446 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
Sebastien Rale vs. New England: A Case Study of Frontier Conflict
No full textAuthor\u27s original abstract: A study was made of the Jesuit missionary, Sebastien Rale, and his role in New England-New France relations. French and English primary and secondary materials were examined to give the broadest possible view of the man and to place him in historical context.
It was found that Sebastien Rale was not an agent of New France. The conflicting opinions surrounding the mission of Norridgewock and the border war of the 1720\u27s were traced to the problems of Massachusetts-Abnaki relations. Rale\u27s frequent and testy letters to the government of the Bay Colony were blunt reactions to what he viewed as religious and territorial threats against his mission.
The frontier conflict between 1713 and 1722 was not the result of French Imperial policy. The French insisted that the Abnakis were allies but refused active participation in the Indians\u27 quarrel with New England. Policy was developed in Maine by the Jesuits. The missionaries were only secondarily interested in Quebec\u27s desire to prevent Massachusetts\u27 settlement of the Kennebec. With the declaration of war in July, 1722, however, the Jesuits left the Abnakis in the hands of the governor and the intendant of New France on whom the Indians relied for vital war supplies.
Finally, the controversial attack on Norridgewock was appraised. It was found that no secondary account had fully evaluated the sources. Examination led to the discovery of crucial inconsistencies in the primary accounts of New England. The French sources were found to be based on the understandably confused impressions of the fleeing Indians. In large measure the English sources present the more valid picture: the sudden attack, the panicked confusion, and Sebastien Rale dying with gun in hand. After Rale\u27s death the war drew to a close. Without Sebastien Rale\u27s persuasion and determination, the Abnakis were not able to present a united front against colonial expansion
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
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