Eric M. Jackson and Alan R. Cohen
Jodi L. Smith, Jon Hobbs, Aonan Tang, David Jackson, Wei Chen, Hema Patel, Anita Prieto, Alexander Sher, Alan Litke and John M. Beggs
Epileptogenicity of neuronal tissues requires both altered excitability and altered synchronization of neurons. However, the network-level mechanisms responsible for neuronal hyperexcitability and synchronization remain unknown, and there is much to learn regarding how even small networks of neurons interact. The present study examines local and network properties of cortical neurons from epileptogenic human and excited (“epileptic”) rat cortex.
Epileptogenic cortex was harvested from pediatric patients with medically refractory seizures undergoing resective surgery. Local field potential signals (LFPs) were recorded continuously for up to several hours with a 60-channel microelectrode array. We also recorded LFPs from slices and organotypic and dissociated cultures of rat cortex bathed in high K+ and low Mg++. We then compared the human and rat data, applied a second-order maximum entropy model (MEM) to the data, and explored how well the MEM predicted sequences of correlated states over time.
Both human and rat cortex produced LFP signals in the form of interictal spikes on almost all electrodes. However, only human cortex demonstrated spontaneous activity in normal cerebrospinal fluid, and the LFPs from human cortex showed greater synchrony across electrodes than the rat LFPs. Moreover, when a second-order MEM was applied to human and rat data, the model accounted for roughly 88% of network correlations. However, in 8/13 preparations the observed sequences of correlated states were significantly longer than predicted by independently concatenating states from the model, suggesting that temporal dependencies are a common feature of cortical network activity.
Excited slices of rat cortex fail to capture some important features of network activity found in epileptogenic human cortex. Furthermore, a second-order MEM successfully predicts correlated states in cortical networks, but not their evolution over time. Thus, higher-order MEMs are necessary to account for temporal correlations observed between states.
Shawna Farquharson, J.-Donald Tournier, Fernando Calamante, Gavin Fabinyi, Michal Schneider-Kolsky, Graeme D. Jackson and Alan Connelly
Diffusion-based MRI tractography is an imaging tool increasingly used in neurosurgical procedures to generate 3D maps of white matter pathways as an aid to identifying safe margins of resection. The majority of white matter fiber tractography software packages currently available to clinicians rely on a fundamentally flawed framework to generate fiber orientations from diffusion-weighted data, namely diffusion tensor imaging (DTI). This work provides the first extensive and systematic exploration of the practical limitations of DTI-based tractography and investigates whether the higher-order tractography model constrained spherical deconvolution provides a reasonable solution to these problems within a clinically feasible timeframe.
Comparison of tractography methodologies in visualizing the corticospinal tracts was made using the diffusion-weighted data sets from 45 healthy controls and 10 patients undergoing presurgical imaging assessment. Tensor-based and constrained spherical deconvolution–based tractography methodologies were applied to both patients and controls.
Diffusion tensor imaging–based tractography methods (using both deterministic and probabilistic tractography algorithms) substantially underestimated the extent of tracks connecting to the sensorimotor cortex in all participants in the control group. In contrast, the constrained spherical deconvolution tractography method consistently produced the biologically expected fan-shaped configuration of tracks. In the clinical cases, in which tractography was performed to visualize the corticospinal pathways in patients with concomitant risk of neurological deficit following neurosurgical resection, the constrained spherical deconvolution–based and tensor-based tractography methodologies indicated very different apparent safe margins of resection; the constrained spherical deconvolution–based method identified corticospinal tracts extending to the entire sensorimotor cortex, while the tensor-based method only identified a narrow subset of tracts extending medially to the vertex.
This comprehensive study shows that the most widely used clinical tractography method (diffusion tensor imaging–based tractography) results in systematically unreliable and clinically misleading information. The higher-order tractography model, using the same diffusion-weighted data, clearly demonstrates fiber tracts more accurately, providing improved estimates of safety margins that may be useful in neurosurgical procedures. We therefore need to move beyond the diffusion tensor framework if we are to begin to provide neurosurgeons with biologically reliable tractography information.
Juan C. Fernandez-Miranda
Daniel Lewis, Carmine A. Donofrio, Claire O’Leary, Ka-loh Li, Xiaoping Zhu, Ricky Williams, Ibrahim Djoukhadar, Erjon Agushi, Cathal J. Hannan, Emma Stapleton, Simon K. Lloyd, Simon R. Freeman, Andrea Wadeson, Scott A. Rutherford, Charlotte Hammerbeck-Ward, D. Gareth Evans, Alan Jackson, Omar N. Pathmanaban, Federico Roncaroli, Andrew T. King and David J. Coope
Inflammation and angiogenesis may play a role in the growth of sporadic and neurofibromatosis type 2 (NF2)–related vestibular schwannoma (VS). The similarities in microvascular and inflammatory microenvironment have not been investigated. The authors sought to compare the tumor microenvironment (TME) in sporadic and NF2-related VSs using a combined imaging and tissue analysis approach.
Diffusion MRI and high-temporal-resolution dynamic contrast-enhanced (DCE) MRI data sets were prospectively acquired in 20 NF2-related and 24 size-matched sporadic VSs. Diffusion metrics (mean diffusivity, fractional anisotropy) and DCE-MRI–derived microvascular biomarkers (transfer constant [Ktrans], fractional plasma volume, tissue extravascular-extracellular space [ve], longitudinal relaxation rate, tumoral blood flow) were compared across both VS groups, and regression analysis was used to evaluate the effect of tumor size, pretreatment tumor growth rate, and tumor NF2 status (sporadic vs NF2-related) on each imaging parameter. Tissues from 17 imaged sporadic VSs and a separate cohort of 12 NF2-related VSs were examined with immunohistochemistry markers for vessels (CD31), vessel permeability (fibrinogen), and macrophage density (Iba1). The expression of vascular endothelial growth factor (VEGF) and VEGF receptor 1 was evaluated using immunohistochemistry, Western blotting, and double immunofluorescence.
Imaging data demonstrated that DCE-MRI–derived microvascular characteristics were similar in sporadic and NF2-related VSs. Ktrans (p < 0.001), ve (p ≤ 0.004), and tumoral free water content (p ≤ 0.003) increased with increasing tumor size and pretreatment tumor growth rate. Regression analysis demonstrated that with the exception of mean diffusivity (p < 0.001), NF2 status had no statistically significant effect on any of the imaging parameters or the observed relationship between the imaging parameters and tumor size (p > 0.05). Tissue analysis confirmed the imaging metrics among resected sporadic VSs and demonstrated that across all VSs studied, there was a close association between vascularity and Iba1+ macrophage density (r = 0.55, p = 0.002). VEGF was expressed by Iba1+ macrophages.
The authors present the first in vivo comparative study of microvascular and inflammatory characteristics in sporadic and NF2-related VSs. The imaging and tissue analysis results indicate that inflammation is a key contributor to TME and should be viewed as a therapeutic target in both VS groups.