Dilation of the superior sagittal sinus detected in rat model of mild traumatic brain injury using 1 T magnetic resonance imaging

Introduction

Mild traumatic brain injury (mTBI) is a common injury that can lead to temporary and, in some cases, life-long disability. Magnetic resonance imaging (MRI) is widely used to diagnose and study brain injuries and diseases, yet mTBI remains notoriously difficult to detect in structural MRI. mTBI is thought to be caused by microstructural or physiological changes in the function of the brain that cannot be adequately captured in structural imaging of the gray and white matter. However, structural MRIs may be useful in detecting significant changes in the cerebral vascular system (e.g., the blood-brain barrier (BBB), major blood vessels, and sinuses) and the ventricular system, and these changes may even be detectable in images taken by low magnetic field strength MRI scanners (<1.5T).

Methods

In this study, we induced a model of mTBI in the anesthetized rat animal model using a commonly used linear acceleration drop-weight technique. Using a 1T MRI scanner, the brain of the rat was imaged, without and with contrast, before and after mTBI on post-injury days 1, 2, 7, and 14 (i.e., P1, P2, P7, and P14).

Results

Voxel-based analyses of MRIs showed time-dependent, statistically significant T2-weighted signal hypointensities in the superior sagittal sinus (SSS) and hyperintensities of the gadolinium-enhanced T1-weighted signal in the superior subarachnoid space (SA) and blood vessels near the dorsal third ventricle. These results showed a widening, or vasodilation, of the SSS on P1 and of the SA on P1-2 on the dorsal surface of the cortex near the site of the drop-weight impact. The results also showed vasodilation of vasculature near the dorsal third ventricle and basal forebrain on P1-7.

Discussion

Vasodilation of the SSS and SA near the site of impact could be explained by the direct mechanical injury resulting in local changes in tissue function, oxygenation, inflammation, and blood flow dynamics. Our results agreed with literature and show that the 1T MRI scanner performs at a level comparable to higher field strength scanners for this type of research.

Computational modeling investigation of pulsed high peak power microwaves and the potential for traumatic brain injury

When considering safety standards for human exposure to radiofrequency (RF) and microwave energy, the dominant concerns pertain to a thermal effect. However, in the case of high-power pulsed RF/microwave energy, a rapid thermal expansion can lead to stress waves within the body. In this study, a computational model is used to estimate the temperature profile in the human brain resulting from exposure to various RF/microwave incident field parameters. The temperatures are subsequently used to simulate the resulting mechanical response of the brain. Our simulations show that, for certain extremely high-power microwave exposures (permissible by current safety standards), very high stresses may occur within the brain that may have implications for neuropathological effects. Although the required power densities are orders of magnitude larger than most real-world exposure conditions, they can be achieved with devices meant to emit high-power electromagnetic pulses in military and research applications.

Indirect Structural Connectivity Identifies Changes in Brain Networks After Stroke

Background/Purpose: The purpose of this study was (1) to identify changes in structural connectivity after stroke and (2) to relate changes in indirect connectivity to post-stroke impairment. Methods: A novel measure of indirect connectivity was implemented to assess the impact of stroke on brain connectivity. Probabilistic tractography was performed on 13 chronic stroke and 16 control participants to estimate connectivity between gray matter (GM) regions. The Fugl-Meyer assessment of motor impairment was measured for stroke participants. Network measures of direct and indirect connectivity were calculated, and these measures were linearly combined with measures of white matter integrity to predict motor impairment. Results: We found significantly reduced indirect connectivity in the frontal and parietal lobes, ipsilesional subcortical regions, and bilateral cerebellum after stroke. When added to the regression analysis, the volume of GM with reduced indirect connectivity significantly improved the correlation between image parameters and upper extremity motor impairment (R² = 0.71, p < 0.05). Conclusion: This study provides evidence of changes in indirect connectivity in regions remote from the lesion, particularly in the cerebellum and regions in the fronto-parietal cortices, and these changes correlate with upper extremity motor impairment. These results highlight the value of using measures of indirect connectivity to identify the effect of stroke on brain networks. Impact statement Changes in indirect structural connectivity occur in regions distant from a lesion after stroke, highlighting the impact that stroke has on brain functional networks. Specifically, losses in indirect structural connectivity occur in hubs with high centrality, including the fronto-parietal cortices and cerebellum. These losses in indirect connectivity more accurately reflect motor impairments than measures of direct structural connectivity. As a consequence, indirect structural connectivity appears to be important to recovery after stroke and imaging biomarkers that incorporate indirect structural connectivity might improve prognostication of stroke outcomes.

