Multiband functional magnetic resonance imaging (fMRI) for functional connectivity assessments

During resting state the brain exhibits synchronized activity within all major brain networks. Using blood oxygen level dependent (BOLD) resting state functional magnetic resonance imaging (fMRI) based detection it is possible to quantify the degree of correlation, connectivity, between regions of interest and assess information regarding the integrity of the inter-regional functional integration. A newly available multiband echo planar imaging (EPI) fMRI sequence allows for faster scan times which possibly allows us to better examine large-scale networks and increase the understanding of brain function/dysfunction. This thesis will assess how the newly developed sequence compares to a conventional EPI sequence for detecting resting state connectivity of canonical brain networks. The data acquisitions were made on a 3 Tesla scanner using a 32 channel head coil. The hypothesis was that the multiband sequence would produce a better result since it has faster sampling rate, thus more data points in its time-series to support the statistical analyses. Using Pearson’s linear correlation between the average time-series (approximately 12 minutes long) within a seed-region and all voxels contained in the image volume, correlation maps where created for each of the eight participants using data normalized to Montreal Neurological Institute (MNI) space. The resting state networks (RSN) were then found by performing a one sample T-test on group level. Six seed-coordinates, based on literature, where used revealing the the homotopic connections in anterior Hippocampus, Motor cortex, Dorsal attention, Visual and the Default mode network (DMN) as well for an anterior-posterior connection in the DMN. By comparing the maximum T-values within the regions for the RSN no systematic difference could be found between the multiband and conventional fMRI data. Further tests were conducted to evaluate if the sequences would differentiate in their results if the acquisition time was shortened, i.e shortening the time-series in the voxels. However no such difference could be established.Importantly, the results are specific to the 32 channel head coil used in the current study. Presumably recently available and improved coil designs could better exploit the multiband technique

Multiband functional magnetic resonance imaging (fMRI) for functional connectivity assessments

During resting state the brain exhibits synchronized activity within all major brain networks. Using blood oxygen level dependent (BOLD) resting state functional magnetic resonance imaging (fMRI) based detection it is possible to quantify the degree of correlation, connectivity, between regions of interest and assess information regarding the integrity of the inter-regional functional integration. A newly available multiband echo planar imaging (EPI) fMRI sequence allows for faster scan times which possibly allows us to better examine large-scale networks and increase the understanding of brain function/dysfunction. This thesis will assess how the newly developed sequence compares to a conventional EPI sequence for detecting resting state connectivity of canonical brain networks. The data acquisitions were made on a 3 Tesla scanner using a 32 channel head coil. The hypothesis was that the multiband sequence would produce a better result since it has faster sampling rate, thus more data points in its time-series to support the statistical analyses. Using Pearson’s linear correlation between the average time-series (approximately 12 minutes long) within a seed-region and all voxels contained in the image volume, correlation maps where created for each of the eight participants using data normalized to Montreal Neurological Institute (MNI) space. The resting state networks (RSN) were then found by performing a one sample T-test on group level. Six seed-coordinates, based on literature, where used revealing the the homotopic connections in anterior Hippocampus, Motor cortex, Dorsal attention, Visual and the Default mode network (DMN) as well for an anterior-posterior connection in the DMN. By comparing the maximum T-values within the regions for the RSN no systematic difference could be found between the multiband and conventional fMRI data. Further tests were conducted to evaluate if the sequences would differentiate in their results if the acquisition time was shortened, i.e shortening the time-series in the voxels. However no such difference could be established.Importantly, the results are specific to the 32 channel head coil used in the current study. Presumably recently available and improved coil designs could better exploit the multiband technique

Multiband functional magnetic resonance imaging (fMRI) for functional connectivity assessments

During resting state the brain exhibits synchronized activity within all major brain networks. Using blood oxygen level dependent (BOLD) resting state functional magnetic resonance imaging (fMRI) based detection it is possible to quantify the degree of correlation, connectivity, between regions of interest and assess information regarding the integrity of the inter-regional functional integration. A newly available multiband echo planar imaging (EPI) fMRI sequence allows for faster scan times which possibly allows us to better examine large-scale networks and increase the understanding of brain function/dysfunction. This thesis will assess how the newly developed sequence compares to a conventional EPI sequence for detecting resting state connectivity of canonical brain networks. The data acquisitions were made on a 3 Tesla scanner using a 32 channel head coil. The hypothesis was that the multiband sequence would produce a better result since it has faster sampling rate, thus more data points in its time-series to support the statistical analyses. Using Pearson’s linear correlation between the average time-series (approximately 12 minutes long) within a seed-region and all voxels contained in the image volume, correlation maps where created for each of the eight participants using data normalized to Montreal Neurological Institute (MNI) space. The resting state networks (RSN) were then found by performing a one sample T-test on group level. Six seed-coordinates, based on literature, where used revealing the the homotopic connections in anterior Hippocampus, Motor cortex, Dorsal attention, Visual and the Default mode network (DMN) as well for an anterior-posterior connection in the DMN. By comparing the maximum T-values within the regions for the RSN no systematic difference could be found between the multiband and conventional fMRI data. Further tests were conducted to evaluate if the sequences would differentiate in their results if the acquisition time was shortened, i.e shortening the time-series in the voxels. However no such difference could be established.Importantly, the results are specific to the 32 channel head coil used in the current study. Presumably recently available and improved coil designs could better exploit the multiband technique

Palliativ vård vid livets slut : - En litteraturöversikt om behov av stöd ur ett närståendeperspektiv

2022-03-22</p

Palliativ vård vid livets slut : - En litteraturöversikt om behov av stöd ur ett närståendeperspektiv

2022-03-22</p

Assessing cerebral arterial pulse wave velocity using 4D flow MRI

Intracranial arterial stiffening is a potential early marker of emerging cerebrovascular dysfunction and could be mechanistically involved in disease processes detrimental to brain function via several pathways. A prominent consequence of arterial wall stiffening is the increased velocity at which the systolic pressure pulse wave propagates through the vasculature. Previous non-invasive measurements of the pulse wave propagation have been performed on the aorta or extracranial arteries with results linking increased pulse wave velocity to brain pathology. However, there is a lack of intracranial “target-organ” measurements. Here we present a 4D flow MRI method to estimate pulse wave velocity in the intracranial vascular tree. The method utilizes the full detectable branching structure of the cerebral vascular tree in an optimization framework that exploits small temporal shifts that exists between waveforms sampled at varying depths in the vasculature. The method is shown to be stable in an internal consistency test, and of sufficient sensitivity to robustly detect age-related increases in intracranial pulse wave velocity