Contrastive machine learning reveals species -shared and -specific brain functional architecture
Yang LiGuannan CaoSongyao ZhangWeihan ZhangYusong SunJingchao ZhouTianyang ZhongYixuan YuanTao LiuTianming LiuLei GuoYong‐Chun YuXi JiangGang LiJunwei HanTuo Zhang
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Human Connectome Project
This database contains the connectivity matrices of the resting-state functional MRI scans that were collected in two databases of the Human Connectome Project, Young Adult and Aging. These matrices contain the functional connectivity between brain regions (here, several different brain atlases were used, leading to several different connectivity matrices for each subject). The connectivity matrices are symmetrical n x n matrices. Here, n indicates the number of regions present in the atlas, and any number ni,j in the matrix is generated by calculating a simple Pearson correlation coefficient between the functional time series that describe the functional activation of regions i and j throughout the resting-state functional scan. The matrices presented in this database are present as .pconn.nii files (which can be handled using software like wb_command) or as .txt file. A full explanation of the database and the brain atlases used here, as well as all the scripts used to generate these connectivity matrices can be found on the GitHub page of this project: floristijhuis/HCP-rfMRI-repository (github.com).
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Functional near-infrared spectroscopy (fNIRS) is an emerging brain imaging technique. Recent fNIRS studies confirm that the intrinsic fluctuation can be robustly detected by functional near infrared spectroscopy (fNIRS), the phenomenon is also termed as resting state functional connectivity (RSFC). However, functional connectivity exists not only during the resting state. Therefore, one important question is that whether the functional connectivity patterns during both states are consistent. In this paper, we investigate the functional connectivity during both resting state and task state. The comparison result suggests that the functional connectivity patterns revealed by fNIRS signal during both states are similar.
Functional near-infrared spectroscopy
Functional Imaging
Dynamic functional connectivity
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Human Connectome Project
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Human Connectome Project
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A whole-cortex macaque structural connectome constructed from a combination of axonal tract-tracing and diffusion-weighted imaging data. Created for modeling brain dynamics using TheVirtualBrain platform. Website: thevirtualbrain.org
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Abstract The search for an ‘ideal’ approach to investigate the functional connections in the human brain is an ongoing challenge for the neuroscience community. While resting-state functional magnetic resonance imaging (fMRI) has been widely used to study individual functional connectivity patterns, recent work has highlighted the benefits of collecting functional connectivity data while participants are exposed to naturalistic stimuli, such as watching a movie or listening to a story. For example, functional connectivity data collected during movie-watching were shown to predict cognitive and emotional scores more accurately than resting-state-derived functional connectivity. We have previously reported a tight link between resting-state functional connectivity and task-derived neural activity, such that the former successfully predicts the latter. In the current work we use data from the Human Connectome Project to demonstrate that naturalistic-stimulus-derived functional connectivity predicts task-induced brain activation maps more accurately than resting-state-derived functional connectivity. We then show that activation maps predicted using naturalistic stimuli are better predictors of individual intelligence scores than activation maps predicted using resting-state. We additionally examine the influence of naturalistic-stimulus type on prediction accuracy. Our findings emphasize the potential of naturalistic stimuli as a promising alternative to resting-state fMRI for connectome-based predictive modelling of individual brain activity and cognitive traits.
Human Connectome Project
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This database contains the connectivity matrices of the resting-state functional MRI scans that were collected in two databases of the Human Connectome Project, Young Adult and Aging. These matrices contain the functional connectivity between brain regions (here, several different brain atlases were used, leading to several different connectivity matrices for each subject). The connectivity matrices are symmetrical n x n matrices. Here, n indicates the number of regions present in the atlas, and any number ni,j in the matrix is generated by calculating a simple Pearson correlation coefficient between the functional time series that describe the functional activation of regions i and j throughout the resting-state functional scan. The matrices presented in this database are present as .pconn.nii files (which can be handled using software like wb_command) or as .txt file. A full explanation of the database and the brain atlases used here, as well as all the scripts used to generate these connectivity matrices can be found on the GitHub page of this project: floristijhuis/HCP-rfMRI-repository (github.com).
Human Connectome Project
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