Dataset Information

Thresholding lesion overlap difference maps: application to category-related naming and recognition deficits.

ABSTRACT: Lesion overlap difference maps have been used in studies designed to test anatomical hypotheses regarding brain systems critical for various cognitive and behavioral tasks, including naming and recognition of concrete entities [Damasio, H., Tranel, D., Grabowski, T., Adolphs, R., Damasio, A., 2004. Neural systems behind word and concept retrieval. Cognition 92, 179-229]. To date, the interpretation of these results has focused on areas of maximum lesion overlap differences. Here we explore formal methods for statistical thresholding and power analysis. We derive exact voxel-wise statistics describing the behavior of lesion overlap difference maps and lesion proportion difference maps under the null hypothesis of no association between lesion and deficit, and we apply the statistics to a large subset of the subjects previously reported in [Damasio, H., Tranel, D., Grabowski, T., Adolphs, R., Damasio, A., 2004. Neural systems behind word and concept retrieval. Cognition 92, 179-229], in order to reassess the lesion correlates of deficits in naming and recognition for five categories of concrete entities. The thresholded maps confirmed many of the results reported previously, but also revealed some differences. Differences in spatial distribution of the lesion correlates of impaired naming of unique versus nonunique entities were confirmed in the inferotemporal region (IT), although overlapping components across categories became apparent in left IT. Additionally, the left inferior frontal gyrus (IFG) was implicated in naming both categories of nonunique natural entities (animals and fruits/vegetables). In corresponding power analyses, we estimated where significant effects could be found under an assumption of maximal effect size given the observed spatial distribution of lesions. Such "effective coverage maps" are valuable for the interpretation of the results, notably because of heterogeneity in lesion coverage encountered in lesion studies. We strongly suggest that when inferential statistics are used in voxel-wise lesion-deficit statistical mapping, these or other power maps be included in the reports.

SUBMITTER: Rudrauf D

PROVIDER: S-EPMC2582202 | biostudies-literature | 2008 Jul

REPOSITORIES: biostudies-literature

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Thresholding lesion overlap difference maps: application to category-related naming and recognition deficits.

Rudrauf David D Mehta Sonya S Bruss Joel J Tranel Daniel D Damasio Hanna H Grabowski Thomas J TJ

NeuroImage 20071227 3

Lesion overlap difference maps have been used in studies designed to test anatomical hypotheses regarding brain systems critical for various cognitive and behavioral tasks, including naming and recognition of concrete entities [Damasio, H., Tranel, D., Grabowski, T., Adolphs, R., Damasio, A., 2004. Neural systems behind word and concept retrieval. Cognition 92, 179-229]. To date, the interpretation of these results has focused on areas of maximum lesion overlap differences. Here we explore forma ...[more]

PMID: 18442925

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Project description:White matter hyperintensities (WMH) or white matter lesions exhibit high variability in their characteristics both at population- and subject-level, making their detection a challenging task. Population-level factors such as age, vascular risk factors and neurodegenerative diseases affect lesion load and spatial distribution. At the individual level, WMH vary in contrast, amount and distribution in different white matter regions. In this work, we aimed to improve BIANCA, the FSL tool for WMH segmentation, in order to better deal with these sources of variability. We worked on two stages of BIANCA by improving the lesion probability map estimation (classification stage) and making the lesion probability map thresholding stage automated and adaptive to local lesion probabilities. Firstly, in order to take into account the effect of population-level factors, we included population-level lesion probabilities, modelled with respect to a parametric factor (e.g. age), in the classification stage. Secondly, we tested BIANCA performance when using four alternative classifiers commonly used in the literature with respect to K-nearest neighbour algorithm (currently used for lesion probability map estimation in BIANCA). Finally, we propose LOCally Adaptive Threshold Estimation (LOCATE), a supervised method for determining optimal local thresholds to apply to the estimated lesion probability map, as an alternative option to global thresholding (i.e. applying the same threshold to the entire lesion probability map). For these experiments we used data from a neurodegenerative cohort, a vascular cohort and the cohorts available publicly as a part of a segmentation challenge. We observed that including population-level parametric lesion probabilities with respect to age and using alternative machine learning techniques provided negligible improvement. However, LOCATE provided a substantial improvement in the lesion segmentation performance, when compared to the global thresholding. It allowed to detect more deep lesions and provided better segmentation of periventricular lesion boundaries, despite the differences in the lesion spatial distribution and load across datasets. We further validated LOCATE on a cohort of CADASIL (Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy) patients, a genetic form of cerebral small vessel disease, and healthy controls, showing that LOCATE adapts well to wide variations in lesion load and spatial distribution.

Dataset Information

Thresholding lesion overlap difference maps: application to category-related naming and recognition deficits.

Publications

Thresholding lesion overlap difference maps: application to category-related naming and recognition deficits.

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