[BIC-announce] MCIN lecture this Friday - Evaluating the Stability of Neuroimaging Pipelines - Greg Kiar
Patrick Bermudez
patrick.bermudez at gmail.com
Tue Jan 28 17:34:14 EST 2020
Please join us this Friday (31st of January) at 13:00 in the de
Grandpré auditorium for a presentation by Greg Kiar entitled
"Evaluating the Stability of Neuroimaging Pipelines". Here is an
abstract of the talk:
-------------
A lack of software reproducibility has become increasingly apparent in
the last several years, calling into question the validity of
scientific findings affected by published tools. Reproducibility
issues may have numerous sources of error, including the underlying
numerical stability of algorithms and implementations employed.
Various forms of instability have been observed in neuroimaging,
including across operating system versions, minor noise injections,
and implementation of theoretically equivalent algorithms.
We will explore the effect of various perturbation methods on a
typical neuroimaging pipeline through the use of i) near-epsilon noise
injections, ii) Monte Carlo Arithmetic, and iii) varying operating
systems to identify the quality and severity of their impact. The work
demonstrates that even low-order computational models, such as the
connectome estimation pipeline that we used, are susceptible to noise.
This suggests that stability is a relevant axis upon which tools
should be compared, developed, or improved, alongside more commonly
considered axes such as accuracy/biological feasibility or
performance. The heterogeneity observed across participants clearly
illustrates that stability is a property of not just the data or tools
independently, but their interaction. Characterization of stability
should therefore be evaluated for specific analyses and performed on a
representative set of subjects for consideration in subsequent
statistical testing. Additionally, identifying how this relationship
scales to higher-order models is an exciting next step which will be
explored. Finally, the joint application of perturbation methods with
post-processing approaches such as bagging or signal normalization may
lead to the development of more numerically stable analyses while
maintaining sensitivity to meaningful variation.
-----------
More information about the BIC-announce
mailing list