Performance of computational models.

<p>(A) Predictive power for all connections and when restricted to intra/interhemispheric, direct/indirect connections. For each type of connections and each model, we represented the individual predictive powers (bar chart representing means and associated standard deviations), as well as the predictive power for the average subject computed using the original SC (diamonds), or after adding homotopic connections (circles). Of note, SC alone has no predictive power (zero) for the subset of indirect connections, by definition. (B) Patterns of SC, empirical FC and FC simulated from the SAR model for the average subject and associated scatter plot of simulated versus empirical FC (solid line represents perfect match). SARh stands for the SAR model with added homotopic connections. Matrices were rearranged such that network structure is highlighted. Homologous regions were arranged symmetrically with respect to the center of the matrix; for instance, the first and last regions are homologous. (C) Similarity of functional brain networks across subjects (bar chart with means and associated standard deviations), for the average subject (diamonds), and when adding homotopic connections (circles) (left). Network maps for the average subject and empirical FC, as well as for FC simulated using either the SAR model with original SC or the SARh.</p

Image_1_Integrated fMRI Preprocessing Framework Using Extended Kalman Filter for Estimation of Slice-Wise Motion.pdf

<p>Functional MRI acquisition is sensitive to subjects' motion that cannot be fully constrained. Therefore, signal corrections have to be applied a posteriori in order to mitigate the complex interactions between changing tissue localization and magnetic fields, gradients and readouts. To circumvent current preprocessing strategies limitations, we developed an integrated method that correct motion and spatial low-frequency intensity fluctuations at the level of each slice in order to better fit the acquisition processes. The registration of single or multiple simultaneously acquired slices is achieved online by an Iterated Extended Kalman Filter, favoring the robust estimation of continuous motion, while an intensity bias field is non-parametrically fitted. The proposed extraction of gray-matter BOLD activity from the acquisition space to an anatomical group template space, taking into account distortions, better preserves fine-scale patterns of activity. Importantly, the proposed unified framework generalizes to high-resolution multi-slice techniques. When tested on simulated and real data the latter shows a reduction of motion explained variance and signal variability when compared to the conventional preprocessing approach. These improvements provide more stable patterns of activity, facilitating investigation of cerebral information representation in healthy and/or clinical populations where motion is known to impact fine-scale data.</p

Manipulation of SC.

<p>(A) Predictive power of the SAR model with original SC (green), when adding homotopic connections (‘SARh’, red), or with shuffled homotopic connections (black). (B) Predictive power of the SAR model with original SC (red) and when SC values were randomly permuted, removed or added (from left to right). For each graph, predictive power was quantified as a function of the percentage of connections manipulated.</p

Stepwise regression performed between RSN_EEG and RSN_fMRI.

<p>The results of the stepwise regression performed between RSN_EEG (a) and RSN_fMRI (b) are shown in (c).</p

EEG resting state data

Data used in paper "Large-scale Functional Networks Indentified from Resting-state EEG Using Spatial ICA" data_EEG: raw data on 64 electrodes - 1 run per subject - the last 300 seconds were used in this paper - (BrainAmps system) xxx.eeg >>> EEG binary xxx.vhdr >>> EEG header xxx.vrmk >>> EEG markers polhemus.pol >>> polhemus file - electrodes localizatio

Hierarchical clustering of the individual spatial ICA components.

<p>The individual spatial ICA components (left) were projected to the MNI colin27 template (right). A hierarchical clustering was then performed to identify the RSN_EEG.</p

Two examples of RSN_EEG.

<p>Two RSN_EEG and associated power distribution in each frequency band are shown. (a) RSN_EEG including somato-motor areas and (b) RSN_EEG including fronto-parietal areas.</p

Association of EEG networks with fMRI networks.

<p>The RSN_EEG overlapping with only one RSN_fMRI are shown in the left part of the figure (1 to 1 association). The RSN_EEG overlapping with several RSN_fMRI are shown in the right part of the figure (1 to N association).</p