Tasked-Based Functional Brain Connectivity in Multisensory Control of Wrist Movement After Stroke

In this study we documented brain connectivity associated with multisensory integration during wrist control in healthy young adults, aged matched controls and stroke survivors. A novel functional MRI task paradigm involving wrist movement was developed to gain insight into the effects of multimodal sensory feedback on brain functional networks in stroke participants. This paradigm consisted of an intermittent position search task using the wrist during fMRI signal acquisition with visual and auditory feedback of proximity to a target position. We enrolled 12 young adults, 10 participants with chronic post-stroke hemiparesis, and nine age-matched controls. Activation maps were obtained, and functional connectivity networks were calculated using an independent component analysis (ICA) approach. Task-based networks were identified using activation maps, and nodes were obtained from the ICA components. These nodes were subsequently used for connectivity analyses. Stroke participants demonstrated significantly greater contralesional activation than controls during the visual feedback condition and less ipsilesional activity than controls during the auditory feedback condition. The sensorimotor component obtained from the ICA differed between rest and task for control and stroke participants: task-related lateralization to the contralateral cortex was observed in controls, but not in stroke participants. Connectivity analyses between the lesioned sensorimotor cortex and the contralesional cerebellum demonstrated decreased functional connectivity in stroke participants (p < 0.005), which was positively correlated the Box and Blocks arm function test (r² = 0.59). These results suggest that task-based functional connectivity provides detail on changes in brain networks in stroke survivors. The data also highlight the importance of cerebellar connections for recovery of arm function after stroke.

Structurofunctional resting-state networks correlate with motor function in chronic stroke

Purpose

Motor function and recovery after stroke likely rely directly on the residual anatomical connections in the brain and its resting-state functional connectivity. Both structural and functional properties of cortical networks after stroke are revealed using multimodal magnetic resonance imaging (MRI). Specifically, functional connectivity MRI (fcMRI) can extract functional networks of the brain at rest, while structural connectivity can be estimated from white matter fiber orientations measured with high angular-resolution diffusion imaging (HARDI). A model that marries these two techniques may be the key to understanding functional recovery after stroke. In this study, a novel set of voxel-level measures of structurofunctional correlations (SFC) was developed and tested in a group of chronic stroke subjects.

Methods

A fully automated method is presented for modeling the structure-function relationship of brain connectivity in individuals with stroke. Brains from ten chronic stroke subjects and nine age-matched controls were imaged with a structural T1-weighted scan, resting-state fMRI, and HARDI. Each subject's T1-weighted image was nonlinearly registered to a T1-weighted 152-brain MNI template using a local histogram-matching technique that alleviates distortions caused by brain lesions. Fractional anisotropy maps and mean BOLD images of each subject were separately registered to the individual's T1-weighted image using affine transformations. White matter fiber orientations within each voxel were estimated with the q-ball model, which approximates the orientation distribution function (ODF) from the diffusion-weighted measurements. Deterministic q-ball tractography was performed in order to obtain a set of fiber trajectories. The new structurofunctional correlation method assigns each voxel a new BOLD time course based on a summation of its structural connections with a common fiber length interval. Then, the voxel's original time-course was correlated with this fiber-distance BOLD signal to derive a novel structurofunctional correlation coefficient. These steps were repeated for eight fiber distance intervals, and the maximum of these correlations was used to define an intrinsic structurofunctional correlation (iSFC) index. A network-based SFC map (nSFC) was also developed in order to enhance resting-state functional networks derived from conventional functional connectivity analyses. iSFC and nSFC maps were individually compared between stroke subjects and controls using a voxel-based two-tailed Student's t-test (alpha = 0.01). A linear regression was also performed between the SFC metrics and the Box and Blocks Score, a clinical measure of arm motor function.

