Evaluation of diffusion measurements reveals radial diffusivity indicative of microstructural damage following acute, mild traumatic brain injury

Mahan, Margaret Y.; Rafter, Daniel; Truwit, Charles L.; Oswood, Mark; Samadani, Uzma

doi:10.1016/j.mri.2020.12.012

Cited by 10 publications

(5 citation statements)

References 87 publications

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“…Second, we did not investigate whether other DTI metrics, including radial diffusivity (RD) and axial diffusivity (AD) were associated with higher classification rates compared to FA. RD and AD have been shown to relate to TBI ( Khong et al, 2016 ; Mahan et al, 2021 ); however, FA was included as the primary DTI metric in the current study, to see whether similar classification rates were observed as our previous study classifying individuals with and without acute TBI recruited from the general community ( Vergara et al, 2017 ). Additionally, we only investigated whether diffusion MRI metrics could reliably classify between participants with and without self-reported history of chronic TBI in incarcerated men, not incarcerated women.…”

Section: Discussionmentioning

confidence: 74%

Machine learning classification of chronic traumatic brain injury using diffusion tensor imaging and NODDI: A replication and extension study

Maurer

Harenski

Paul

et al. 2023

Neuroimage: Reports

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Section: Discussionmentioning

confidence: 74%

Machine learning classification of chronic traumatic brain injury using diffusion tensor imaging and NODDI: A replication and extension study

Maurer

Harenski

Paul

et al. 2023

Neuroimage: Reports

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“…Further development of these algorithms could potentially reduce implicit biases in the management of brain injury and improve outcomes for all patients, although great care must be taken to make sure that the algorithms are themselves not biased. Examples of assessors to include in these prognosticating algorithms include measures of brainstem function such as pupillometry, eye tracking or other quantitative cranial nerve function ( 30 – 33 ), serum markers ( 26 ), and image analysis ( 36 – 39 ). These are measures that should potentially be able to be confirmed as “colorblind.” Volitional assessments that rely on physician bias, level of patient education, cooperation, absence of cultural dissonance and language skills will likely contribute to inequity.…”

Section: Discussionmentioning

confidence: 99%

“…The utilization of objective measures with machine learning (artificial intelligence) has the potential to reduce inequities in the neurosurgical field through automation, improved accuracy, speed, accessibility, and reduced costs ( 34 – 36 ). A major caveat is that we need to ensure that data elements incorporated into future algorithms do not perpetuate inequity ( 40 – 42 ).…”

Section: Discussionmentioning

confidence: 99%

“…Volitional assessments that rely on physician bias, level of patient education, cooperation, absence of cultural dissonance and language skills will likely contribute to inequity. The utilization of objective measures with machine learning (artificial intelligence) has the potential to reduce inequities in the neurosurgical field through automation, improved accuracy, speed, accessibility, and reduced costs (34)(35)(36). A major caveat is that we need to ensure that data elements incorporated into future algorithms do not perpetuate inequity (40)(41)(42).…”

Section: Can Artificial Intelligence or Advanced Automation Correct T...mentioning

confidence: 99%

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Consequences of inequity in the neurosurgical workforce: Lessons from traumatic brain injury

Venkatesh

Bravo²,

Schaaf³

et al. 2022

Front. Surg.

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Women and minorities leave or fail to advance in the neurosurgical workforce more frequently than white men at all levels from residency to academia. The consequences of this inequity are most profound in fields such as traumatic brain injury (TBI), which lacks objective measures. We evaluated published articles on TBI clinical research and found that TBI primary investigators or corresponding authors were 86·5% White and 59·5% male. First authors from the resulting publications were 92.6% white. Most study participants were male (68%). 64·4% of NIH-funded TBI clinical trials did not report or recruit any black subjects and this number was even higher for other races and the Hispanic ethnicity. We propose several measures for mitigation of the consequences of the inequitable workforce in traumatic brain injury that could potentially contribute to more equitable outcomes. The most immediately feasible of these is validation and establishment of objective measures for triage and prognostication that are less susceptible to bias than current protocols. We call for incorporation of gender and race neutral metrics for TBI evaluation to standardize classification of injury. We offer insights into how socioeconomic factors contribute to increased death rates from women and minority groups. We propose the need to study how these disparities are caused by unfair health insurance reimbursement practices. Surgical and clinical research inequities have dire consequences, and until those inequities can be corrected, mitigation of those consequences requires system wide change.

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“…Examples of such parameters include the mean, axial, and radial diffusivity (MD, AD, and RD, respectively) and the fractional anisotropy (FA). Changes in these parameters have been detected in numerous conditions, including aging ( 1 ), traumatic brain injury (TBI) ( 2 , 3 ), schizophrenia ( 4 , 5 ), Parkinson's disease ( 6 , 7 ), multiple sclerosis (MS) ( 8 ), and systemic lupus erythematosus (SLE) ( 9 – 12 ), ( 13 – 15 ). Diffusion kurtosis imaging (DKI) is an extension to DTI that provides information complementary to DTI ( 16 – 18 ), but requires a more comprehensive acquisition protocol and, thus, longer scan times.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of Diffusion MRI to White Matter Pathology: Influence of Diffusion Protocol, Magnetic Field Strength, and Processing Pipeline in Systemic Lupus Erythematosus

et al. 2022

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There are many ways to acquire and process diffusion MRI (dMRI) data for group studies, but it is unknown which maximizes the sensitivity to white matter (WM) pathology. Inspired by this question, we analyzed data acquired for diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) at 3T (3T-DTI and 3T-DKI) and DTI at 7T in patients with systemic lupus erythematosus (SLE) and healthy controls (HC). Parameter estimates in 72 WM tracts were obtained using TractSeg. The impact on the sensitivity to WM pathology was evaluated for the diffusion protocol, the magnetic field strength, and the processing pipeline. Sensitivity was quantified in terms of Cohen's d for group comparison. Results showed that the choice of diffusion protocol had the largest impact on the effect size. The effect size in fractional anisotropy (FA) across all WM tracts was 0.26 higher when derived by DTI than by DKI and 0.20 higher in 3T compared with 7T. The difference due to the diffusion protocol was larger than the difference due to magnetic field strength for the majority of diffusion parameters. In contrast, the difference between including or excluding different processing steps was near negligible, except for the correction of distortions from eddy currents and motion which had a clearly positive impact. For example, effect sizes increased on average by 0.07 by including motion and eddy correction for FA derived from 3T-DTI. Effect sizes were slightly reduced by the incorporation of denoising and Gibbs-ringing removal (on average by 0.011 and 0.005, respectively). Smoothing prior to diffusion model fitting generally reduced effect sizes. In summary, 3T-DTI in combination with eddy current and motion correction yielded the highest sensitivity to WM pathology in patients with SLE. However, our results also indicated that the 3T-DKI and 7T-DTI protocols used here may be adjusted to increase effect sizes.

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Evaluation of diffusion measurements reveals radial diffusivity indicative of microstructural damage following acute, mild traumatic brain injury

Cited by 10 publications

References 87 publications

Machine learning classification of chronic traumatic brain injury using diffusion tensor imaging and NODDI: A replication and extension study

Machine learning classification of chronic traumatic brain injury using diffusion tensor imaging and NODDI: A replication and extension study

Consequences of inequity in the neurosurgical workforce: Lessons from traumatic brain injury

Sensitivity of Diffusion MRI to White Matter Pathology: Influence of Diffusion Protocol, Magnetic Field Strength, and Processing Pipeline in Systemic Lupus Erythematosus

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