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 2.4TB, 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 2,500 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 analyse 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.

我们提出了精细分辨率的功能模式图册 (DiFuMo)，由 64 到 1024 个网络组成，很好地提取了降维信号，以及对大量数据处理技术进行了比较和解释。