Diffusion tensor imaging (DTI) is a noninvasive imaging method for measuring the diffusion properties of the underlying white matter tracts through which epileptiform activity is propagated. This study investigates the structural abnormalities quantified by DTI in mesial temporal lobe epilepsy (mTLE). Fiber tracts passing through 54 anatomical sites in 12 adult mTLE patients and 12 age- and gender-matched controls were identified using DTI tractography. DTI nodal degree (ND) and laterality index were then calculated. ND laterality, after Bonferroni adjustment, showed significant differences for right versus left mTLE in gyrus rectus, insular cortex, precuneus and superior temporal gyrus (p<;0.025). None of these anatomical sites showed statistically significant differences in ND laterality between right and left sides of the controls. Laterality models determined by logistic regression on the ND laterality data agreed with the side of epileptogenicity as it pertained to the gyrus rectus, insular cortex, precuneus and superior temporal gyrus for 89%, 72%, 83% and 92% of the patients, respectively. Combining the laterality measures in these four anatomical sites improved the results further with correct lateralization of 100% for all patients. The proposed methodology for using DTI connectivity to investigate diffusion abnormalities related to focal epileptogenicity and propagation can provide a further means of noninvasive lateralization.
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http://dx.doi.org/10.1109/EMBC.2016.7591978 | DOI Listing |
Ann Clin Transl Neurol
January 2025
Department of Neuro-Urology, Balgrist University Hospital, University of Zürich, Zürich, Switzerland.
Objective: To characterize structural integrity of the lumbosacral enlargement and conus medullaris within one month after spinal cord injury (SCI).
Methods: Lumbosacral cord MRI data were acquired in patients with sudden onset (<7 days) SCI at the cervical or thoracic level approximately one month after injury and in healthy controls. Tissue integrity and loss were evaluated through diffusion tensor (DTI) and T2*-weighted imaging (cross-sectional area [CSA] measurements).
Med Phys
January 2025
Paul Albrechtsen Research Institute, CancerCare Manitoba, Winnipeg, Canada.
Background: The treatment of glioblastomas (GBM) with radiation therapy is extremely challenging due to their invasive nature and high recurrence rate within normal brain tissue.
Purpose: In this work, we present a new metric called the tumour spread (TS) map, which utilizes diffusion tensor imaging (DTI) to predict the probable direction of tumour cells spread along fiber tracts. We hypothesized that the TS map could serve as a predictive tool for identifying patterns of likely recurrence in patients with GBM and, therefore, be used to modify the delivery of radiation treatment to pre-emptively target regions at high risk of tumour spread.
Brain Sci
January 2025
CVJ Operative Unit, CVJ Research Center Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy.
: Diffusion tensor imaging (DTI), a variant of Diffusion Weighted Imaging (DWI), enables a neuroanatomical microscopic-like examination of the brain, which can detect brain damage using physical parameters. DTI's application to traumatic brain injury (TBI) has the potential to reveal radiological features that can assist in predicting the clinical outcomes of these patients. What is the ongoing role of DTI in detecting brain alterations and predicting neurological outcomes in patients with moderate to severe traumatic brain injury and/or diffuse axonal injury? : A scoping review of the PubMed, Scopus, EMBASE, and Cochrane databases was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines.
View Article and Find Full Text PDFBrain Sci
December 2024
Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA.
Brain connectivity analysis plays a crucial role in unraveling the complex network dynamics of the human brain, providing insights into cognitive functions, behaviors, and neurological disorders. Traditional graph-theoretical methods, while foundational, often fall short in capturing the high-dimensional and dynamic nature of brain connectivity. Graph Neural Networks (GNNs) have recently emerged as a powerful approach for this purpose, with the potential to improve diagnostics, prognostics, and personalized interventions.
View Article and Find Full Text PDFBr J Pharmacol
January 2025
Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.
Drug discovery is a complicated process through which new therapeutics are identified to prevent and treat specific diseases. Identification of drug-target interactions (DTIs) stands as a pivotal aspect within the realm of drug discovery and development. The traditional process of drug discovery, especially identification of DTIs, is marked by its high costs of experimental assays and low success rates.
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