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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., Competing Interests: JB, BK, MS, CC, and DF were employed by General Dynamics Information Technology. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Burns, Kalinosky, Sloan, Cerna, Fines, Valdez and Voorhees.)

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.

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 ( R 2  = 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.

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 ( r 2 = 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.

Network analysis based on graph theory depicts the brain as a complex network that allows inspection of overall brain connectivity pattern and calculation of quantifiable network metrics. To date, large-scale network analysis has not been applied to resting-state functional networks in complete spinal cord injury (SCI) patients. To characterize modular reorganization of whole brain into constituent nodes and compare network metrics between SCI and control subjects, fifteen subjects with chronic complete cervical SCI and 15 neurologically intact controls were scanned. The data were preprocessed followed by parcellation of the brain into 116 regions of interest (ROI). Correlation analysis was performed between every ROI pair to construct connectivity matrices and ROIs were categorized into distinct modules. Subsequently, local efficiency (LE) and global efficiency (GE) network metrics were calculated at incremental cost thresholds. The application of a modularity algorithm organized the whole-brain resting-state functional network of the SCI and the control subjects into nine and seven modules, respectively. The individual modules differed across groups in terms of the number and the composition of constituent nodes. LE demonstrated statistically significant decrease at multiple cost levels in SCI subjects. GE did not differ significantly between the two groups. The demonstration of modular architecture in both groups highlights the applicability of large-scale network analysis in studying complex brain networks. Comparing modules across groups revealed differences in number and membership of constituent nodes, indicating modular reorganization due to neural plasticity.

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.

Large-scale network analysis characterizes the brain as a complex network of nodes and edges to evaluate functional connectivity patterns. The utility of graph-based techniques has been demonstrated in an increasing number of resting-state functional MRI (rs-fMRI) studies in the normal and diseased brain. However, to our knowledge, graph theory has not been used to study the reorganization pattern of resting-state brain networks in patients with traumatic complete spinal cord injury (SCI). In the present analysis, we applied a graph-theoretical approach to explore changes to global brain network architecture as a result of SCI. Fifteen subjects with chronic (> 2 years) complete (American Spinal Injury Association [ASIA] A) cervical SCI and 15 neurologically intact controls were scanned using rs-fMRI. The data were preprocessed followed by parcellation of the brain into 116 regions of interest (ROI) or nodes. The average time series was extracted at each node, and correlation analysis was performed between every pair of nodes. A functional connectivity matrix for each subject was then generated. Subsequently, the matrices were averaged across groups, and network changes were evaluated between groups using the network-based statistic (NBS) method. Our results showed decreased connectivity in a subnetwork of the whole brain in SCI compared with control subjects. Upon further examination, increased connectivity was observed in a subnetwork of the sensorimotor cortex and cerebellum network in SCI. In conclusion, our findings emphasize the applicability of NBS to study functional connectivity architecture in diseased brain states. Further, we show reorganization of large-scale resting-state brain networks in traumatic SCI, with potential prognostic and therapeutic implications.

Functional magnetic resonance imaging (fMRI) studies have demonstrated alterations during task-induced brain activation in spinal cord injury (SCI) patients. The interruption to structural integrity of the spinal cord and the resultant disrupted flow of bidirectional communication between the brain and the spinal cord might contribute to the observed dynamic reorganization (neural plasticity). However, the effect of SCI on brain resting-state connectivity patterns remains unclear. We undertook a prospective resting-state fMRI (rs-fMRI) study to explore changes to cortical activation patterns following SCI. With institutional review board approval, rs-fMRI data was obtained in eleven patients with complete cervical SCI (>2 years post injury) and nine age-matched controls. The data was processed using the Analysis of Functional Neuroimages software. Region of interest (ROI) based analysis was performed to study changes in the sensorimotor network using pre- and post-central gyri as seed regions. Two-sampled t-test was carried out to check for significant differences between the two groups. SCI patients showed decreased functional connectivity in motor and sensory cortical regions when compared to controls. The decrease was noted in ipsilateral, contralateral, and interhemispheric regions for left and right precentral ROIs. Additionally, the left postcentral ROI demonstrated increased connectivity with the thalamus bilaterally in SCI patients. Our results suggest that cortical activation patterns in the sensorimotor network undergo dynamic reorganization following SCI. The presence of these changes in chronic spinal cord injury patients is suggestive of the inherent neural plasticity within the central nervous system.

