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\u27s 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\u27s 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\u27s 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\u27s 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

Severity of Spinal Cord Injury Influences Diffusion Tensor Imaging of the Brain

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 (r2 = 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

Quantifying the Tibiofemoral Joint Space Using X-ray Tomosynthesis

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 (VolumeRadTM, 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 (R2¼0.962) 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 (R2¼0.377), 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

Alterations in Cortical Sensorimotor Connectivity following Complete Cervical Spinal Cord Injury: A Prospective Resting-State fMRI Study

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 (\u3e2 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

Evaluation of Whole-Brain Resting-State Functional Connectivity in Spinal Cord Injury - A Large-Scale Network Analysis Using Network Based Statistic

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 restingstate 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 (\u3e 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

Structural-functional brain connectivity underlying integrative sensorimotor function after stroke

In this dissertation research project, we demonstrated the relationship between the structural and functional connections across the brain in stroke survivors. We used this information to predict arm function in stroke survivors, suggesting that the tools developed through this research will be useful for prescribing individualized rehabilitation strategies in people after stroke. Current clinical methods for rehabilitating sensorimotor function after stroke are not based on the locus of injury in the brain. Instead, therapies are generalized, treating symptoms such as weakness and spasticity. This results in outcomes that are highly variable, with severity of impairment immediately following stroke as the best predictor of recovery. By using measures of brain structural and functional relations, we can better prognosticate and plan rehabilitation interventions. This research study utilized diffusion and functional magnetic resonance imaging (MRI) to quantify anatomical connectivity and functional networks of the brain after stroke. In the first aim, diffusion MRI was used to track the white matter pathways throughout the entire brain. A new imaging biomarker sensitive to stroke lesions was developed that quantifies the level of anatomical connections between every point in the brain. It was found that cortical areas most responsible for integration of sensorimotor and multisensory integration were the best predictors of motor impairments in chronic stroke subjects. Our second aim investigated the role of multisensory integration during sensorimotor control in healthy adults 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 subjects. We found that the loss of functional interactions between the cerebellum and lesioned sensorimotor area were correlated with loss of movement function. Our final aim investigated the relationship between structural and functional connectivity after stroke. A model that marries diffusion MRI fiber tracking and resting-state functional MRI was designed to enhance indirect functional connections with structural information. The technique was capable of detecting changes in cortical networks that were not seen in functional or structural analysis alone. In conclusion, structure is essential to functional networks and ultimately, recovery of functional movements after stroke

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 \u3c 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

Large-Scale Network Analysis of Whole-Brain Resting-State Functional Connectivity in Spinal Cord Injury: A Comparative Study

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

Table_1_Dilation of the superior sagittal sinus detected in rat model of mild traumatic brain injury using 1 T magnetic resonance imaging.pdf

IntroductionMild 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 (MethodsIn 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).ResultsVoxel-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.DiscussionVasodilation 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.</p

Overview of Data Process.

<p>Top to bottom: Data acquisition, preprocessing to obtain individual sensorimotor network maps, and statistical analysis for group-level comparisons. SCI, spinal cord injury; con, control; SPGR, spoiled gradient-recalled echo sequence; fmri, functional magnetic resonance imaging; rsFMRI, resting state functional magnetic resonance imaging; WM, white matter; CSF, cerebrospinal fluid; SMN, sensory motor network.</p