50 research outputs found
Sebastien Tourbier's Quick Files
No full textThe Quick Files feature was discontinued and it’s files were migrated into this Project on March 11, 2022. The file URL’s will still resolve properly, and the Quick Files logs are available in the Project’s Recent Activity
Sebastien Tourbier's Quick Files
No full textThe Quick Files feature was discontinued and it’s files were migrated into this Project on March 11, 2022. The file URL’s will still resolve properly, and the Quick Files logs are available in the Project’s Recent Activity
Sebastien Tourbier's Quick Files
No full textThe Quick Files feature was discontinued and it’s files were migrated into this Project on March 11, 2022. The file URL’s will still resolve properly, and the Quick Files logs are available in the Project’s Recent Activity
Structure supports function: Informing directed and dynamic functional connectivity with anatomical priors
No full textThe dynamic repertoire of functional brain networks is constrained by the underlying topology of structural connections. Despite this intrinsic relationship between structural connectivity (SC) and functional connectivity (FC), integrative and multimodal approaches to combine the two remain limited. Here, we propose a new adaptive filter for estimating dynamic and directed FC using structural connectivity information as priors. We tested the filter in rat epicranial recordings and human event-related EEG data, using SC priors from a meta-analysis of tracer studies and diffusion tensor imaging metrics, respectively. We show that, particularly under conditions of low signal-to-noise ratio, SC priors can help to refine estimates of directed FC, promoting sparse functional networks that combine information from structure and function. In addition, the proposed filter provides intrinsic protection against SC-related false negatives, as well as robustness against false positives, representing a valuable new tool for multimodal imaging in the context of dynamic and directed FC analysis
Using structural connectivity to augment community structure in EEG functional connectivity
No full textRecently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle from spurious FC introduced by volume conduction. Here, we investigate the relationship between white matter structural connectivity (SC) and large-scale network structure encoded in EEG-FC. We start by confirming that FC (power envelope correlations) is predicted by SC beyond the impact of Euclidean distance, in line with the assumption that SC mediates genuine FC. We then use information from white matter structural connectivity in order to smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions increases while FC between nonconnected regions remains unchanged, resulting in an increase in genuine, SC-mediated FC. We analyze the induced changes in FC, assessing the resemblance between EEG-FC and volume-conduction- free fMRI-FC, and find that smoothing increases resemblance in terms of overall correlation and community structure. This result suggests that our method boosts genuine FC, an outcome that is of interest for many EEG network neuroscience questions
Connectome Mapping ToolKit LIBrary (CMTKLIB): Data resources
No full textThis component was built from a DataLad dataset using the datalad-osf extension (https://github.com/datalad/datalad-osf). With this extension installed, this component can be git or datalad cloned from a 'osf://ID' URL, where 'ID' is the OSF node ID that shown in the OSF HTTP URL, e.g. https://osf.io/q8xnk can be cloned from osf://q8xnk. This particular project can be cloned using 'datalad clone osf://5hy42
Connectome spectral analysis to track EEG task dynamics on a subsecond scale
No full textWe present an approach for tracking fast spatiotemporal cortical dynamics in which we combine white matter connectivity data with source-projected electroencephalographic (EEG) data. We employ the mathematical framework of graph signal processing in order to derive the Fourier modes of the brain structural connectivity graph, or "network harmonics" . These network harmonics are naturally ordered by smoothness. Smoothness in this context can be understood as the amount of variation along the cortex, leading to a multi-scale representation of brain connectivity. We demonstrate that network harmonics provide a sparse representation of the EEG signal, where, at certain times, the smoothest 15 network harmonics capture 90% of the signal power. This suggests that network harmonics are functionally meaningful, which we demonstrate by using them as a basis for the functional EEG data recorded from a face detection task. There, only 13 network harmonics are sufficient to track the large-scale cortical activity during the processing of the stimuli with a 50 ms resolution, reproducing well-known activity in the fusiform face area as well as revealing co-activation patterns in somatosensory/motor and frontal cortices that an unconstrained ROI-by-ROI analysis fails to capture. The proposed approach is simple and fast, provides a means of integration of multimodal datasets, and is tied to a theoretical framework in mathematics and physics. Thus, network harmonics point towards promising research directions both theoretically - for example in exploring the relationship between structure and function in the brain - and practically - for example for network tracking in different tasks and groups of individuals, such as patients
Connectome Mapping ToolKit LIBrary (CMTKLIB): Data resources
No full textThis component was built from a DataLad dataset using the datalad-osf extension (https://github.com/datalad/datalad-osf). With this extension installed, this component can be git or datalad cloned from a 'osf://ID' URL, where 'ID' is the OSF node ID that shown in the OSF HTTP URL, e.g. https://osf.io/q8xnk can be cloned from osf://q8xnk. This particular project can be cloned using 'datalad clone osf://5hy42
Connectome Mapping ToolKit LIBrary (CMTKLIB): Data resources
No full textThis component was built from a DataLad dataset using the datalad-osf extension (https://github.com/datalad/datalad-osf). With this extension installed, this component can be git or datalad cloned from a 'osf://ID' URL, where 'ID' is the OSF node ID that shown in the OSF HTTP URL, e.g. https://osf.io/q8xnk can be cloned from osf://q8xnk. This particular project can be cloned using 'datalad clone osf://5hy42
NCCR-SYNAPSY/neurodatapub: NeuroDataPub v0.1
No full textBeta release of NeuroDataPub which provides a first working prototype.
Features
Provide a commandline interface (CLI) to create and publish neuroimaging datasets to GitHub NCCR-SYNAPSY, with files annexed in a host institution, accessible via ssh.
Adopt a traits/traitsui model that extends the CLI with a graphical user interface, aka the NeuroDataPub Assistant, to improve its accessibility by non IT experts.
Provide a Conda environment.yml to support the installation of Python with all dependencies.
Provide a setup.py to make installation of the neurodatapub package easy with pip install.
Adopt CircleCI for continuous integration testing. CircleCI project page:
https://app.circleci.com/pipelines/github/NCCR-SYNAPSY/neurodatapub
Use Codacy to support code reviews and monitor code quality over time. Codacy project page:
https://app.codacy.com/gh/NCCR-SYNAPSY/neurodatapub/dashboard
More...
For more change details and development discussions, please check:
PR #1: Main PR with the core API.
PR #7, PR #16, PR #17, PR #18, PR #19: PRs that adds the read-the-docs documentation source code and images.
PR #8: PR that adds the use of CircleCI for testing the installation and deploying the package to PyPI.
PR #9: PR that adopts Codacy, correct code style issues, and update the README.
PR #12: PR that refines the setup.py (project status set to BETA) before deployment to PyPI .
PR #13, PR #21: PRs that refines changes.rst. PR #21 updates tool intro in index.rst and README.md before deployment to PyPI.
PR #20: PR that makes all options not required when executing with --gui.
GitHub Repo
https://github.com/NCCR-SYNAPSY/neurodatapu
