Fine-grain atlases of functional modes for fMRI analysis

@article{Dadi2020FinegrainAO,
  title={Fine-grain atlases of functional modes for fMRI analysis},
  author={Kamalaker Dadi and Ga{\"e}l Varoquaux and Antonia Machlouzarides-Shalit and Krzysztof J. Gorgolewski and Demian Wassermann and Bertrand Thirion and Arthur Mensch},
  journal={NeuroImage},
  year={2020},
  volume={221}
}

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References

SHOWING 1-10 OF 165 REFERENCES

A whole brain fMRI atlas generated via spatially constrained spectral clustering

TLDR
A data‐driven method for generating an ROI atlas by parcellating whole brain resting‐state fMRI data into spatially coherent regions of homogeneous FC is introduced and several clustering statistics are used to compare methodological trade‐offs as well as determine an adequate number of clusters.

Task-driven ICA feature generation for accurate and interpretable prediction using fMRI

Region segmentation for sparse decompositions: better brain parcellations from rest fMRI

TLDR
This work presents post-processing techniques that automatically sparsify brain maps and separate regions properly using geometric operations, and compares these techniques according to faithfulness to data and stability metrics.

Individual Brain Charting, a high-resolution fMRI dataset for cognitive mapping

TLDR
The present article gives a detailed description of the first release of the IBC dataset, a high-resolution multi-task fMRI dataset that intends to provide the objective basis toward a comprehensive functional atlas of the human brain.

A supervised clustering approach for fMRI-based inference of brain states

Which fMRI clustering gives good brain parcellations?

TLDR
It is shown that in general Ward’s clustering performs better than alternative methods with regard to reproducibility and accuracy and that the two criteria diverge regarding the preferred models (reproducibility leading to more conservative solutions), thus deferring the practical decision to a higher level alternative, namely the choice of a trade-off between accuracy and stability.

Consistent resting-state networks across healthy subjects

TLDR
Findings show that the baseline activity of the brain is consistent across subjects exhibiting significant temporal dynamics, with percentage BOLD signal change comparable with the signal changes found in task-related experiments.

Spatial parcellations, spectral filtering, and connectivity measures in fMRI: Optimizing for discrimination

TLDR
It is found that discriminative FC properties are frequency specific; higher frequencies performed surprisingly well under certain configurations of atlas choices and dependency measures, with ICA‐based parcellations revealing greater discriminability at high frequencies compared to other parcellation.
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