Functional Brain Basis of Hypnotizability

Context Focused hypnotic concentration is a model for brain control over sensation and behavior. Pain and anxiety can be effectively alleviated by hypnotic suggestion, which modulates activity in brain regions associated with focused attention, but the specific neural network underlying this phenomenon is not known. Objective To investigate the brain basis of hypnotizability. Design Cross-sectional, in vivo neuroimaging study performed from November 2005 through July 2006. Setting Academic medical center at Stanford University School of Medicine. Patients Twelve adults with high and 12 adults with low hypnotizability. Main Outcome Measures Functional magnetic resonance imaging to measure functional connectivity networks at rest, including default-mode, salience, and executive-control networks; structural T1 magnetic resonance imaging to measure regional gray and white matter volumes; and diffusion tensor imaging to measure white matter microstructural integrity. Results High compared with low hypnotizable individuals had greater functional connectivity between the left dorsolateral prefrontal cortex, an executive-control region of the brain, and the salience network composed of the dorsal anterior cingulate cortex, anterior insula, amygdala, and ventral striatum, involved in detecting, integrating, and filtering relevant somatic, autonomic, and emotional information using independent component analysis. Seed-based analysis confirmed elevated functional coupling between the dorsal anterior cingulate cortex and the dorsolateral prefrontal cortex in high compared with low hypnotizable individuals. These functional differences were not due to any variation in brain structure in these regions, including regional gray and white matter volumes and white matter microstructure. Conclusions Our results provide novel evidence that altered functional connectivity in the dorsolateral prefrontal cortex and dorsal anterior cingulate cortex may underlie hypnotizability. Future studies focusing on how these functional networks change and interact during hypnosis are warranted

Acute Stroke Imaging Research Roadmap II

No abstract available

Feasibility of marker-free motion tracking for motion-corrected MRI and PET-MRI

Prospective motion correction is a very promising compensation approach for magnetic resonance imaging (MRI) studies impacted by motion. It has the advantage over retrospective methods of being applicable to any pulse sequence. In prospective motion correction of brain studies, the magnetic field gradients and radio frequency waveforms are adjusted in real time in response to motion of the head, thereby maintaining a fixed frame of reference for the brain inside the scanner. A key requirement of this approach is accurate and rapidly sampled head pose information. Optical motion tracking is typically used to obtain these pose estimates, however current methods are limited by the need to attach physical markers to the skin. This readily leads to decoupling of the head and marker motion, reducing the effectiveness of correction. In this work we investigate the feasibility and initial performance of an optical motion tracking method which does not require any attached markers. The method relies on detecting natural features or amplified features (from skins stamps on the forehead) using multiple cameras, and estimates pose using a 3D-2D registration between a growing database of known 3D locations on the forehead and these features. We have performed out-of-bore and in-bore experiments to test the accuracy performance of this marker-free method for very small feature patches consistent with the limited visibility afforded by head coils used during imaging. The results showed excellent agreement between the marker-free method and our current ground truth method based on wireless MR-sensitive markers

Marker-free optical stereo motion tracking for in-bore MRI and PET-MRI application

