Wilburn E Reddick1, John O Glass1, Elizabeth C Duncan1, Jung Won Hyun2, Qing Ji1, Yimei Li2, and Amar Gajjar3
1Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, TN, United States, 2Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, United States, 3Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States
Synopsis
Diffusion tensor imaging from 30 childhood medulloblastoma
patients were analyzed to assess changes in structural connectivity of the
central executive network (CEN) in response to cranial irradiation. Significant
drops in fractional anisotropy and axial diffusivity (AX) were demonstrated in
most of the CEN subnetworks after irradiation. Furthermore, patients receiving
the highest CRT dose had significantly decreased AX in all subnetworks of the
CEN. These findings suggest significant reduction in the microstructural
integrity within the CEN immediately after CRT in this population and support
the use of the CEN model for evaluating changes in cerebral white matter early
in therapy.Purpose
Approximately 55% of all brain tumors in children are located in
the posterior fossa with medulloblastoma being the most common form.
1
The current standard of care for pediatric medulloblastoma includes maximal
surgical excision, risk-adapted cranial radiation therapy (CRT), and adjuvant
chemotherapy which has achieved an overall survival of 75%.
2
Unfortunately, effective therapy is associated with neurocognitive deficits in
the frontal lobe–mediated cognitive domains, such as working memory,
3,4
intelligence, academic performance,
5,6 processing speed, and
attention
4 in survivors. In an effort to more fully understand the association
between CRT and neurocognitive deficits, this study assesses changes in structural
connectivity of the central executive network (CEN)
7-9 in response
to CRT.
Methods
MR scans of 30 medulloblastoma patients (Age at exam 11.2+5.2
years; 8 average-risk [23.4 Gy CRT], 22 high-risk [36.0 Gy CRT]) were used for
this study. For each patient, two MR scans were performed, one Pre-CRT and one Post-CRT.
Each MR scan consists of anatomic 3D T1 weighted imaging and diffusion tensor
imaging (DTI, 30 directions, 2 averages, b=700). For each scan, the anatomic
imaging set was processed using Freesurfer (surfer.nmr.mgh.harvard.edu/)
10 to obtain the 20 brain structures (4
sub-cortical and 6 cortical for each hemisphere) included in the CEN. DTI processing
was performed using the FSL FRMIB Toolbox (fsl.fmrib.ox.ac.uk/fsl/)
11.
To establish a reproducible network graph for each exam, probabilistic fiber
tracking was then performed using FSL with 500 permutations from each of the anatomic
structures for the pathways identified in the CEN. The connection pathway
between two nodes, which was the volume in image space that the connection
fibers passed through, was extracted for each valid connection using a
previously developed adaptation of the probabilistic fiber tracing technique.
12
The mean fractional anisotropy (FA), axial diffusivity (AX) and radial
diffusivity (RAD) values of the connection pathway served as the quantitative
measure for each edge and were evaluated at Pre-CRT and Post-CRT. Differences
in metrics Pre-CRT and Post-CRT (Post-CRT – Pre-CRT) were tested using a paired
T-test and differences for each risk-arm were tested using a Wilcoxon ranked
sums test to determine if the differences were significantly different from
zero. All tests were corrected for multiple comparisons using the false
discovery rate (FDR) procedure.
13Results
Figure 1 demonstrates the full CEN model used in
this study along with the identification of subnetworks within the CEN that
were used for subsequent analyses. Results from the analysis comparing
differences in DTI metrics of FA and AX Pre-CRT and Post-CRT for all 30
subjects for each of the subnetworks within the CEN are shown in Table 1. A
negative delta value indicates a decrease in the metric during CRT. There was
no significant change in the RAD metric across CRT. Due to the unbalanced
distribution of subjects within the risk arms, we were unable to find any
significant differences between the risk arms using a direct comparison.
However, average-risk subjects demonstrated only 4 subnetworks with changes in
FA that were significantly different from zero and none for AX, as shown in Table
2. Furthermore, high-risk subjects showed that half (6 of 12) subnetworks for
FA and all subnetworks for AX were significantly different from zero (Table 2).
Discussion / Conclusion
Evaluation of the CEN model demonstrated significant
changes in cerebral white matter due to CRT in children treated for
medulloblastoma. The decreased FA and AX primarily involved connections to the
basal ganglia. While the average-risk
group was too small to reliably evaluate, the larger group of high-risk
patients demonstrated that all subnetworks within the CEN demonstrated
significant decreases in AX in response to CRT. Decreased AX may occur due to
the accumulation of cellular debris, disordering of microtubule arrangement,
and filament aggregation in acute axonal injury.
14 These findings suggest
significant reduction in the microstructural integrity within the CEN
immediately after CRT in this population and support the use of the CEN model for
evaluating changes in cerebral white matter early in therapy which could
potentially be associated with later neurocognitive deficits.
Acknowledgements
We acknowledge the valuable contributions of Rhonda Simmons, advanced
signal processing technician, and funding in part by the Cancer Center Support
Grant P30 CA-21765 from the National Cancer Institute and ALSAC.References
1.
Kornienko VN, Pronin IN. Diagnostic
Neuroradiology: Springer Berlin Heidelberg; 2008.
2.
Gajjar AJ, Robinson GW.
Medulloblastoma-translating discoveries from the bench to the bedside. Nature
reviews. Clinical Oncology. 2014; 11(12):714-722.
3.
Knight SJ, Conklin HM, Palmer SL, et al.
Working memory abilities among children treated for medulloblastoma: parent
report and child performance. Journal of Pediatric Psychology. 2014;
39(5):501-511.
4.
Palmer SL, Armstrong C, Onar-Thomas A, et al.
Processing speed, attention, and working memory after treatment for
medulloblastoma: an international, prospective, and longitudinal study. Journal
of Clinical Oncology. 2013; 31(28):3494-3500.
5.
Schreiber JE, Gurney JG, Palmer SL, et al.
Examination of risk factors for intellectual and academic outcomes following
treatment for pediatric medulloblastoma. Neuro-oncology. 2014; 16(8):1129-1136.
6.
Moxon-Emre I, Bouffet E, Taylor MD, et al.
Impact of craniospinal dose, boost volume, and neurologic complications on
intellectual outcome in patients with medulloblastoma. Journal of Clinical Oncology.
2014; 32(17):1760-1768.
7.
Bressler
S, Memon V. Large-scale brain networks in cognition: emerging methods and
principles. Trends in Cognitive Sciences. 2010; 14:277-290.
8.
Memon V. Developmental pathways to functional
brain networks: emerging principles. Trends in Cognitive Sciences. 2013 17:627-640.
9.
Seeley, W. et al. Dissociable intrinsic
connectivity networks for salience processing and executive control. J.
Neuosci. 2007; 27:2349-2356.
10.
Fishl B. et al, Automatically Parcellating the
Human Cerebral Cortex, Cerebral Cortex. 2004; 14:11-22.
11.
Brehrens TE, et al. Probabilistic diffusion tractorgraphy with
multiple fibre orientations: What can we gain? Neuroimage. 2007; 34:144-55.
12.
Ji Q. et al, Extraction of fiber Pathway using
probabilistic fiber tracking, OHBM, 2015.
13. Benjamini Y, Hochberg Y. Controlling the
false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B, 1995; 57(1):289-300.
14. Aung
WY, Mar S, Benzinger TL. Diffusion tensor MRI as a biomarker in axonal and
myelin damage. Imaging in medicine. 2013; 5(5):427-440.