Background: The purpose of this study was to determine whether DTI changes in the brain induced by a thoracic spinal cord injury are sensitive to varying severity of spinal contusion in rats., Methods: A control, mild, moderate, or severe contusion injury was administered over the eighth thoracic vertebral level in 32 Sprague-Dawley rats. At 11 weeks postinjury, ex vivo DTI of the brain was performed on a 9.4T Bruker scanner using a pulsed gradient spin-echo sequence., Results: Mean water diffusion in the internal capsule regions of the brain and pyramid locations of the brainstem were correlated with motor function (r(2)  = 0.55). Additionally, there were significant differences between injury severity groups for mean diffusivity and fractional anisotropy at regions associated with the corticospinal tract (P = 0.05)., Conclusion: These results indicate that DTI is sensitive to changes in brain tissue as a consequence of thoracic SCI., (© 2015 Wiley Periodicals, Inc.)

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.

Purpose: Digital x-ray tomosynthesis (DTS) has the potential to provide 3D information about the knee joint in a load-bearing posture, which may improve diagnosis and monitoring of knee osteoarthritis compared with projection radiography, the current standard of care. Manually quantifying and visualizing the joint space width (JSW) from 3D tomosynthesis datasets may be challenging. This work developed a semiautomated algorithm for quantifying the 3D tibiofemoral JSW from reconstructed DTS images. The algorithm was validated through anthropomorphic phantom experiments and applied to three clinical datasets., Methods: A user-selected volume of interest within the reconstructed DTS volume was enhanced with 1D multiscale gradient kernels. The edge-enhanced volumes were divided by polarity into tibial and femoral edge maps and combined across kernel scales. A 2D connected components algorithm was performed to determine candidate tibial and femoral edges. A 2D joint space width map (JSW) was constructed to represent the 3D tibiofemoral joint space. To quantify the algorithm accuracy, an adjustable knee phantom was constructed, and eleven posterior-anterior (PA) and lateral DTS scans were acquired with the medial minimum JSW of the phantom set to 0-5 mm in 0.5 mm increments (VolumeRad™, GE Healthcare, Chalfont St. Giles, United Kingdom). The accuracy of the algorithm was quantified by comparing the minimum JSW in a region of interest in the medial compartment of the JSW map to the measured phantom setting for each trial. In addition, the algorithm was applied to DTS scans of a static knee phantom and the JSW map compared to values estimated from a manually segmented computed tomography (CT) dataset. The algorithm was also applied to three clinical DTS datasets of osteoarthritic patients., Results: The algorithm segmented the JSW and generated a JSW map for all phantom and clinical datasets. For the adjustable phantom, the estimated minimum JSW values were plotted against the measured values for all trials. A linear fit estimated a slope of 0.887 (R² = 0.962) and a mean error across all trials of 0.34 mm for the PA phantom data. The estimated minimum JSW values for the lateral adjustable phantom acquisitions were found to have low correlation to the measured values (R² = 0.377), with a mean error of 2.13 mm. The error in the lateral adjustable-phantom datasets appeared to be caused by artifacts due to unrealistic features in the phantom bones. JSW maps generated by DTS and CT varied by a mean of 0.6 mm and 0.8 mm across the knee joint, for PA and lateral scans. The tibial and femoral edges were successfully segmented and JSW maps determined for PA and lateral clinical DTS datasets., Conclusions: A semiautomated method is presented for quantifying the 3D joint space in a 2D JSW map using tomosynthesis images. The proposed algorithm quantified the JSW across the knee joint to sub-millimeter accuracy for PA tomosynthesis acquisitions. Overall, the results suggest that x-ray tomosynthesis may be beneficial for diagnosing and monitoring disease progression or treatment of osteoarthritis by providing quantitative images of JSW in the load-bearing knee.