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Indirect structural disconnection-symptom mapping

期刊

BRAIN STRUCTURE & FUNCTION
卷 227, 期 9, 页码 3129-3144

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s00429-022-02559-x

关键词

Lesion-symptom mapping; Inference; Connectome; Diffusion tensor imaging; White matter; Statistics

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In vivo tracking of white matter fibres has shed light on the crucial role of brain connectome disruption in neuropsychological deficits. However, using diffusion-weighted magnetic resonance imaging to examine white matter integrity in neurological patients has conceptual limitations and is not widely applicable. Indirect estimation of structural disconnection serves as an elegant and economical alternative.
In vivo tracking of white matter fibres catalysed a modern perspective on the pivotal role of brain connectome disruption in neuropsychological deficits. However, the examination of white matter integrity in neurological patients by diffusion-weighted magnetic resonance imaging bears conceptual limitations and is not widely applicable, as it requires imaging-compatible patients and resources beyond the capabilities of many researchers. The indirect estimation of structural disconnection offers an elegant and economical alternative. For this approach, a patient's structural lesion information and normative connectome data are combined to estimate different measures of lesion-induced structural disconnection. Using one of several toolboxes, this method is relatively easy to implement and is even available to scientists without expertise in fibre tracking analyses. Nevertheless, the anatomo-behavioural statistical mapping of structural brain disconnection requires analysis steps that are not covered by these toolboxes. In this paper, we first review the current state of indirect lesion disconnection estimation, the different existing measures, and the available software. Second, we aim to fill the remaining methodological gap in statistical disconnection-symptom mapping by providing an overview and guide to disconnection data and the statistical mapping of their relationship to behavioural measurements using either univariate or multivariate statistical modelling. To assist in the practical implementation of statistical analyses, we have included software tutorials and analysis scripts.

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