Purpose: Prospective motion correction is arguably the “silver bullet” solution for magnetic resonance imaging (MRI) studies impacted by motion, applicable to almost any pulse sequence and immune from the spin history artifacts introduced by a moving object. In prospective motion correction, the magnetic field gradients and radio frequency waveforms are adjusted in real time in response to measured head motion so as to maintain the head in a stationary reference frame relative to the scanner. Vital for this approach are accurate and rapidly sampled head pose measurements, which may be obtained optically using cameras. However, most optical methods are limited by the need to attach physical markers to the skin, which leads to decoupling of head and marker motion and reduces the effectiveness of correction. In this work we investigate the feasibility and initial performance of a stereo-optical motion tracking method which does not require any attached markers. Methods: The method relies on detecting distinctive natural features or amplified features (using skin stamps) directly on the forehead in multiple camera views, and then deriving pose estimates via a 3D-2D registration between the skin features and a database of forehead landmarks. To demonstrate the feasibility and potential accuracy of the marker-free method for discrete (step-wise) head motion, we performed out-of-bore and in-bore experiments using robotically and manually controlled phantoms in addition to in-bore testing on human volunteers. We also developed a convenient out-of-bore test bed to benchmark and optimize the motion tracking performance. Results: For out-of-bore phantom tests, the pose estimation accuracy (compared to robotic ground truth) was 0.14 mm and 0.23 degrees for incremental translation and rotation, respectively. For arbitrary motion, the pose accuracy obtained using the smallest forehead feature patch was equivalent to 0.21 0.11 mm positional accuracy in the striatum. For in-bore phantom experiments, the accuracy of rigid-body motion parameters (compared to wireless MR-sensitive markers) was 0.08–0.41 0.18 mm/0.05–0.3 0.12 deg and 0.14–0.16 0.12 mm/0.08-0.17 0.08 deg for the small and large feature patches, respectively. In vivo results in human volunteers indicated sub-millimeter and sub-degree pose accuracy for all rotations and translations except the depth direction (max error 1.8 mm) when compared to a registration-based approach. Conclusions: In both bench-top and in vivo experiments we demonstrate the feasibility of using very small feature patches directly on the skin to obtain high accuracy head pose measurements needed for motion-correction in MRI brain studies. The optical technique uses in-bore cameras and is consistent with the limited visibility of the forehead afforded by head coils used in brain imaging. Future work will focus on optimization of the technique and demonstration in prospective motion correction

Marker‐free optical stereo motion tracking for in‐bore MRI and PET‐MRI application

Purpose: Prospective motion correction is arguably the “silver bullet” solution for magnetic resonance imaging (MRI) studies impacted by motion, applicable to almost any pulse sequence and immune from the spin history artifacts introduced by a moving object. In prospective motion correction, the magnetic field gradients and radio frequency waveforms are adjusted in real time in response to measured head motion so as to maintain the head in a stationary reference frame relative to the scanner. Vital for this approach are accurate and rapidly sampled head pose measurements, which may be obtained optically using cameras. However, most optical methods are limited by the need to attach physical markers to the skin, which leads to decoupling of head and marker motion and reduces the effectiveness of correction. In this work we investigate the feasibility and initial performance of a stereo-optical motion tracking method which does not require any attached markers. Methods: The method relies on detecting distinctive natural features or amplified features (using skin stamps) directly on the forehead in multiple camera views, and then deriving pose estimates via a 3D-2D registration between the skin features and a database of forehead landmarks. To demonstrate the feasibility and potential accuracy of the marker-free method for discrete (step-wise) head motion, we performed out-of-bore and in-bore experiments using robotically and manually controlled phantoms in addition to in-bore testing on human volunteers. We also developed a convenient out-of-bore test bed to benchmark and optimize the motion tracking performance. Results: For out-of-bore phantom tests, the pose estimation accuracy (compared to robotic ground truth) was 0.14 mm and 0.23 degrees for incremental translation and rotation, respectively. For arbitrary motion, the pose accuracy obtained using the smallest forehead feature patch was equivalent to 0.21 0.11 mm positional accuracy in the striatum. For in-bore phantom experiments, the accuracy of rigid-body motion parameters (compared to wireless MR-sensitive markers) was 0.08–0.41 0.18 mm/0.05–0.3 0.12 deg and 0.14–0.16 0.12 mm/0.08-0.17 0.08 deg for the small and large feature patches, respectively. In vivo results in human volunteers indicated sub-millimeter and sub-degree pose accuracy for all rotations and translations except the depth direction (max error 1.8 mm) when compared to a registration-based approach. Conclusions: In both bench-top and in vivo experiments we demonstrate the feasibility of using very small feature patches directly on the skin to obtain high accuracy head pose measurements needed for motion-correction in MRI brain studies. The optical technique uses in-bore cameras and is consistent with the limited visibility of the forehead afforded by head coils used in brain imaging. Future work will focus on optimization of the technique and demonstration in prospective motion correction