Results

Significant decreases (p < 0.01) in iSFC were found in stroke subjects within functional hubs of the brain, including the precuneus, prefrontal cortex, posterior parietal cortex, and cingulate gyrus. Many of these differences were significantly correlated with the Box and Blocks Score. The nSFC maps of prefrontal networks in control subjects revealed localized increases within the cerebellum, and these enhancements were diminished in stroke subjects. This finding was further supported by a reduction in functional connectivity between the prefrontal cortex and cerebellum. Default-mode network nSFC maps were higher in the contralesional hemisphere of lower-functioning stroke subjects.

Conclusion

The results demonstrate that changes after a stroke in both intrinsic and network-based structurofunctional correlations at rest are correlated with motor function, underscoring the importance of residual structural connectivity in cortical networks.

White matter structural connectivity is associated with sensorimotor function in stroke survivors

Purpose

Diffusion tensor imaging (DTI) provides functionally relevant information about white matter structure. Local anatomical connectivity information combined with fractional anisotropy (FA) and mean diffusivity (MD) may predict functional outcomes in stroke survivors. Imaging methods for predicting functional outcomes in stroke survivors are not well established. This work uses DTI to objectively assess the effects of a stroke lesion on white matter structure and sensorimotor function.

Methods

A voxel-based approach is introduced to assess a stroke lesion's global impact on motor function. Anatomical T1-weighted and diffusion tensor images of the brain were acquired for nineteen subjects (10 post-stroke and 9 age-matched controls). A manually selected volume of interest was used to alleviate the effects of stroke lesions on image registration. Images from all subjects were registered to the images of the control subject that was anatomically closest to Talairach space. Each subject's transformed image was uniformly seeded for DTI tractography. Each seed was inversely transformed into the individual subject space, where DTI tractography was conducted and then the results were transformed back to the reference space. A voxel-wise connectivity matrix was constructed from the fibers, which was then used to calculate the number of directly and indirectly connected neighbors of each voxel. A novel voxel-wise indirect structural connectivity (VISC) index was computed as the average number of direct connections to a voxel's indirect neighbors. Voxel-based analyses (VBA) were performed to compare VISC, FA, and MD for the detection of lesion-induced changes in sensorimotor function. For each voxel, a t-value was computed from the differences between each stroke brain and the 9 controls. A series of linear regressions was performed between Fugl-Meyer (FM) assessment scores of sensorimotor impairment and each DTI metric's log number of voxels that differed from the control group.

Results

Correlation between the logarithm of the number of significant voxels in the ipsilesional hemisphere and total Fugl-Meyer score was moderate for MD (R2 = 0.512), and greater for VISC (R2 = 0.796) and FA (R2 = 0.674). The slopes of FA (p = 0.0036), VISC (p = 0.0005), and MD (p = 0.0199) versus the total FM score were significant. However, these correlations were driven by the upper extremity motor component of the FM score (VISC: R2 = 0.879) with little influence of the lower extremity motor component (FA: R2 = 0.177).

Conclusion

The results suggest that a voxel-wise metric based on DTI tractography can predict upper extremity sensorimotor function of stroke survivors, and that supraspinal intraconnectivity may have a less dominant role in lower extremity function.

•

An intrinsic voxel-based structural connectivity metric is proposed.

•

The metric enhances the impact of stroke lesions on the distant voxels.

•

Whole-brain extralesional anatomical connectivity predicts functional outcome.

•

Functional impact of a lesion is determined by residual anatomical connectivity.

•

Connectivity to the posterior parietal cortex is a key to sensorimotor function.