Unraveling reproducible dynamic states of individual brain functional parcellation

Abstract

Data-driven parcellations are widely used for exploring the functional organization of the brain, and also for reducing the high dimensionality of fMRI data. Despite the flurry of methods proposed in the literature, functional brain parcellations are not highly reproducible at the level of individual subjects, even with very long acquisitions. Some brain areas are also more difficult to parcellate than others, with association heteromodal cortices being the most challenging. An important limitation of classical parcellations is that they are static, i.e. they neglect dynamic reconfigurations of brain networks. In this paper, we proposed a new method to identify dynamic states of parcellations, which we hypothesized would improve reproducibility over static parcellation approaches. For a series of seed voxels in the brain, we applied a cluster analysis to regroup short (3 minutes) time windows into "states" with highly similar seed parcel. We splitted individual time series of the Midnight Scan Club sample into two independent sets of 2.5 hours (test and retest). We found that average within-state parcels, called stability maps, were highly reproducible (over .9 test-retest spatial correlation in many instances) and subject specific (fingerprinting accuracy over 70% on average) between test and retest. Consistent with our hypothesis, seeds in heteromodal cortices (posterior and anterior cingulate) showed a richer repertoire of states than unimodal (visual) cortex. Taken together, our results indicate that static functional parcellations are incorrectly averaging well defined and distinct dynamic states of brain parcellations. This work calls to revisit previous methods based on static parcellations, which includes the majority of published network analyses of fMRI data. Our method may thus impact how researchers model the rich interactions between brain networks in health and disease.

Inside Information: Systematic within-node connectivity changes observed across tasks or groups

“…Studies have shown that there can be differences in the spatial configurations of nodes across subjects 44 and individualized parcellation methods have been developed to attempt to account for these differences 45, 46 . Methods have also been developed to compensate for both spatial differences between subjects as well as task-induced brain states 33, 34 . Individualized parcellations have been shown to have higher functional homogeneity than fixed atlases 33, 45, 46 .…”

“…While it has long been known that there is sub-specialization in cortical regions, this raises two fundamental questions with respect to connectivity analyses: 1) Is there an ideal atlas for defining functional nodes; 2) Does it make sense to combine structural data (which is fixed at the functional time scale) with functional data which is dynamic and flexible in its organization. Node reorganization over short temporal windows has been demonstrated in non-overlapping whole-brain parcellations 34 . There is considerable support for this flexible organization in the literature from studies using a variety of methods 35, 36, 37, 38, 39 .…”

Unraveling reproducible dynamic states of individual brain functional parcellation

“…The generation of state stability maps can be executed online via a Jupyter notebook via the binder platform. We have also made available online all the state stability maps for the 10 subjects of the MSC dataset on the NeuroVault website ( https://identifiers.org/neurovault.collection:6642 ; Boukhdhir, 2020b ).…”

A novel 5D brain parcellation approach based on spatio-temporal encoding of resting fMRI data from deep residual learning

“…This approach has shown acceptable results based on standard metrics [23]. Similarly, Boukhdhir et al presented a novel scheme to detect dynamic states of segments; In their method, a clustering analysis has been executed to regroup short time windows into states with similar seed patches [26].…”