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A combined bootstrap/histogram analysis approach for computing a lateralization index from neuroimaging data.

Abstract：

:Cerebral hemispheric specialization has traditionally been described using a lateralization index (LI). Such an index, however, shows a very severe threshold dependency and is prone to be influenced by statistical outliers. Reliability of this index thus has been inherently weak, and the assessment of this reliability is as yet not possible as methods to detect such outliers are not available. Here, we propose a new approach to calculating a lateralization index on functional magnetic resonance imaging data by combining a bootstrap procedure with a histogram analysis approach. Synthetic and real functional magnetic resonance imaging data was used to assess performance of our approach. Using a bootstrap algorithm, 10,000 indices are iteratively calculated at different thresholds, yielding a robust mean, maximum and minimum LI and thus allowing to attach a confidence interval to a given index. Taking thresholds into account, an overall weighted bootstrapped lateralization index is calculated. Additional histogram analyses of these bootstrapped values allow to judge reliability and the influence of outliers within the data. We conclude that the proposed methods yield a robust and specific lateralization index, sensitively detect outliers and allow to assess the underlying data quality.

journal_name

Neuroimage

journal_title

NeuroImage

authors

Wilke M,Schmithorst VJ

doi

10.1016/j.neuroimage.2006.07.010

subject

Has Abstract

pub_date

2006-11-01 00:00:00

pages

522-30

issue

2

eissn

1053-8119

issn

1095-9572

pii

S1053-8119(06)00742-7

journal_volume

33

pub_type

杂志文章

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