Background: Diffusion tensor imaging (DTI) biomarkers can be used to quantify microstructural changes in the cerebral white matter (WM) following injury.
Objectives: This prospective single-center study aimed to evaluate whether atlas-based DTI-derived metrics obtained within 1 week after stroke can predict the motor outcome at 3 months.
Methods: Forty patients with small acute stroke (2-7 days after onset) involving the corticospinal tract were included. Each patient underwent magnetic resonance imaging (MRI) within 1 week and at 3 months after stroke, and the changes based on DTI-derived metrics were compared by performing WM tract atlas-based quantitative analysis.
Results: A total of 40 patients were included, with median age 63.5 years and a majority of males (72.5%). Patients were classified into good-prognosis group (mRS 0-2, = 27) and poor-prognosis group (mRS 3-5, = 13) by outcome. The median (25-75 percentile) of MD (0.7 (0.6-0.7) vs. 0.7 (0.7-0.8); = 0.049) and AD (0.6 (0.5, 0.7) vs. 0.7 (0.6, 0.8); = 0.023) ratios within 1 week were significantly lower in the poor-prognosis group compared to the good-prognosis group. The ROC curve of the combined DTI-derived metrics model showed comparable Youden index (65.5% vs. 58.4%-65.4%) and higher specificity (96.3% vs. 69.2%-88.5%) compared to clinical indexes. The area under the ROC curve of the combined DTI-derived metrics model is comparable to those of the clinical indexes (all > 0.1) and higher than those of the individual DTI-derived metrics parameters.
Conclusions: Atlas-based DTI-derived metrics at acute stage provide objective information for prognosis prediction of patients with ischemic or lacunar stroke.
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http://dx.doi.org/10.1080/10749357.2023.2214977 | DOI Listing |
CNS Neurosci Ther
December 2024
Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Background: Mild traumatic brain injury (mTBI) frequently results in persistent cognitive, emotional, and functional impairments, closely linked to disruptions in the default mode network (DMN). Understanding the mechanisms driving these network abnormalities is critical for advancing diagnostic and therapeutic strategies.
Methods: This study adopted a multimodal approach, combining functional connectivity (FC) analysis, diffusion tensor imaging (DTI), and gene expression profiling to investigate DMN disruptions in mTBI.
J Magn Reson Imaging
December 2024
Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Background: Selective inversion recovery quantitative magnetization transfer (SIR-qMT)-derived macromolecular to free water pool size ratio (PSR) and diffusion tensor imaging (DTI)-derived radial diffusivity (RD) are potential metrics for assessing myelin integrity in multiple sclerosis (MS). However, establishing their accuracy in identifying tissue injury is essential for clinical translation.
Purpose: To compare the accuracy and Cohen's effect size (ES) of PSR and RD in detecting and quantifying tissue injury in early MS.
Expert Rev Anticancer Ther
November 2024
Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Res Sq
September 2024
VA San Diego Healthcare System.
Acad Radiol
September 2024
Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center (VUMC), Nashville, TN (A.A.T., C.G., T.V., K.Y., S.A., P.A., F.B.); Department of Neurology, VA Medical Center, TN Valley Healthcare System, Nashville, TN (F.B.). Electronic address:
Rationale And Objectives: Several quantitative magnetic resonance imaging (MRI) methods are available to measure tissue injury in multiple sclerosis (MS), but their pathological specificity remains limited. The multi-compartment diffusion imaging using the spherical mean technique (SMT) overcomes several technical limitations of the diffusion-weighted image signal, thus delivering metrics with increased pathological specificity. Given these premises, here we assess whether the SMT-derived apparent axonal volume (V) provides a better tissue classifier than the diffusion tensor imaging (DTI)-derived axial diffusivity (AD) in the white matter (WM) of MS brains.
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