Objective: To determine whether reliable brain atrophy measures can be obtained from post-contrast 3D T1-weighted images in patients with multiple sclerosis (MS) using FreeSurfer.
Methods: Twenty-two patients with MS were included, in which 3D T1-weighted MR images were obtained during the same scanner visit, with the same acquisition protocol, before and after administration of gadolinium-based contrast agents (GBCAs). Two FreeSurfer versions (v.6.0.1 and v.7.1.1.) were applied to calculate grey matter (GM) and white matter (WM) volumes and global and regional cortical thickness. The consistency between measures obtained in pre- and post-contrast images was assessed by intra-class correlation coefficient (ICC), the difference was investigated by paired t-tests, and the mean percentage increase or decrease was calculated for total WM and GM matter volume, total deep GM and thalamus volume, and mean cortical thickness.
Results: Good to excellent reliability was found between all investigated measures, with ICC ranging from 0.926 to 0.996, all p values < 0.001. GM volumes and cortical thickness measurements were significantly higher in post-contrast images by 3.1 to 17.4%, while total WM volume decreased significantly by 1.7% (all p values < 0.001).
Conclusion: The consistency between values obtained from pre- and post-contrast images was excellent, suggesting it may be possible to extract reliable brain atrophy measurements from T1-weighted images acquired after administration of GBCAs, using FreeSurfer. However, absolute values were systematically different between pre- and post-contrast images, meaning that such images should not be compared directly. Potential systematic effects, possibly dependent on GBCA dose or the delay time after contrast injection, should be investigated.
Trial Registration: Clinical trials.gov. identifier: NCT00360906.
Key Points: • The influence of gadolinium-based contrast agents (GBCAs) on atrophy measurements is still largely unknown and challenges the use of a considerable source of historical and prospective real-world data. • In 22 patients with multiple sclerosis, the consistency between brain atrophy measurements obtained from pre- and post-contrast images was excellent, suggesting it may be possible to extract reliable atrophy measurements in T1-weighted images acquired after administration of GBCAs, using FreeSurfer. • Absolute values were systematically different between pre- and post-contrast images, meaning that such images should not be compared directly, and measurements extracted from certain regions (e.g., the temporal pole) should be interpreted with caution.
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http://dx.doi.org/10.1007/s00330-021-08405-8 | DOI Listing |
Eur J Neurosci
January 2025
WRIISC-Women, VA Palo Alto Health Care System, Palo Alto, California, USA.
Combination of structural and functional brain connectivity methods provides a more complete and effective avenue into the investigation of cortical network responses to traumatic brain injury (TBI) and subtle alterations in brain connectivity associated with TBI. Structural connectivity (SC) can be measured using diffusion tensor imaging to evaluate white matter integrity, whereas functional connectivity (FC) can be studied by examining functional correlations within or between functional networks. In this study, the alterations of SC and FC were assessed for TBI patients, with and without chronic symptoms (TBIcs/TBIncs), compared with a healthy control group (CG).
View Article and Find Full Text PDFNucleic Acids Res
January 2025
Institute for Biomedicine and Glycomics, School of Environment and Science, Griffith University, 46 Don Young Road, Brisbane QLD 4111, Australia., Brisbane, QLD 4111, Australia.
While many genetic tools exist for zebrafish, this animal model still lacks robust gene-silencing and microRNA-delivery technologies enabling spatio-temporal control and traceability. We have recently demonstrated that engineered pri-miR backbones can trigger stable gene knockdown and/or express microRNA(s) of choice in this organism. However, this miRNA-expressing technology presents important limitations.
View Article and Find Full Text PDFPostoperative delirium is the most common postsurgical complication in older adults and is associated with an increased risk of long-term cognitive decline and Alzheimer's disease (AD) and related dementias (ADRD). However, the neurological basis of this increased risk- whether postoperative delirium unmasks latent preoperative pathology or leads to AD-relevant pathology after perioperative brain injury-remains unclear. Recent advancements in neuroimaging techniques now enable the detection of subtle brain features or damage that may underlie clinical symptoms.
View Article and Find Full Text PDFFront Neurol
January 2025
Department of Medicine, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
Multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSD) are distinct demyelinating diseases of the central nervous system, each characterized by unique patterns of motor, sensory, and visual dysfunction. While MS typically affects the brain and spinal cord, NMOSD predominantly targets the optic nerves and spinal cord. This study aims to elucidate the morphometric differences between MS and NMOSD by focusing on gray matter volume changes in specific brain regions.
View Article and Find Full Text PDFBrain Behav
January 2025
Department of Neurology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
Background: While automated methods for differential diagnosis of parkinsonian syndromes based on MRI imaging have been introduced, their implementation in clinical practice still underlies considerable challenges.
Objective: To assess whether the performance of classifiers based on imaging derived biomarkers is improved with the addition of basic clinical information and to provide a practical solution to address the insecurity of classification results due to the uncertain clinical diagnosis they are based on.
Methods: Retro- and prospectively collected data from multimodal MRI and standardized clinical datasets of 229 patients with PD (n = 167), PSP (n = 44), or MSA (n = 18) underwent multinomial classification in a benchmark study comparing the performance of nine machine learning methods.
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