In recent years, the field of neuroimaging has undergone dramatic development. Specifically, of importance for clinicians and researchers managing patients with epilepsies, new methods of brain imaging in search of the seizure-producing abnormalities have been implemented, and older methods have undergone additional refinement. Methodology to predict seizure freedom and cognitive outcome has also rapidly progressed. In general, the image data processing methods are very different and more complicated than even a decade ago. In this review, we identify the recent developments in neuroimaging that are aimed at improved management of epilepsy patients. Advances in structural imaging, diffusion imaging, fMRI, structural and functional connectivity, hybrid imaging methods, quantitative neuroimaging, and machine-learning are discussed. We also briefly summarize the potential new developments that may shape the field of neuroimaging in the near future and may advance not only our understanding of epileptic networks as the source of treatment-resistant seizures but also better define the areas that need to be treated in order to provide the patients with better long-term outcomes.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/s11910-017-0746-x | DOI Listing |
Neuroimage
January 2025
High-Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for MR Imaging Biomarkers (BIOMAK), Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Objectives: To assess topographical patterns of metabolic abnormalities in the cerebrum of multiple sclerosis (MS) patients and their relationship to clinical disability using rapid echo-less 3D-MR spectroscopic imaging (MRSI) at 7T.
Materials And Methods: This study included 26 MS patients (13 women; median age 34) and 13 age- and sex-matched healthy controls (7 women; median age 33). Metabolic maps were obtained using echo-less 3D-MRSI at 7T with a 64 × 64 × 33 matrix and a nominal voxel size of 3.
Neuroimage Clin
January 2025
Backgrounds/objective: Deep brain stimulation (DBS) has proved the viability of alleviating depression symptoms by stimulating deep reward-related nuclei. This study aims to investigate the abnormal connectivity profiles among superficial, intermediate, and deep brain regions within the reward circuit in major depressive disorder (MDD) and therefore provides references for identifying potential superficial cortical targets for non-invasive neuromodulation.
Methods: Resting-state functional magnetic resonance imaging data were collected from a cohort of depression patients (N = 52) and demographically matched healthy controls (N = 60).
J Integr Neurosci
January 2025
Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA.
Resting state networks (RSNs) of the brain are characterized as correlated spontaneous time-varying fluctuations in the absence of goal-directed tasks. These networks can be local or large-scale spanning the brain. The study of the spatiotemporal properties of such networks has helped understand the brain's fundamental functional organization under healthy and diseased states.
View Article and Find Full Text PDFJ Clin Med
January 2025
Department of Neurosurgery, "Carol Davila" University of Medicine and Pharmacy, 020021 Bucharest, Romania.
The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding of the brain, unlocking new possibilities in research, diagnosis, and therapy. This review explores how AI's cutting-edge algorithms-ranging from deep learning to neuromorphic computing-are revolutionizing neuroscience by enabling the analysis of complex neural datasets, from neuroimaging and electrophysiology to genomic profiling. These advancements are transforming the early detection of neurological disorders, enhancing brain-computer interfaces, and driving personalized medicine, paving the way for more precise and adaptive treatments.
View Article and Find Full Text PDFHum Brain Mapp
February 2025
BCBL - Basque Center on Cognition Brain and Language, Donostia - San Sebastián, Spain.
Population receptive field (pRF) mapping is a quantitative functional MRI (fMRI) analysis method that links visual field positions with specific locations in the visual cortex. A common preprocessing step in pRF analyses involves projecting volumetric fMRI data onto the cortical surface, typically leading to upsampling of the data. This process may introduce biases in the resulting pRF parameters.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!