Henry Szu-Meng Chen1, Vinodh Kumar2, Sujit S Prabhu3, Kyle R Noll4, Sherise D Ferguson3, Ganesh Rao3, Ping Hou1, Jason M Johnson2, and Ho-Ling Anthony Liu1
1Imaging Physics, UT MD Anderson Cancer Center, Houston, TX, United States, 2Neuroradiology, UT MD Anderson Cancer Center, Houston, TX, United States, 3Neurosurgery, UT MD Anderson Cancer Center, Houston, TX, United States, 4Neuropsychology, UT MD Anderson Cancer Center, Houston, TX, United States
Synopsis
Post
glioma-resection functional connectomic measures were simulated by modifying
the presurgical resting-state fMRI data using the resection margin in 13
patients. The simulation uses atlas based approaches that removes the signal
contribution in the resected area based on the portion of parcel removed. The
simulated connectomic measures were found to approximate the actual
postsurgical connectomic measures. BN246 atlas appears to be more stable in
predicting the changes in connectomic measures than AAL90 atlas. The results show
that derivation of postsurgical functional connectomic measures from
presurgical data is feasible.
Introduction
Gliomas
represents approximately 80% of malignant brain tumors1. Treatment
of glioma typically involve neurosurgical intervention (e.g. resection).
Unfortunately the majority of patients experience postsurgical decline in
neurocognitive function (NCF). Brain lesions directly impact the cerebral
network organization and is associated with alterations in NCF. Studies in various
neurological diseases have linked changes in the brain’s functional connectomic measures to NCF decline2-7. Simulation of disruption to brain
network and the prediction of NCF may be especially useful in surgical
planning. However, methods of simulation has not been well established and few
studies have validated the simulation with post-lesioned brain8,9. We
hypothesize that, given the limited neuroplasticity shortly after surgery
(<4 weeks), functional connectivity measures in this period may be used to
optimize and validate the simulated lesioning on the presurgical functional
connectome using the resection margin. Methods
Resting-state
(rs)-fMRI, 3D GRE T1-weighted and 2D fluid attenuated inversion recovery scans
were acquired from 13 gliomas patients within 1 week before and 4 weeks after the
tumor resection. Resected areas were outlined on presurgical T1w images by an
experienced neuroradiologist by comparing the pre- and postsurgical structural MR
images. These ROIs were then spatially transformed to the presurgical rs-fMRI
images via registration between the T1w and EPI images. Pre- and postsurgical rs-fMRI
data were pre-processed using a standard pipeline consisting of timing and
motion correction, spatial normalization, regressing-out of covariates, and
bandpass filtering implemented using MATLAB (MathWorks, Natick, MA) and SPM12 (Wellcome
Department of Cognitive Neurology, Institute of Neurology, London, UK). AAL9010
and BN24611 atlas were used to extract the ROI time course to
generate the pre- and postsurgical connectivity matrices using MATLAB. To
simulate the postsurgical functional connectivity from the presurgical rs-fMRI
data, signal in the resected were removed from the rs-fMRI data using two
methods: 1) the signal from the resected area was set to zero, and 2) same as
1), but if the extent of the resection exceeds a percentage of the parcel, then
the signal from the entire parcel was set to zero as well. For method 2), a threshold of 0%,
25%, 50%, and 75% were investigated. Several network measures were calculated over a range of
connectivity density from 0.1 to 0.3 using the Brain Connectivity Toolbox12. To assess the performance of the simulations, paired t-test were used to identify if the difference between the simulated
postsurgical measures and the actual postsurgical measures were significantly smaller
than the difference between the actual pre- and postsurgical measures.Results
Table 1 and 2 show the postsurgical connectomic measures,
their changes from pre- to postsurgery and whether the changes obtained by the
simulations were significant for the two different atlases used. Using the AAL90
atlas, the best performing simulating was obtain using a parcel removal
threshold of 25%, with significant results across all the calculated measures.
Using the BN246 atlas, the best performing simulation was obtained using a
parcel threshold of 0%, but other thresholds (25-75%) also had good performance
in most connectomic measures. Figure 1 shows selected patient-average connectomic measures that illustrates the performances of different methods in simulating postsurgical
connectomic measures.Discussion
The results here show that derivation of postsurgical
functional connectivity from presurgical data is possible. Using a relatively
simple atlas based approach we were able to approximate several postsurgical
connectomic measures using only the presurgical data and resection margin as
inputs, as illustrated in Figure 2 for one of the patients. Simulated connectomic
measures obtained by simply removing the signal from the resected area resulted
in postsurgical changes that is significantly different from the actual
postsurgical data, likely because the remaining tissue is not a good functional
representation of the parcels. By removing the signal from an entire parcels
based on the percentage of the parcel resected, the simulation was significantly
improved. It was interesting that the best performing parcel threshold was 25%
using the AAL90 atlas and 0% using the BN246 atlas. This may be due to the
difference in parcel size. BN246 atlas has nearly 3x smaller parcel on average
than AAL90 atlas, and may therefore be more resilient to the node removal. The
measures derived using BN246 atlas also appear to be more stable in predicting
the changes in connectomic measures across the different thresholds.
Testing the simulated connectomic measures with their
association with the actual NCF changes is underway. Nevertheless, the results
here points to the feasibility of simulating postsurgical functional
connectivity using only the presurgical rs-fMRI data.Conclusion
The results here show that derivation of postsurgical
functional connectomic measures from presurgical data is feasible.Acknowledgements
No acknowledgement found.References
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