Fine-grain atlases of functional modes for fMRI analysis

Population imaging markedly increased the size of functional-imaging datasets, shedding new light on the neural basis of inter-individual differences. Analyzing these large data entails new scalability challenges, computational and statistical. For this reason, brain images are typically summarized...

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Bibliographic Details
Published in:NeuroImage (Orlando, Fla.) Vol. 221; no. 221; p. 117126
Main Authors: Dadi, Kamalaker, Varoquaux, Gaël, Machlouzarides-Shalit, Antonia, Gorgolewski, Krzysztof J., Wassermann, Demian, Thirion, Bertrand, Mensch, Arthur
Format: Journal Article
Language:English
Published: United States Elsevier Inc 01.11.2020
Elsevier Limited
Elsevier
Subjects:
Adult
Algorithms
Atlases as Topic
Brain - diagnostic imaging
Brain - physiology
Brain imaging atlases
Brain mapping
Brain Mapping - methods
Clustering
Compression
Computational neuroscience
Computer Science
Connectome - methods
Data analysis
Dictionaries
Functional magnetic resonance imaging
Functional networks
Functional parcellations
Humans
Machine Learning
Magnetic Resonance Imaging - methods
Medical Imaging
Multi-resolution
Nerve Net - diagnostic imaging
Nerve Net - physiology
Neuroimaging
Sparsity
Statistics
Systematic review
Functional parcellations
Multi-resolution
Brain imaging atlases
Functional networks
ISSN:1053-8119, 1095-9572, 1095-9572
Online Access:Get full text
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Summary:Population imaging markedly increased the size of functional-imaging datasets, shedding new light on the neural basis of inter-individual differences. Analyzing these large data entails new scalability challenges, computational and statistical. For this reason, brain images are typically summarized in a few signals, for instance reducing voxel-level measures with brain atlases or functional modes. A good choice of the corresponding brain networks is important, as most data analyses start from these reduced signals. We contribute finely-resolved atlases of functional modes, comprising from 64 to 1024 networks. These dictionaries of functional modes (DiFuMo) are trained on millions of fMRI functional brain volumes of total size 2.4 ​TB, spanned over 27 studies and many research groups. We demonstrate the benefits of extracting reduced signals on our fine-grain atlases for many classic functional data analysis pipelines: stimuli decoding from 12,334 brain responses, standard GLM analysis of fMRI across sessions and individuals, extraction of resting-state functional-connectomes biomarkers for 2500 individuals, data compression and meta-analysis over more than 15,000 statistical maps. In each of these analysis scenarii, we compare the performance of our functional atlases with that of other popular references, and to a simple voxel-level analysis. Results highlight the importance of using high-dimensional “soft” functional atlases, to represent and analyze brain activity while capturing its functional gradients. Analyses on high-dimensional modes achieve similar statistical performance as at the voxel level, but with much reduced computational cost and higher interpretability. In addition to making them available, we provide meaningful names for these modes, based on their anatomical location. It will facilitate reporting of results. •We contribute finely-resolved high-dimensional functional modes for fMRI analysis.•Those are trained on millions of varied fMRI functional brain volumes, using a sparse matrix factorisation algorithm. The total training size is 2.4TB.•These Dictionaries of Functional Modes (DiFuMo) are multi-scale, with a number of functional networks ranging from 64 to 1024.•Our benchmarks reveal the importance of using high-dimensional “soft” continuous-valued functional atlases when extracting image-derived phenotypes.•We provide an anatomical name to each of the modes of the DiFuMo atlases. Those are available at https://parietal-inria.github.io/DiFuMo/.
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ISSN:1053-8119
1095-9572
1095-9572
DOI:10.1016/j.neuroimage.2020.117126