Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion
The alignment of brain imaging data for functional neuroimaging studies is challenging due to the discrepancy between correspondence of morphology, and equivalence of functional role. In this paper we map functional activation areas across individuals by
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Department of Biomedical Imaging and Image-guided Therapy, CIR Lab, Medical University of Vienna, Vienna, Austria 2 CSAIL, MIT 3 McGovern Institute for Brain Research, MIT, Cambridge, MA, USA 4 Department of Otology and Laryngology, Harvard Medical School, Boston, MA, USA Abstract. The alignment of brain imaging data for functional neuroimaging studies is challenging due to the discrepancy between correspondence of morphology, and equivalence of functional role. In this paper we map functional activation areas across individuals by a multiatlas label fusion algorithm in a functional space. We learn the manifold of resting-state fMRI signals in each individual, and perform manifold alignment in an embedding space. We then transfer activation predictions from a source population to a target subject via multi-atlas label fusion. The cost function is derived from the aligned manifolds, so that the resulting correspondences are derived based on the similarity of intrinsic connectivity architecture. Experiments show that the resulting label fusion predicts activation evoked by various experiment conditions with higher accuracy than relying on morphological alignment. Interestingly, the distribution of this gain is distributed heterogeneously across the cortex, and across tasks. This offers insights into the relationship between intrinsic connectivity, morphology and task activation. Practically, the mechanism can serve as prior, and provides an avenue to infer taskrelated activation in individuals for whom only resting data is available.
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Introduction
Establishing functional correspondence across the brains of individuals is a central prerequisite for neuroimaging group studies. Standard approaches rely on brain morphology to perform group-wise registration, and their improvement has brought a substantial boost to the specificity of neuroimaging results and their interpretation in light of neuroscientific questions. Recent results indicate that the variability of the functional architecture across individuals makes the concept of correspondence more challenging to grasp. Specifically, the link between anatomical location and functional role can be weak. This results in the decrease of specificity in group studies, and potential bias. In this paper we propose multi-atlas label fusion based on functional alignment. The method establishes correspondence of cortical positions based on resting-state functional magnetic resonance imaging (rs-fMRI) signals. Using this functional alignment with label-fusion of activations c Springer International Publishing Switzerland 2015 N. Navab et al. (Eds.): MICCAI 2015, Part II, LNCS 9350, pp. 313–320, 2015. DOI: 10.1007/978-3-319-24571-3_38
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G. Langs, P. Golland, and S.S. Ghosh
observed during task fMRI (t-fMRI) in a population of source subjects, we predict task activations in a target, aligned subject. Transferring information using functional connectivity alignment results in higher accuracy of transferring task activation compared to morphological alignment. This method exten
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