Publications by authors named "E I Kornaropoulos"
Predicting recovery in patients with mild traumatic brain injury and a normal CT using serum biomarkers and diffusion tensor imaging (CENTER-TBI): an observational cohort study.
EClinicalMedicine
September 2024
Background: Even patients with normal computed tomography (CT) head imaging may experience persistent symptoms for months to years after mild traumatic brain injury (mTBI). There is currently no good way to predict recovery and triage patients who may benefit from early follow-up and targeted intervention. We aimed to assess if existing prognostic models can be improved by serum biomarkers or diffusion tensor imaging metrics (DTI) from MRI, and if serum biomarkers can identify patients for DTI.
View Article and Find Full Text PDFSports-related concussions may cause white matter injuries and persistent post-concussive symptoms (PPCS). We hypothesized that athletes with PPCS would have neurocognitive impairments and white matter abnormalities that could be revealed by advanced neuroimaging using ultra-high field strength diffusion tensor (DTI) and diffusion kurtosis (DKI) imaging metrics and cerebrospinal fluid (CSF) biomarkers. A cohort of athletes with PPCS severity limiting the ability to work/study and participate in sport school and/or social activities for ≥6 months completed 7T magnetic resonance imaging (MRI) (morphological T1-weighed volumetry, DTI and DKI), extensive neuropsychological testing, symptom rating, and CSF biomarker sampling.
View Article and Find Full Text PDFArticle Synopsis
- Predicting recovery outcomes after mild traumatic brain injury (mTBI) is difficult, especially since conventional MRI often shows normal results despite incomplete recovery in patients.
- Advanced imaging techniques like diffusion MRI (dMRI) can reveal microstructural brain changes, possibly improving the accuracy of outcome predictions using machine learning models known as linear support vector classifiers (linearSVCs).
- The study involved analyzing dMRI data from 179 mTBI patients and 85 controls, aiming to differentiate between patients with complete versus incomplete recovery, while also experimenting with a method called ComBat to standardize imaging data and enhance classification accuracy.
Article Synopsis
- The study investigated the effectiveness of various harmonization methods (neuroCombat, longCombat, gamCombat) to address scanner effects in multi-center neuroimaging studies focused on both structural and diffusion MRI metrics.
- Using data from 73 healthy volunteers and 161 scans across different sites and MRI machines, the research analyzed metrics related to brain structure and diffusion.
- Results showed that while structural data did not benefit from harmonization due to minor scanner effects, diffusion data exhibited significant variance that was effectively harmonized, improving detection of genuine biological differences without inflating false positives.
Background: Magnetic resonance imaging (MRI) carries prognostic importance after traumatic brain injury (TBI), especially when computed tomography (CT) fails to fully explain the level of unconsciousness. However, in critically ill patients, the risk of deterioration during transfer needs to be balanced against the benefit of detecting prognostically relevant information on MRI. We therefore aimed to assess if day of injury serum protein biomarkers could identify critically ill TBI patients in whom the risks of transfer are compensated by the likelihood of detecting management-altering neuroimaging findings.
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