Abstract #4160
A Novel Method for Robust Automated Thresholding in Pre-surgical fMRI using a Single Functional Run.
Tynan Stevens 1,2 , David Clarke 3,4 , Ryan D'Arcy 5,6 , Gerhard Stroink 1 , and Steven Beyea 2,7
1
Physics, Dalhousie University, Halifax, NS,
Canada,
2
Neuroimaging
Research Lab, BIOTIC, Halifax, NS, Canada,
3
Surgery,
Dalhousie University, Halifax, NS, Canada,
4
Neurosurgery,
QEII Health Sciences Centre, Halifax, NS, Canada,
5
Applied
Science, Simon Frasier University, Burnaby, BC, Canada,
6
Surrey
Memorial Hospital, Surrey, BC, Canada,
7
Radiology,
Dalhousie University, Halifax, NS, Canada
We demonstrate a novel data-driven method for selecting
thresholds for pre-surgical fMRI data, based on
reliability of the activation patterns in just a single
fMRI run. Our new method incorporates spatial
information not present in histogram based thresholding
methods, and alleviates the need for test-retest imaging
of existing reliability optimization methods. The new
method produces significantly higher test-retest overlap
when compared to established threshold optimization
methods, particularly for low CNR situations like
language mapping in patient populations. This analysis
therefore provides the most robust automated thresholds,
and unlike other techniques can be applied to any
existing fMRI paradigm without modification.
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