Inter-subject alignment of human cortical anatomy using functional connectivity

Conroy, Bryan; Singer, Benjamin D.; Guntupalli, J. Swaroop; Ramadge, Peter J.; Haxby, James V.

doi:10.1016/j.neuroimage.2013.05.009

Cited by 120 publications

(125 citation statements)

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“…6,[d] Many studies have also focused on improving spatial coregistration and thus functional localization or vice versa. [7][8][9] These structural and functional variations are clearly widespread and likely have a complex impact on the resulting functional patterns, motivating a multivariate whole-brain approach.…”

Section: [A]mentioning

confidence: 99%

Spatial Variance in Resting fMRI Networks of Schizophrenia Patients: An Independent Vector Analysis

Gopal

Miller

Andrew

et al. 2015

SCHBUL

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Spatial variability in resting functional MRI (fMRI) brain networks has not been well studied in schizophrenia, a disease known for both neurodevelopmental and widespread anatomic changes. Motivated by abundant evidence of neuroanatomical variability from previous studies of schizophrenia, we draw upon a relatively new approach called independent vector analysis (IVA) to assess this variability in resting fMRI networks. IVA is a blind-source separation algorithm, which segregates fMRI data into temporally coherent but spatially independent networks and has been shown to be especially good at capturing spatial variability among subjects in the extracted networks. We introduce several new ways to quantify differences in variability of IVA-derived networks between schizophrenia patients (SZs = 82) and healthy controls (HCs = 89). Voxelwise amplitude analyses showed significant group differences in the spatial maps of auditory cortex, the basal ganglia, the sensorimotor network, and visual cortex. Tests for differences (HC-SZ) in the spatial variability maps suggest, that at rest, SZs exhibit more activity within externally focused sensory and integrative network and less activity in the default mode network thought to be related to internal reflection. Additionally, tests for difference of variance between groups further emphasize that SZs exhibit greater network variability. These results, consistent with our prediction of increased spatial variability within SZs, enhance our understanding of the disease and suggest that it is not just the amplitude of connectivity that is different in schizophrenia, but also the consistency in spatial connectivity patterns across subjects.

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Section: [A]mentioning

confidence: 99%

Spatial Variance in Resting fMRI Networks of Schizophrenia Patients: An Independent Vector Analysis

Gopal

Miller

Andrew

et al. 2015

SCHBUL

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“…For the cerebral cortex, it is particularly important to optimize the alignment of functional regions rather than the underlying pattern of gyri and sulci. Fortunately, recently reported methods for function-based intersubject alignment hold great promise for improving the fidelity of human cortical alignment (Conroy et al, 2013;Robinson et al, in press;Smith et al, 2013).…”

Section: )mentioning

confidence: 99%

Interoperable atlases of the human brain

Amunts

Hawrylycz²,

Essen³

et al. 2014

NeuroImage

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The last two decades have seen an unprecedented development of human brain mapping approaches at various spatial and temporal scales. Together, these have provided a large fundus of information on many different aspects of the human brain including micro-and macrostructural segregation, regional specialization of function, connectivity, and temporal dynamics. Atlases are central in order to integrate such diverse information in a topographically meaningful way. It is noteworthy, that the brain mapping field has been developed along several major lines such as structure vs. function, postmortem vs. in vivo, individual features of the brain vs. population-based aspects, or slow vs. fast dynamics. In order to understand human brain organization, however, it seems inevitable that these different lines are integrated and combined into a multimodal human brain model. To this aim, we held a workshop to determine the constraints of a multi-modal human brain model that are needed to enable (i) an integration of different spatial and temporal scales and data modalities into a common reference system, and (ii) efficient data exchange and analysis. As detailed in this report, to arrive at fully interoperable atlases of the human brain will still require much work at the frontiers of data acquisition, analysis, and representation. Among them, the latter may provide the most challenging task, in particular when it comes to representing features of vastly different scales of space, time and abstraction. The potential benefits of such endeavor, however, clearly outweigh the problems, as only such kind of multi-modal human brain atlas may provide a starting point from which the complex relationships between structure, function, and connectivity may be explored.

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“…The FreeSurfer (fsaverage) template is widely adopted, used by [28] within a diffeomorphic alignment framework, and in [26] it is adapted to force hemispheric symmetry. Further, [5,22] adapt the FreeSurfer framework to align fMRI timeseries and functional connectivity features respectively. In each case the registration target is the population average.…”

Section: Introductionmentioning

confidence: 99%

Discrete Optimisation for Group-Wise Cortical Surface Atlasing

Glocker

Rajchl

Rueckert

2016

2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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This paper presents a novel method for cortical surface atlasing. Group-wise registration is performed through a discrete optimisation framework that seeks to simultaneously improve pairwise correspondences between surface feature sets, whilst minimising a global cost relating to the rank of the feature matrix. It is assumed that when fully aligned, features will be highly linearly correlated, and thus have low rank. The framework is regularised through use of multi-resolution control point grids and higher-order smoothness terms, calculated by considering deformation strain for displacements of triplets of points. Accordingly the discrete framework is solved through high-order clique reduction. The framework is tested on cortical folding based alignment, using data from the Human Connectome Project. Preliminary results indicate that group-wise alignment improves folding correspondences, relative to registration between all pair-wise combinations, and registration to a global average template.

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Inter-subject alignment of human cortical anatomy using functional connectivity

Cited by 120 publications

References 50 publications

Spatial Variance in Resting fMRI Networks of Schizophrenia Patients: An Independent Vector Analysis

Spatial Variance in Resting fMRI Networks of Schizophrenia Patients: An Independent Vector Analysis

Interoperable atlases of the human brain

Discrete Optimisation for Group-Wise Cortical Surface Atlasing

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