Functional System and Areal Organization of a Highly Sampled Individual Human Brain

Abstract

Summary Resting state functional MRI has enabled description of group-level functional brain organization at multiple spatial scales. However, cross-subject averaging may obscure patterns of brain organization specific to each individual. Here, we characterized the brain organization of a single individual repeatedly measured over more than a year. We report a reproducible and internally valid subject-specific areal-level parcellation that corresponds with subject-specific task activations. Highly convergent correlation network estimates can be derived from this parcellation if sufficient data are collected – considerably more than typically acquired. Notably, within-subject correlation variability across sessions exhibited a heterogeneous distribution across the cortex concentrated in visual and somato-motor regions, distinct from the pattern of inter-subject variability. Further, although the individual's systems-level organization is broadly similar to the group, it demonstrates distinct topological features. These results provide a foundation for studies of individual differences in cortical organization and function, especially for special or rare individuals.

Progress and challenges in probing the human brain

“…For example, Smith et al 46 used independent component analysis to identify spatially independent sets of voxels from resting-state fMRI data and from task-based data (obtained from the Brainmap meta-analytic database), and demonstrated that the components extracted from resting-state fMRI showed a high degree of concordance with those extracted from task-based data. The overlap between resting-state and task-based functional organization can also be seen within individuals; for example, the longitudinal examination of a single individual revealed reliable spatial parcellation of activity in the cerebral cortex (using resting fMRI data) that mapped systematically to the activation patterns observed across a large number of task measurements 47 . Despite the substantial excitement around resting-state fMRI findings, numerous concerns have been raised about their interpretation.…”

Striatal subdivisions that coherently interact with multiple cerebrocortical networks

“…Parcellation analyses based on boundary mapping (Cohen et al, 2008; Glasser et al, 2016; Gordon et al, 2016; Gordon, Laumann, Gilmore, et al, 2017; Hirose et al, 2012, 2013, 2016; Laumann et al, 2015; Osada et al, 2017) were applied to the striatum (Figure 1). Each voxel in the striatum of each participant was used as a seed to calculate its correlations with the voxels in the gray matter of the cerebral cortex.…”

“…The cerebrocortical parcels were calculated in a similar manner, as described previously (Cohen et al, 2008; Gordon et al, 2016; Laumann et al, 2015; Supporting Information Figure S1). Each vertex in the fiducial surface in the cerebral cortex of each participant was used as a seed to calculate its correlations with all of the vertices.…”

“…The similarity of the spatial patterns of the correlation maps was then evaluated using correlation coefficients, and similarity maps were generated. After spatial smoothing (FWHM = 6.0 mm) (Gordon et al, 2016; Laumann et al, 2015), spatial gradients of the similarity maps were computed for each seed vertex. A two‐dimensional watershed algorithm was applied to the gradient maps, and the binary watershed maps were averaged across the seed vertices after spatial smoothing (FWHM = 6.0 mm) to generate a boundary probability map.…”

Inferring Individual-Level Variations in the Functional Parcellation of the Cerebral Cortex

“…The most straightforward has been to acquire more fMRI data. To generate an individual functional parcellation, Laumann et al accumulated 14 hours of resting-state fMRI data from one individual over more than a year [36]. A second approach is to introduce additional prior knowledge on the functional parcellation.…”