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Structural brain network properties and cognitive impairment in adolescents after radiation therapy
Justin P Yuan1, Melanie A Morrison1, Angela Jakary1, Sabine Mueller2, Olga Tymofiyeva1, Duan Xu1, and Janine M Lupo1

1Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, 2Department of Neurology, University of California, San Francisco, San Francisco, CA, United States

Synopsis

Cranial radiation therapy (CRT) is an effective brain cancer treatment but many patients exhibit cognitive deficits over time. We studied these deficits in adolescent and young adult survivors of pediatric brain cancer with prior CRT using white matter graph network analysis. Executive function and working memory performance were correlated with structural connectivity metrics. Global integration and segregation metrics were associated with neurocognitive deficits, as well as connectivity at domain-specific regions. Our results support past findings of CRT’s negative cognitive effects and suggest that they are driven through structural changes in brain white matter

Introduction

The prognosis for pediatric brain cancers has improved such that there is a growing population of long-term cancer survivors. Many of these patients have undergone whole-brain cranial radiation therapy (CRT), a now-standard treatment that has significantly improved outcomes. However, CRT is linked to neurocognitive impairments1 that often affect attention, working memory, visual-motor processing, and spatial relations.2-4 Such declines have been observed in adult populations who received prior radiation as children5 and newly-diagnosed elderly patients receiving radiation therapy for the first time.6

Previous studies using magnetic resonance imaging (MRI) have found that CRT is associated with structural brain changes such as altered white matter morphology.7 Graph network analysis of white matter (WM) connectivity is a promising method to study CRT effects on cognitive deficits, as such functions engage multiple brain regions. The method models the brain as a network of connected edges and nodes, which are quantitatively described with graph theory.8 The goal of this study was to examine the long-term effects of whole-brain and whole-ventricular CRT in adolescent and young adult survivors of pediatric brain cancer using graph analysis of ultra-high-field (7T) MRI to study the associations between current neurocognitive deficits and WM structural connectivity.

Methods

Ten adolescent and young adult subjects (6F, mean age ± SD = 17.7 ± 5.0yrs.) with prior whole-brain CRT for pediatric brain cancers participated in this study (Table 1). Subjects were scanned using a GE 7T MRI scanner using a protocol that included a T1 IR-SPGR sequence (TR/TE=4s/1.75ms, TI=1350ms (T1), flip angle=8o, 1mm isotropic resolution, parallel imaging R=2.2, and Tacq=4:28min) and a multiband two-shell diffusion sequence with 90 directions (30 at b=1000s/mm2; 60 at b=2000s/mm2), 7 b0 images, and 1 additional b0 image with reversed PE gradient, TR/TE=4000/71.6ms, isotropic 2mm resolution, FOV=256x256 mm, 128x128 matrix, parallel imaging R=3, and Tacq=7:20min.

Diffusion image processing was performed using FSL,9 MATLAB, and Diffusion Toolkit,10 and included: multiband reconstruction, TOPUP and eddy current correction, and deterministic tractography (FACT).11 T1-weighted data was registered to the DTI b0-volume and the MNI template (FLIRT),12,13 allowing AAL atlas application to create 90 network ROIs. A weighted connectivity matrix was constructed, with average voxel fractional anisotropy (FA) along streamlines as the edge weight.

Global graph metrics included measures of segregation (clustering coefficient and transitivity), integration (characteristic path length and efficiency), and small-worldness. Local metrics included node strength at regions associated with executive function14 (middle frontal gyrus, [MFG]) and working memory15,16 (superior frontal gyrus [SFG] and superior parietal lobule [SPL]).Subjects completed a computerized battery of neurocognitive tests (CogState),17,18 which included the Groton Maze Learning (GML) task,19,20 an executive functioning and spatial working memory task, and the One-Back (ONB) working memory task21 (Table 2). Graph metric and cognitive task performance were associated using a Pearson correlation coefficient.

Results

Figure 1 illustrates a tractogram and connectivity matrix of a study participant. Subjects performed poorly for both tasks relative to age-matched peers: GML=-0.49±1.39 (mean±std. dev); ONB=-1.17±1.52. There were significant negative correlations between GML legal errors, a spatial working memory component of the task, and both global metrics (clustering coefficient, efficiency, and transitivity) and local node strength at all regions except the R-MFG (Figure 2). Table 3 details the full results.

Discussion

Our results indicated an inverse relationship between WM structural connectivity properties and cognitive performance in survivors who received whole-brain CRT. The tumors’ infratentorial locations suggested that the changes were due to CRT rather than the tumor itself. These findings support previous reports of CRT-related cognitive deficits22 and WM structural changes.23 Our graph analysis of WM connectivity showed that global WM segregation and integration were negatively correlated with spatial working memory performance. Local node strength findings suggested that decreased WM connections from executive function brain regions (L-MFG) and working memory regions (bilateral SFG and SPL) were associated with increased error rates. These indicated that WM network changes, particularly at domain-specific regions, may underlie CRT-associated cognitive deficits. Ongoing analyses include longitudinal follow-up, differentiating between RT-type, and control analysis with healthy adolescents and cancer survivors without CRT treatment.

Acknowledgements

NIH Natl Inst. Child Health & Human Dev. R01 HD079568 and R01 HD072074

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Figures

Figure 1. Graph network analysis visualizations. a) WM tracts in an adolescent survivor of pediatric medulloblastoma with prior whole-brain CRT (over DWI image). Tractogram derived from ultra-high-field (7T) multishell diffusion data, acquired with 90 directions at two b-values (30d-1000; 60d-2000). b) Connectivity matrix of the same subject illustrating FA-weighted connectivity between the 90 AAL atlas-based ROIs. c&d) Axial tractogram and network representation plot. Local analysis nodes are highlighted in red (middle frontal gyrus), blue (superior frontal gyrus), green (superior parietal lobule). Plot made with Gephi24.

Figure 2. Scatterplots showing significant graph metrics of WM connectivity and GML legal errors, a measure of spatial working memory. a) Global analysis. Clust. Coef. = clustering coefficient; Trans = transitivity. b) Local analysis – node strength results at domain-related regions. MFG = middle frontal gyrus; SFG = superior frontal gyrus; SPL = superior parietal lobule; L = left; R = right.

Table 1. Demographics. Subjects consisted of adolescents and young adults with previous pediatric brain cancer. All subjects received whole-brain or whole-ventricular CRT treatment as children.

Table 2. Cognitive task summary. Subjects’ cognitive performance was assessed with the computerized CogState battery. The Groton Maze Learning and One-Back tasks’ measures and tested cognitive domains are detailed. Primary measure z-scores were calculated relative to age-matched CogState data.

Table 3. Pearson correlation coefficients between global and local graph metrics and the GML and ONB task subcomponents. Significant correlations (p<0.05) are highlighted in green; r=Pearson correlation coefficient, p=p-value, Z=z-score.

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)
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