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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Analysis of Functional Connectivity and Oscillatory Power Using DICS: From Raw MEG Data to Group-Level Statistics in Python
|
|---|---|
| Published in |
Frontiers in Neuroscience, September 2018
|
| DOI | 10.3389/fnins.2018.00586 |
| Pubmed ID | |
| Authors |
Marijn van Vliet, Mia Liljeström, Susanna Aro, Riitta Salmelin, Jan Kujala |
| Abstract |
Communication between brain regions is thought to be facilitated by the synchronization of oscillatory activity. Hence, large-scale functional networks within the brain may be estimated by measuring synchronicity between regions. Neurophysiological recordings, such as magnetoencephalography (MEG) and electroencephalography (EEG), provide a direct measure of oscillatory neural activity with millisecond temporal resolution. In this paper, we describe a full data analysis pipeline for functional connectivity analysis based on dynamic imaging of coherent sources (DICS) of MEG data. DICS is a beamforming technique in the frequency-domain that enables the study of the cortical sources of oscillatory activity and synchronization between brain regions. All the analysis steps, starting from the raw MEG data up to publication-ready group-level statistics and visualization, are discussed in depth, including methodological considerations, rules of thumb and tradeoffs. We start by computing cross-spectral density (CSD) matrices using a wavelet approach in several frequency bands (alpha, theta, beta, gamma). We then provide a way to create comparable source spaces across subjects and discuss the cortical mapping of spectral power. For connectivity analysis, we present a canonical computation of coherence that facilitates a stable estimation of all-to-all connectivity. Finally, we use group-level statistics to limit the network to cortical regions for which significant differences between experimental conditions are detected and produce vertex- and parcel-level visualizations of the different brain networks. Code examples using the MNE-Python package are provided at each step, guiding the reader through a complete analysis of the freely available openfMRI ds000117 "familiar vs. unfamiliar vs. scrambled faces" dataset. The goal is to educate both novice and experienced data analysts with the "tricks of the trade" necessary to successfully perform this type of analysis on their own data. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Finland | 3 | 18% |
| United Kingdom | 3 | 18% |
| Netherlands | 1 | 6% |
| Unknown | 10 | 59% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 8 | 47% |
| Members of the public | 8 | 47% |
| Practitioners (doctors, other healthcare professionals) | 1 | 6% |
Mendeley readers
The data shown below were compiled from readership statistics for 116 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 116 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 30 | 26% |
| Researcher | 29 | 25% |
| Student > Master | 16 | 14% |
| Student > Doctoral Student | 6 | 5% |
| Student > Bachelor | 6 | 5% |
| Other | 16 | 14% |
| Unknown | 13 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 43 | 37% |
| Psychology | 14 | 12% |
| Engineering | 13 | 11% |
| Computer Science | 6 | 5% |
| Medicine and Dentistry | 4 | 3% |
| Other | 13 | 11% |
| Unknown | 23 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 June 2023.
All research outputs
#3,379,268
of 26,493,550 outputs
Outputs from Frontiers in Neuroscience
#2,362
of 11,895 outputs
Outputs of similar age
#62,808
of 351,981 outputs
Outputs of similar age from Frontiers in Neuroscience
#72
of 253 outputs
Altmetric has tracked 26,493,550 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,895 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.2. This one has done well, scoring higher than 80% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 351,981 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 253 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.