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Making robust inferences about the functional neuroanatomy of the brain is critically dependent on experimental techniques that examine the consequences of focal loss of brain function. Unfortunately, the use of the most comprehensive such technique-lesion-function mapping-is complicated by the need for time-consuming and subjective manual delineation of the lesions, greatly limiting the practicability of the approach. Here we exploit a recently-described general measure of statistical anomaly, zeta, to devise a fully-automated, high-dimensional algorithm for identifying the parameters of lesions within a brain image given a reference set of normal brain images. We proceed to evaluate such an algorithm in the context of diffusion-weighted imaging of the commonest type of lesion used in neuroanatomical research: ischaemic damage. Summary performance metrics exceed those previously published for diffusion-weighted imaging and approach the current gold standard-manual segmentation-sufficiently closely for fully-automated lesion-mapping studies to become a possibility. We apply the new method to 435 unselected images of patients with ischaemic stroke to derive a probabilistic map of the pattern of damage in lesions involving the occipital lobe, demonstrating the variation of anatomical resolvability of occipital areas so as to guide future lesion-function studies of the region.

Original publication

DOI

10.1016/j.cortex.2012.12.008

Type

Journal article

Journal

Cortex

Publication Date

07/2014

Volume

56

Pages

51 - 63

Keywords

Diffusion-weighted imaging, Lesion segmentation, Lesion-mapping, Magnetic resonance imaging, Occipital lobe, Stroke, Zeta score, Aged, Aged, 80 and over, Algorithms, Brain Ischemia, Diffusion Magnetic Resonance Imaging, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neuroimaging, Occipital Lobe, Sensitivity and Specificity, Stroke