High-resolution electro-encephalogram: source estimates of Laplacian-transformed somatosensory-evoked potentials using a realistic subject head model constructed from magnetic resonance images

2000 ◽  
Vol 38 (5) ◽  
pp. 512-519 ◽  
Author(s):  
F. Babiloni ◽  
C. Babiloni ◽  
L. Locche ◽  
F. Cincotti ◽  
P. M. Rossini ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
High Resolution ◽  
Evoked Potentials ◽  
Somatosensory Evoked Potentials ◽  
Magnetic Resonance Images ◽  
Head Model

scholarly journals Functional Magnetic Resonance Imaging and Somatosensory Evoked Potentials in Rats with a Neonatally Induced Freeze Lesion of the Somatosensory Cortex

2004 ◽  
Vol 24 (12) ◽  
pp. 1409-1418 ◽  
Author(s):  
Wolfram Schwindt ◽  
Michael Burke ◽  
Frank Pillekamp ◽  
Heiko J. Luhmann ◽  
Mathias Hoehn
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Evoked Potentials ◽  
Somatosensory Cortex ◽  
Somatosensory Evoked Potentials ◽  
Magnetic Resonance Images ◽  
Projection Area ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging

Brain plasticity is an important mechanism for functional recovery from a cerebral lesion. The authors aimed to visualize plasticity in adult rats with a neonatal freeze lesion in the somatosensory cortex using functional magnetic resonance imaging (fMRI), and hypothesized activation outside the primary projection area. A freeze lesion was induced in the right somatosensory cortex of newborn Wistar rats (n = 12). Sham-operated animals (n = 7) served as controls. After 6 or 7 months, a neurologic examination was followed by recording of somatosensory evoked potentials (SSEPs) and magnetic resonance experiments (anatomical images, fMRI with blood oxygen level–dependent contrast and perfusion-weighted imaging) with electrical forepaw stimulation under α-chloralose anesthesia. Lesioned animals had no obvious neurologic deficits. Anatomical magnetic resonance images showed a malformed cortex or hyperintense areas (cysts) in the lesioned hemisphere. SSEPs were distorted and smaller in amplitude, and fMRI activation was significantly weaker in the lesioned hemisphere. Only in a few animals were cortical areas outside the primary sensory cortex activated. The results are discussed in respect to an apparent absence of plasticity, loss of excitable tissue, the excitability of the lesioned hemisphere, altered connectivity, and a disturbed coupling of increased neuronal activity to the hemodynamic response.

Functional information from magnetic resonance imaging

1995 ◽  
Vol 53 ◽  
pp. 84-85
Author(s):  
Alan P. Koretsky ◽  
Afonso Costa e Silva ◽  
Yi-Jen Lin
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
High Resolution ◽  
Cancer Biology ◽  
Imaging Modality ◽  
Magnetic Resonance Images ◽  
Great Success ◽  
Functional Information ◽  
Resonance Imaging ◽  
High Resolution Mri

Magnetic resonance imaging (MRI) has become established as an important imaging modality for the clinical management of disease. This is primarily due to the great tissue contrast inherent in magnetic resonance images of normal and diseased organs. Due to the wide availability of high field magnets and the ability to generate large and rapidly switched magnetic field gradients there is growing interest in applying high resolution MRI to obtain microscopic information. This symposium on MRI microscopy highlights new developments that are leading to increased resolution. The application of high resolution MRI to significant problems in developmental biology and cancer biology will illustrate the potential of these techniques.In combination with a growing interest in obtaining high resolution MRI there is also a growing interest in obtaining functional information from MRI. The great success of MRI in clinical applications is due to the inherent contrast obtained from different tissues leading to anatomical information.

scholarly journals Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction

2021 ◽  
Author(s):  
Gaia Amaranta Taberna ◽  
Jessica Samogin ◽  
Dante Mantini
Keyword(s):  
Magnetic Resonance ◽  
Brain Structures ◽  
Magnetic Resonance Images ◽  
Head Model ◽  
Source Reconstruction ◽  
Mr Images ◽  
Tissue Segmentation ◽  
Mr Image ◽  
Eeg Recordings ◽  
Tissue Modelling

AbstractIn the last years, technological advancements for the analysis of electroencephalography (EEG) recordings have permitted to investigate neural activity and connectivity in the human brain with unprecedented precision and reliability. A crucial element for accurate EEG source reconstruction is the construction of a realistic head model, incorporating information on electrode positions and head tissue distribution. In this paper, we introduce MR-TIM, a toolbox for head tissue modelling from structural magnetic resonance (MR) images. The toolbox consists of three modules: 1) image pre-processing – the raw MR image is denoised and prepared for further analyses; 2) tissue probability mapping – template tissue probability maps (TPMs) in individual space are generated from the MR image; 3) tissue segmentation – information from all the TPMs is integrated such that each voxel in the MR image is assigned to a specific tissue. MR-TIM generates highly realistic 3D masks, five of which are associated with brain structures (brain and cerebellar grey matter, brain and cerebellar white matter, and brainstem) and the remaining seven with other head tissues (cerebrospinal fluid, spongy and compact bones, eyes, muscle, fat and skin). Our validation, conducted on MR images collected in healthy volunteers and patients as well as an MR template image from an open-source repository, demonstrates that MR-TIM is more accurate than alternative approaches for whole-head tissue segmentation. We hope that MR-TIM, by yielding an increased precision in head modelling, will contribute to a more widespread use of EEG as a brain imaging technique.

Adaptive filtering for high resolution magnetic resonance images

1996 ◽  
Vol 6 (2) ◽  
pp. 367-377 ◽  
Author(s):  
Kui Ying ◽  
Bradley D. Clymer ◽  
Petra Schmalbrock
Keyword(s):  
Magnetic Resonance ◽  
High Resolution ◽  
Adaptive Filtering ◽  
Magnetic Resonance Images
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