Lesion Induced Error on Automated Measures of Brain Volume: Data From a Pediatric Traumatic Brain Injury Cohort

Abstract

Structural segmentation of T1-weighted (T1w) MRI has shown morphometric differences, both compared to controls and longitudinally, following a traumatic brain injury (TBI). While many patients with TBI present with abnormalities on structural MRI images, most neuroimaging software packages have not been systematically evaluated for accuracy in the presence of these pathology-related MRI abnormalities. The current study aimed to assess whether acute MRI lesions (MRI acquired 7–71 days post-injury) cause error in the estimates of brain volume produced by the semi-automated segmentation tool, Freesurfer. More specifically, to investigate whether this error was global, the presence of lesion-induced error in the contralesional hemisphere, where no abnormal signal was present, was measured. A dataset of 176 simulated lesion cases was generated using actual lesions from 16 pediatric TBI (pTBI) cases recruited from the emergency department and 11 typically-developing controls. Simulated lesion cases were compared to the “ground truth” of the non-lesion control-case T1w images. Using linear mixed-effects models, results showed that hemispheric measures of cortex volume were significantly lower in the contralesional-hemisphere compared to the ground truth. Interestingly, however, cortex volume (and cerebral white matter volume) were not significantly different in the lesioned hemisphere. However, percent volume difference (PVD) between the simulated lesion and ground truth showed that the magnitude of difference of cortex volume in the contralesional-hemisphere (mean PVD = 0.37%) was significantly smaller than that in the lesioned hemisphere (mean PVD = 0.47%), suggesting a small, but systematic lesion-induced error. Lesion characteristics that could explain variance in the PVD for each hemisphere were investigated. Taken together, these results suggest that the lesion-induced error caused by simulated lesions was not focal, but globally distributed. Previous post-processing approaches to adjust for lesions in structural analyses address the focal region where the lesion was located however, our results suggest that focal correction approaches are insufficient for the global error in morphometric measures of the injured brain.

The Unresolved Methodological Challenge of Detecting Neuroplastic Changes in Astronauts

“…However, it is possible that similar errors of varying magnitude are present in other studies in which changes in the rates and location of tissue misclassification can produce artifactual findings. Identifying more reliable data acquisition and processing methods that improve or better leverage tissue contrast, and therefore reduce the reliance on spatial tissue probability priors, may also improve segmentation performance in volumetric analyses of the brain in the presence of atrophy [ 31 ], unique morphology [ 60 , 61 ], or normal development [ 62 ], in which salient changes in brain and/or CSF volumes may interact with the positioning of the cortex with respect to other tissues and produce a spatial bias in segmentation errors between conditions of interest.…”

Association between mild traumatic brain injury, brain structure, and mental health outcomes in the Adolescent Brain Cognitive Development Study

“…Another possibility for our unexpected finding (i.e., decreased CSF volume in children with an mTBI) is lesion-induced errors in the volumetric results. Prior research with pediatric populations and TBI have found biases in automated software packages (e.g., Freesurfer pipelines) that can be attributed to lesions (62). Methods that incorporate a focal pre-processing approach (e.g., enantiomorphic filling of the damaged area) can reduce errors and improve the detection of physiologically meaningful differences (62, 63).…”

Personalised structural connectomics for moderate-to-severe traumatic brain injury

“…The present study introduces personalized measurement and analysis of individual connectomic profiles in five chronic moderate-to-severe TBI patients with varying lesion loads, mechanisms of injury, age at injury, and burden of neural/cognitive symptoms. Our implementation extends current methods by addressing the long-standing and prominent challenge of analysing TBI structural profiles when automatic sub/cortical segmentation or parcellation of MRIs fail in the presence of lesions 18 . Significantly, this problem is addressed here by synergizing connectomic analysis with virtual brain repair , where the lesion is replaced by healthy looking tissue in the T1-weighted images (lesion inpainting).…”

“…(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. moderate to severe TBI cohorts with diverse brain injuries pose a serious technical challenge, as the available tools for MRI processing to generate connectomes fail in such 18 .…”

“…Since graph metrics were recently shown to have prognostic potential 7,17 , this type of approach could provide valuable information to clinicians, leading to neuroimaging-guided strategies to improve functional outcomes of TBI patients. However, personalised connectomics in moderate to severe TBI cohorts with diverse brain injuries pose a serious technical challenge, as the available tools for MRI processing to generate connectomes fail in such 18 .…”