Inter-sequence and inter-imaging unit variability of diffusion tensor MR imaging histogram-derived metrics of the brain in healthy volunteers

Background and purposeDiffusion tensor MR imaging has the potential to improve our ability to monitor several neurologic conditions. As a preliminary step to the assessment of the role of diffusion tensor MR imaging in the context of longitudinal and multicenter studies, we evaluated the effect of sequence-, imaging unit-, and imaging-reimaging-induced variations on diffusion tensor MR imaging quantities derived from histogram analysis of a large portion of the central brain of healthy volunteers.MethodsEach of eight healthy volunteers underwent imaging on two MR imaging units using three different pulsed gradient spin-echo single shot echo-planar pulse sequences (each of them having a different diffusion gradient scheme). Four additional healthy participants underwent imaging twice on the same imaging unit to assess imaging-reimaging variability.ResultsFor mean diffusivity histograms, the differences between inter-sequence and inter-imaging unit coefficients of variation were significant for all the considered quantities with P values ranging from.003 to ConclusionThis study shows that inter-sequence, imaging-reimaging, and inter-imaging unit variabilities of diffusion tensor MR imaging-derived measurements are relatively low, suggesting that diffusion tensor MR imaging might provide additional measures of outcome with which to assess the evolution of brain structural damage in large scale studies of various neurologic conditions

Inter-sequence and inter-imaging unit variability of diffusion tensor MR imaging histogram-derived metrics of the brain in healthy volunteers

Background and purposeDiffusion tensor MR imaging has the potential to improve our ability to monitor several neurologic conditions. As a preliminary step to the assessment of the role of diffusion tensor MR imaging in the context of longitudinal and multicenter studies, we evaluated the effect of sequence-, imaging unit-, and imaging-reimaging-induced variations on diffusion tensor MR imaging quantities derived from histogram analysis of a large portion of the central brain of healthy volunteers.MethodsEach of eight healthy volunteers underwent imaging on two MR imaging units using three different pulsed gradient spin-echo single shot echo-planar pulse sequences (each of them having a different diffusion gradient scheme). Four additional healthy participants underwent imaging twice on the same imaging unit to assess imaging-reimaging variability.ResultsFor mean diffusivity histograms, the differences between inter-sequence and inter-imaging unit coefficients of variation were significant for all the considered quantities with P values ranging from.003 to ConclusionThis study shows that inter-sequence, imaging-reimaging, and inter-imaging unit variabilities of diffusion tensor MR imaging-derived measurements are relatively low, suggesting that diffusion tensor MR imaging might provide additional measures of outcome with which to assess the evolution of brain structural damage in large scale studies of various neurologic conditions

Non-rigid motion detection for motion tracking of the head

Optical motion tracking systems are effective tools for measuring head motion during MRI and PET scans in order to correct for motion. Most systems rely on the attachment of fiducial markers which can slip or become decoupled from the head, causing erroneous motion estimates which can introduce further image artifacts. In this work, we investigated two methods of detecting non-rigid motion, both of which can be easily incorporated into a stereo-optical feature-based motion tracking system. The tracking system tracks detected features on small patches of the forehead. By monitoring these features, surface deformations on parts of the face that deform non-rigidly with respect to the rest of the head can be detected and potentially characterized. We investigated two methods of detecting non-rigid deformations: one involved measuring distances between detected landmarks and comparing these distances to previous frames; the other used a neural network to classify a group of landmarks as either `rigid' or `non-rigid'. A simulation tool was also developed to aid in the characterization of non-rigid motion and its effects. Landmark distance discrepancies were found to be correlated closely with pose measurement errors in the feature-based motion tracking system, suggesting it is a useful metric for detecting non-rigid motion. The trained neural network was able to classify a collection of landmarks as 'rigid' with 99.8 % accuracy and classified the `non-rigid' case with 93.3 % accuracy