Abstract #0279
Bias and instability in graph theoretical analyses of neuroimaging data
Mark Drakesmith 1 , Karen Caeyenberghs 2 , Anirban Dutt 3 , Glyn Lewis 4 , Anthony S David 3 , and Derek K Jones 1
1
CUBRIC, Cardiff University, Cardiff, Wales,
United Kingdom,
2
Department
of Physical therapy and motor rehabilitation, Ghent
University, Gent, Belgium,
3
Institute of
Psychiatry, Kings College London, London, United
Kingdom,
4
Academic
Unit of Psychiatry, University of Bristol, Bristol,
United Kingdom
Graph theory (GT), a powerful tool for quantifying
network properties from tractography, is subject to bias
and instability due to false positives (FPs). This study
illustrates this bias in GT metrics and examines the
effects of thresholding to reduce this bias.
Thresholding does reduce the effects of FPs but also
introduce their own biases. Statistical comparisons of
GT metrics are also shown to be highly unstable across
thresholds compared to non-GT metrics, although genuine
group differences tend to be more stable. A
multi-threshold permutation correction strategy is
suggested to improve sensitivity of statistical
comparisons of GT metrics to genuine group differences.
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