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Found 8 papers, 1 papers with code
Compression supports low-dimensional representations of behavior across neural circuits
Using a large sample of youth ($n=1, 040$), we test predictions in two ways: by measuring the dimensionality of spontaneous activity from sensorimotor to association cortex, and by assessing the representational capacity for 24 behaviors in neural circuits and 20 cognitive variables in recurrent neural networks.
A structurally informed model for modulating functional connectivity
For each individual, we measured the TMS-induced change in FC between the FPS and DMS (the FC network), and the structural coupling between the stimulated area and the FPS and DMS (the structural context network (SCN)).
Medical Image Harmonization Using Deep Learning Based Canonical Mapping: Toward Robust and Generalizable Learning in Imaging
Conventional and deep learning-based methods have shown great potential in the medical imaging domain, as means for deriving diagnostic, prognostic, and predictive biomarkers, and by contributing to precision medicine.
Efficient Coding in the Economics of Human Brain Connectomics
In doing so, we introduce the metric of compression efficiency, which quantifies the trade-off between lossy compression and transmission fidelity in structural networks.
Extraction of hierarchical functional connectivity components in human brain using resting-state fMRI
This paper provides a novel method for the extraction of hierarchical connectivity components in the human brain using resting-state fMRI.
Finding the needle in high-dimensional haystack: A tutorial on canonical correlation analysis
Since the beginning of the 21st century, the size, breadth, and granularity of data in biology and medicine has grown rapidly.
Influence of Neighborhood SES on Functional Brain Network Development
We quantitatively characterize this topology using a local measure of network segregation known as the clustering coefficient, and find that it accounts for a greater degree of SES-associated variance than meso-scale segregation captured by modularity.
Neurons and Cognition
Brain Age Prediction Based on Resting-State Functional Connectivity Patterns Using Convolutional Neural Networks
Whole brain voxel-wise FC measures could provide fine-grained FC information of the brain and may improve the prediction performance.
