-source computing platform that integrates 3D exploration and visualization of diverse datasets with interactive exploration of the functional logic of modeled executable brain circuits. FlyBrainLab's User Interface, Utilities Libraries and Circuit Libraries bring together neuroanatomical, neurogenetic and electrophysiological datasets with computational models of different researchers for validation and comparison within the same platform. Seeking to transcend the limitations of the connectome/synaptome, FlyBrainLab also provides libraries for molecular transduction arising in sensory coding in vision/olfaction. Together with sensory neuron activity data, these libraries serve as entry points for the exploration, analysis, comparison and evaluation of circuit functions of the fruit fly brain.
Code Availability and InstallationStable and tested FlyBrainLab installation instructions for user-side components and utility libraries are available at https://github.com/FlyBrainLab/FlyBrainLab for Linux, MacOS and Windows. The installation and use of FlyBrainLab does not require a GPU, but a service-side backend must be running, for example, on a cloud service, that the user-side of FlyBrainLab can connect to. By default, the user-side-only installation will access the backend services hosted on our public servers. Note that users do not have write permission to the NeuroArch Database, nor will they be able to access a Neurokernel Server for execution. The server-side backend codebase is publicly available at https://github.com/fruitflybrain and https://github.com/neurokernel.A full installation of FlyBrainLab, including all backend and frontend components, is available as a Docker image at https://hub.docker.com/r/fruitflybrain/fbl. The image requires a Linux host with at least 1 CUDA-enabled GPU and the nvidia-docker package (https://github.com/NVIDIA/nvidia-docker) installed. For a custom installation of the complete FlyBrainLab platform, a shell script is available at https://github.com/FlyBrainLab/FlyBrainLab.To help users get started, a number of tutorials are available written as Jupyter notebooks at https://github.com/FlyBrainLab/Tutorials, including a reference to English queries at https://github.com/FlyBrainLab/Tutorials/blob/master/tutorials/getting_started/1b_nlp_queries.ipynb. An overview of the FlyBrainLab resources is available at https://github.com/FlyBrainLab/FlyBrainLab/wiki/FlyBrainLab-Resources.Data AvailabilityThe NeuroArch Database created from publicly available FlyCircuit, Hemibrain and Larva L1EM datasets can be downloaded from https://github.com/FlyBrainLab/dataset. The same repository provides Jupyter notebooks for loading publicly available datasets, such as the FlyCircuit dataset with inferred connectivity, the Hemibrain dataset and the Larva L1 EM dataset, into the NeuroArch Database.
- Mehmet Kerem Turkcan
- Aurel A Lazar
- Tingkai Liu
- Mehmet Kerem Turkcan
- Yiyin Zhou
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Upinder Singh Bhalla, Tata Institute of Fundamental Research, India
© 2021, Lazar et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Posterior cingulate cortex (PCC) is an enigmatic region implicated in psychiatric and neurological disease, yet its role in cognition remains unclear. Human studies link PCC to episodic memory and default mode network (DMN), while findings from the non-human primate emphasize executive processes more associated with the cognitive control network (CCN) in humans. We hypothesized this difference reflects an important functional division between dorsal (executive) and ventral (episodic) PCC. To test this, we utilized human intracranial recordings of population and single unit activity targeting dorsal PCC during an alternated executive/episodic processing task. Dorsal PCC population responses were significantly enhanced for executive, compared to episodic, task conditions, consistent with the CCN. Single unit recordings, however, revealed four distinct functional types with unique executive (CCN) or episodic (DMN) response profiles. Our findings provide critical electrophysiological data from human PCC, bridging incongruent views within and across species, furthering our understanding of PCC function.
Understanding how thought emerges from the topographical structure of the cerebral cortex is a primary goal of cognitive neuroscience. Recent work has revealed a principal gradient of intrinsic connectivity capturing the separation of sensory-motor cortex from transmodal regions of the default mode network (DMN); this is thought to facilitate memory-guided cognition. However, studies have not explored how this dimension of connectivity changes when conceptual retrieval is controlled to suit the context. We used gradient decomposition of informational connectivity in a semantic association task to establish how the similarity in connectivity across brain regions changes during familiar and more original patterns of retrieval. Multivoxel activation patterns at opposite ends of the principal gradient were more divergent when participants retrieved stronger associations; therefore, when long-term semantic information is sufficient for ongoing cognition, regions supporting heteromodal memory are functionally separated from sensory-motor experience. In contrast, when less related concepts were linked, this dimension of connectivity was reduced in strength as semantic control regions separated from the DMN to generate more flexible and original responses. We also observed fewer dimensions within the neural response towards the apex of the principal gradient when strong associations were retrieved, reflecting less complex or varied neural coding across trials and participants. In this way, the principal gradient explains how semantic cognition is organised in the human cerebral cortex: the separation of DMN from sensory-motor systems is a hallmark of the retrieval of strong conceptual links that are culturally shared.