Spatial normalization for voxel-based lesion symptom mapping: impact of registration approaches

Daniel Jühling; Deepthi Rajashekar; Bastian Cheng; Claus Christian Hilgetag; Nils Daniel Forkert; Rene Werner

doi:10.3389/fnins.2024.1296357

Spatial normalization for voxel-based lesion symptom mapping: impact of registration approaches

Front Neurosci. 2024 Jan 17:18:1296357. doi: 10.3389/fnins.2024.1296357. eCollection 2024.

Authors

Daniel Jühling^{1

2}, Deepthi Rajashekar³, Bastian Cheng⁴, Claus Christian Hilgetag^{2

5}, Nils Daniel Forkert³, Rene Werner^{1

2

5}

Affiliations

¹ Institute of Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
² Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
³ Department of Radiology, University of Calgary, Calgary, AB, Canada.
⁴ Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
⁵ Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany.

Abstract

Background: Voxel-based lesion symptom mapping (VLSM) assesses the relation of lesion location at a voxel level with a specific clinical or functional outcome measure at a population level. Spatial normalization, that is, mapping the patient images into an atlas coordinate system, is an essential pre-processing step of VLSM. However, no consensus exists on the optimal registration approach to compute the transformation nor are downstream effects on VLSM statistics explored. In this work, we evaluate four registration approaches commonly used in VLSM pipelines: affine (AR), nonlinear (NLR), nonlinear with cost function masking (CFM), and enantiomorphic registration (ENR). The evaluation is based on a standard VLSM scenario: the analysis of statistical relations of brain voxels and regions in imaging data acquired early after stroke onset with follow-up modified Rankin Scale (mRS) values.

Materials and methods: Fluid-attenuated inversion recovery (FLAIR) MRI data from 122 acute ischemic stroke patients acquired between 2 and 3 days after stroke onset and corresponding lesion segmentations, and 30 days mRS values from a European multicenter stroke imaging study (I-KNOW) were available and used in this study. The relation of the voxel location with follow-up mRS was assessed by uni- as well as multi-variate statistical testing based on the lesion segmentations registered using the four different methods (AR, NLR, CFM, ENR; implementation based on the ANTs toolkit).

Results: The brain areas evaluated as important for follow-up mRS were largely consistent across the registration approaches. However, NLR, CFM, and ENR led to distortions in the patient images after the corresponding nonlinear transformations were applied. In addition, local structures (for instance the lateral ventricles) and adjacent brain areas remained insufficiently aligned with corresponding atlas structures even after nonlinear registration.

Conclusions: For VLSM study designs and imaging data similar to the present work, an additional benefit of nonlinear registration variants for spatial normalization seems questionable. Related distortions in the normalized images lead to uncertainties in the VLSM analyses and may offset the theoretical benefits of nonlinear registration.

Keywords: VLSM; image registration; neuroimaging; spatial normalization; stroke.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Deutsche Forschungsgemeinschaft (DFG) (SFB 936, projects A1 and Z3, and SPP 2041, project number 347572269, to CCH). We acknowledge financial support from the Open Access Publication Fund of UKE (Universitätsklinikum Hamburg-Eppendorf) and the DFG.