Spatial normalization of lesioned brains: Performance evaluation and impact on fMRI analyses

By Jennifer T. Crinion, John Ashburner, Alexander P. Leff, Matthew Brett, Cathy J. Price, Karl J. Friston. NeuroImage, 2007

316 citations recorded by the source corpus.

A key component of group analyses of neuroimaging data is precise and valid spatial normalization (i.e., inter-subject image registration). When patients have structural brain lesions, such as a stroke, this process can be confounded by the lack of correspondence between the subject and standardized template images. Current procedures for dealing with this problem include regularizing the estimate of warping parameters used to match lesioned brains to the template, or “cost function masking”; both these solutions have significant drawbacks. We report three experiments that identify the best spatial normalization for structurally damaged brains and establish whether differences among normalizations have a significant effect on inferences about functional activations. Our novel protocols evaluate the effects of different normalization solutions and can be applied easily to any neuroimaging study. This has important implications for users of both structural and functional imaging techniques in the study of patients with structural brain damage.

Subfields: Cognitive Neuroscience, Neurology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/992a17de2974db0bf1ccc3e704e70c43f932e387

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In Cognitive Neuroscience

In Neurology

Stewart H. Mostofsky

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

The left superior temporal gyrus is a shared substrate for auditory short-term memory and speech comprehension: evidence from 210 patients with stroke (2009, unknown): “This algorithm was developed to deal with normal subjects’ brains but with patients it outperforms the previous ‘gold-standard’ of cost-function masking (Crinion et al., 2007).”
Age-specific CT and MRI templates for spatial normalization (2012, unknown): “…we found substantial variability in the occipital horn (which is bulbous in some individuals and very elongated in others, a feature that is exacerbated following stroke) and found it difficult to consistently identify the cortical occipital landmark ‘OC’ described by Crinion et al. (2007).”
The impact of T1 vs EPI spatial normalization templates for fMRI data analyses (2017, background): “While several studies have compared nonlinear warping procedures commonly used within the T1norm framework [Ardekani et al., 2004, 2005; Crinion et al., 2007; Ghosh et al., 2010; Tahmasebi et al., 2009], to our knowledge, no study has systematically compared the EPInorm and T1norm approaches.”
An a contrario approach for the detection of patient-specific brain perfusion abnormalities with arterial spin labelling (2016, unknown): “This registration algorithm was selected since it gives good results even in the presence of large anatomical lesions (Crinion et al., 2007).”