The effect of IDH1 mutation on the structural connectome in malignant astrocytoma

Abstract

Mutation of the IDH1 gene is associated with differences in malignant astrocytoma growth characteristics that impact phenotypic severity, including cognitive impairment. We previously demonstrated greater cognitive impairment in patients with IDH1 wild type tumor compared to those with IDH1 mutant, and therefore we hypothesized that brain network organization would be lower in patients with wild type tumors. Volumetric, T1-weighted MRI scans were obtained retrospectively from 35 patients with IDH1 mutant and 32 patients with wild type malignant astrocytoma (mean age = 45 ± 14 years) and used to extract individual level, gray matter connectomes. Graph theoretical analysis was then applied to measure efficiency and other connectome properties for each patient. Cognitive performance was categorized as impaired or not and random forest classification was used to explore factors associated with cognitive impairment. Patients with wild type tumor demonstrated significantly lower network efficiency in several medial frontal, posterior parietal and subcortical regions (p < 0.05, corrected for multiple comparisons). Patients with wild type tumor also demonstrated significantly higher incidence of cognitive impairment (p = 0.03). Random forest analysis indicated that network efficiency was inversely, though nonlinearly associated with cognitive impairment in both groups (p < 0.0001). Cognitive reserve appeared to mediate this relationship in patients with mutant tumor suggesting greater neuroplasticity and/or benefit from neuroprotective factors. Tumor volume was the greatest contributor to cognitive impairment in patients with wild type tumor, supporting our hypothesis that greater lesion momentum between grades may cause more disconnection of core neurocircuitry and consequently lower efficiency of information processing.

Altered structural hub connectivity and its clinical relevance in glioma

“…2,[12][13][14] In newly diagnosed glioma, hub-related functional connectivity in the hemisphere contralateral to the tumor is increased, 7 possibly reflecting network failure in response to local dysfunction as an initially compensatory but long-term detrimental relaying of load. 10 Structural connectivity throughout macroscopically tumor-free brain regions also experimentally associates with molecular subtype 15 and prognosis, 16 indicating that connectivity measures may inform our understanding of performance status and survival beyond the currently known molecular determinants.…”

Probability of Alzheimer's disease in breast cancer survivors based on gray‐matter structural network efficiency

“…Gray-matter covariance networks were constructed for each participant using a similarity-based extraction method [26], [27] (Extract Individual GM Networks Toolbox v20150902 https://github.com/bettytijms/Single_Subject_Grey_Matter_Networks). Network nodes were defined as 3 × 3 × 3 voxel cubes spanning the entire gray-matter volume (i.e., 27 gray-matter values per cube).…”

“…Matrices were then submitted to graph theoretical analysis using Brain Connectivity Toolbox [29] and our bNets Toolbox (https://github.com/srkesler/bNets.git) implemented in MATLAB v2014b (Mathworks, Inc, Natick, MA). Connectome metrics were calculated as described previously [26], [27]. Specifically, efficiency is defined as the inverse of the average shortest path between nodes and is high when nodes can interact directly.…”

“…Nodes were assigned 1 of 90 Automated Anatomical Labeling Atlas (AAL) labels based on the node's voxel coordinates. Efficiency was calculated for each node as the average efficiency across all nodes with the same AAL label [26], [27]. We have previously demonstrated impaired connectome efficiency associated with cancer and its treatments [9], [27], and others have shown impaired efficiency in individuals with early AD [30].…”

“…Accuracy significance was evaluated using a two-sided exact binomial test in addition to the area under the curve of the receiver operating characteristic. Feature importance was determined using mean decrease in Gini index [27].…”