pNet Report
This is a report summarizing individual results in '{$subject_info$}' obtained using pNet.
pNet is an open-source toolbox (GitHub link) to obtain personalized functional networks from fMRI data.
pNet is developed by a team of (lab link).
Report is generated at {$report_time$}
Essential settings for this pNet workflow
The personalized functional network modeling is {$pnet_FN_method$}.
The number of FNs is set to {$K$}.
The fMRI data type is '{$dataType$}' with format as '{$dataFormat$}.'
The whole fMRI dataset contains {$nScan$} scans from {$nSubject$} subjects.
Parameters for the data input are stored in './Data_Input/Setting.json'
Parameters for the personalized FN modeling are stored in './FN_Computation/Setting.json'
Group Functional Networks (gFNs)
{$text_gFN$}
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Personalized Functional Networks (pFNs)
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Quality Control (QC)
The QC examines spatial correspondence and functional coherence of personalized FNs to ensure that the personalized FNs have higher functional homogeneity than their group-level counterparts and maintain good spatial correspondence with their group-level counterparts.
The delta spatial correspondence measures the minimum difference of spatial similarity between pFNs and their group counterparts.
The functional coherence of each FN is measured by a weighted mean of the correlation coefficients between the time courses of all the voxels within the FN and its centroid time course that was calculated as a weighted mean time course over the FN with its voxel-wise loadings as weights.
{$text_qc$}
Reference
[1] Cui, Z. (2020). Individual variation in functional topography of association networks in youth. Neuron, 106(2), 340-353.
[2] Cui, Z. (2022). Linking Individual Differences in Personalized Functional Network Topography to Psychopathology in Youth. Biological Psychiatry.
[3] Du, Yuhui, and Yong Fan. (2013). Group information guided ICA for fMRI data analysis.
[4] Du, Yuhui. (2023). IABC: a toolbox for intelligent analysis of brain connectivity.
[5] Li, H. and Fan, Y., 2016, April. Individualized brain parcellation with integrated funcitonal and morphological information. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) (pp. 992-995). IEEE.
[6] Li H. (2017) Large-scale sparse functional networks from resting state fMRI. Neuroimage 156:1-13.
[7] Shanmugan, S. (2022). Sex differences in the functional topography of association networks in youth. Proceedings of the National Academy of Sciences, 119(33), e2110416119.
[8] Zhou, Z. (2023). Multiscale functional connectivity patterns of the aging brain learned from harmonized rsfMRI data of the multi-cohort iSTAGING study. NeuroImage, 269, 119911.