Diffeomorphic functional brain surface alignment: Functional demons

Abstract

Aligning brain structures across individuals is a central prerequisite for comparative neuroimaging studies. Typically, registration approaches assume a strong association between the features used for alignment, such as macro-anatomy, and the variable observed, such as functional activation or connectivity. Here, we propose to use the structure of intrinsic resting state fMRI signal correlation patterns as a basis for alignment of the cortex in functional studies. Rather than assuming the spatial correspondence of functional structures between subjects, we have identified locations with similar connectivity profiles across subjects. We mapped functional connectivity relationships within the brain into an embedding space, and aligned the resulting maps of multiple subjects. We then performed a diffeomorphic alignment of the cortical surfaces, driven by the corresponding features in the joint embedding space. Results show that functional alignment based on resting state fMRI identifies functionally homologous regions across individuals with higher accuracy than alignment based on the spatial correspondence of anatomy. Further, functional alignment enables measurement of the strength of the anatomo-functional link across the cortex, and reveals the uneven distribution of this link. Stronger anatomo-functional dissociation was found in higher association areas compared to primary sensory- and motor areas. Functional alignment based on resting state features improves group analysis of task based functional MRI data, increasing statistical power and improving the delineation of task-specific core regions. Finally, a comparison of the anatomo-functional dissociation between cohorts is demonstrated with a group of left and right handed subjects.

Cross-species Functional Alignment Reveals Evolutionary Hierarchy Within the Connectome

“…Joint-embedding approach captures the common brain architecture across species. To construct a common functional space for cross-species comparison, we extended the spectral embedding-based approach for mapping connectivity topographies (termed as 'connectopies' in short) 30, [40][41][42] . Specifically, rather than computing spectral embeddings for each species individually and subsequently performing alignment, we applied embedding to a joint similarity matrix ( Fig 1A, details in Material and Method).…”

BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets

“…Our across-subjects evaluations highlighted an increase in correspondence between individual subjects 465 and the template manifold when Procrustes alignment was used compared to unaligned approaches, mainly driven by reasonably trivial changes in gradient, such as a change in the sign of specific gradients in a subgroup of subjects. As an alternative to the Procrustes alignment, it is also possible to align gradients by applying joint manifold alignments, often referred to as joint-embeddings (Xu et al, 2019).The rotations provided by embedding alignments can augment both cross-subject (Nenning et al, 2017) as well as cross-species analyses (Xu et al, 2019). Of note, this joint embedding technique generates a new manifold from the mapping between different gradients, which may result in new solutions that do not fully correspond to the initial gradients in important, and in a potentially 475 important way.…”

Functional density and edge maps: Characterizing functional architecture in individuals and improving cross-subject registration

“…Thus, features derived from ICA components may lose spatial information and not fully characterize individual functional organization. In a recent work (Nenning et al, 2017), spectral maps were derived, from functional connectivity of resting-state fMRI, which served as promising features for improving the functional alignment of task activations. In this work, the functional alignment was driven by the proposed functional density and edge maps.…”