sGraSP: A graph-based method for the derivation of subject-specific functional parcellations of the brain

BACKGROUND

Resting-state fMRI (rs-fMRI) has emerged as a prominent tool for the study of functional connectivity. The identification of the regions associated with the different brain functions has received significant interest. However, most of the studies conducted so far have focused on the definition of a common set of regions, valid for an entire population. The variation of the functional regions within a population has rarely been accounted for.

NEW METHOD

In this paper, we propose sGraSP, a graph-based approach for the derivation of subject-specific functional parcellations. Our method generates first a common parcellation for an entire population, which is then adapted to each subject individually.

RESULTS

Several cortical parcellations were generated for 859 children being part of the Philadelphia Neurodevelopmental Cohort. The stability of the parcellations generated by sGraSP was tested by mixing population and subject rs-fMRI signals, to generate subject-specific parcels increasingly closer to the population parcellation. We also checked if the parcels generated by our method were better capturing a development trend underlying our data than the original parcels, defined for the entire population.

COMPARISON WITH EXISTING METHODS

We compared sGraSP with a simpler and faster approach based on a Voronoi tessellation, by measuring their ability to produce functionally coherent parcels adapted to the subject data.

CONCLUSIONS

Our parcellations outperformed the Voronoi tessellations. The parcels generated by sGraSP vary consistently with respect to signal mixing, the results are highly reproducible and the neurodevelopmental trend is better captured with the subject-specific parcellation, under all the signal mixing conditions.

GraSP: geodesic Graph-based Segmentation with Shape Priors for the functional parcellation of the cortex

Resting-state functional MRI is a powerful technique for mapping the functional organization of the human brain. However, for many types of connectivity analysis, high-resolution voxelwise analyses are computationally infeasible and dimensionality reduction is typically used to limit the number of network nodes. Most commonly, network nodes are defined using standard anatomic atlases that do not align well with functional neuroanatomy or regions of interest covering a small portion of the cortex. Data-driven parcellation methods seek to overcome such limitations, but existing approaches are highly dependent on initialization procedures and produce spatially fragmented parcels or overly isotropic parcels that are unlikely to be biologically grounded. In this paper, we propose a novel graph-based parcellation method that relies on a discrete Markov Random Field framework. The spatial connectedness of the parcels is explicitly enforced by shape priors. The shape of the parcels is adapted to underlying data through the use of functional geodesic distances. Our method is initialization-free and rapidly segments the cortex in a single optimization. The performance of the method was assessed using a large developmental cohort of more than 850 subjects. Compared to two prevalent parcellation methods, our approach provides superior reproducibility for a similar data fit. Furthermore, compared to other methods, it avoids incoherent parcels. Finally, the method's utility is demonstrated through its ability to detect strong brain developmental effects that are only weakly observed using other methods.

A spatio-temporally compensated acousto-optic scanner for two-photon microscopy providing large field of view

Acousto-optic deflectors (AOD) are promising ultrafast scanners for non-linear microscopy. Their use has been limited until now by their small scanning range and by the spatial and temporal dispersions of the laser beam going through the deflectors. We show that the use of AOD of large aperture (13mm) compared to standard deflectors allows accessing much larger field of view while minimizing spatio-temporal distortions. An acousto-optic modulator (AOM) placed at distance of the AOD is used to compensate spatial and temporal dispersions. Fine tuning of the AOM-AOD setup using a frequency-resolved optical gating (GRENOUILLE) allows elimination of pulse front tilt whereas spatial chirp is minimized thanks to the large aperture AOD.