Intraoperative physiology augments atlas-based data in awake deep brain stimulation.

BACKGROUND: Deep brain stimulation (DBS) is commonly performed with patients awake to perform intraoperative microelectrode recordings and/or macrostimulation testing to guide final electrode placement. Supplemental information from atlas-based databases derived from prior patient data and visualised as efficacy heat maps transformed and overlaid onto preoperative MRIs can be used to guide preoperative target planning and intraoperative final positioning. Our quantitative analysis of intraoperative testing and corresponding changes made to final electrode positioning aims to highlight the value of intraoperative neurophysiological testing paired with image-based data to optimise final electrode positioning in a large patient cohort. METHODS: Data from 451 patients with movement disorders treated with 822 individual DBS leads at a single institution from 2011 to 2021 were included. Atlas-based data was used to guide surgical targeting. Intraoperative testing data and coordinate data were retrospectively obtained from a large patient database. Medical records were reviewed to obtain active contact usage and neurologist-defined outcomes at 1 year. RESULTS: Microelectrode recording firing profiles differ per track, per target and inform the locations where macrostimulation testing is performed. Macrostimulation performance correlates with the final electrode track chosen. Centroids of atlas-based efficacy heat maps per target were close in proximity to and may predict active contact usage at 1 year. Overall, patient outcomes at 1 year were improved for patients with better macrostimulation response. CONCLUSIONS: Atlas-based imaging data is beneficial for target planning and intraoperative guidance, and in conjunction with intraoperative neurophysiological testing during awake DBS can be used to individualize and optimise final electrode positioning, resulting in favourable outcomes.

Abnormal functional connectivity of the posterior hypothalamus and other arousal regions in surgical temporal lobe epilepsy.

OBJECTIVE: This study sought to characterize resting-state functional MRI (fMRI) connectivity patterns of the posterior hypothalamus (pHTH) and the nucleus basalis of Meynert (NBM) in surgical patients with mesial temporal lobe epilepsy (mTLE), and to investigate potential correlations between functional connectivity of these arousal regions and neurocognitive performance. METHODS: The study evaluated resting-state fMRI in 60 patients with preoperative mTLE and in 95 healthy controls. The authors first conducted voxel-wise connectivity analyses seeded from the pHTH, combined anterior and tuberal hypothalamus (atHTH; i.e., the rest of the hypothalamus), and the NBM ipsilateral (ipsiNBM) and contralateral (contraNBM) to the epileptogenic zone. Based on these results, the authors included the pHTH, ipsiNBM, and frontoparietal neocortex in a network-based statistic (NBS) analysis to elucidate a network that best distinguishes patients from controls. The connections involving the pHTH and ipsiNBM from this network were included in age-corrected pairwise region of interest (ROI) analysis, along with connections between arousal structures, including the pHTH, ipsiNBM, and brainstem arousal regions. Finally, patient functional connectivity was correlated with clinical neurocognitive testing scores for IQ as well as attention and concentration tests. RESULTS: The voxel-wise analysis demonstrated that the pHTH, when compared with the atHTH, showed more widespread functional connectivity decreases in surgical mTLE patients when compared with controls. It was also observed that the ipsiNBM, but not the contraNBM, showed decreased functional connectivity in mTLE. The NBS analysis uncovered a perturbed network of frontoparietal regions, the pHTH, and ipsiNBM that distinguishes patients from controls. Age-corrected ROI analysis revealed functional connectivity decreases between the pHTH and bilateral superior frontal gyri, medial orbitofrontal cortices, rostral anterior cingulate cortices, and inferior parietal cortices in mTLE when compared with controls. For the ipsiNBM, there was reduced connectivity with bilateral medial orbitofrontal and rostral anterior cingulate cortices. Age-corrected ROI analysis also demonstrated upstream connectivity decreases from controls between the pHTH and the brainstem arousal regions, cuneiform/subcuneiform (CSC) nuclei, and ventral tegmental area, as well as the ipsiNBM and CSC nuclei. Reduced functional connectivity was also detected between the pHTH and ipsiNBM. Lastly, neurocognitive test scores for attention and concentration were found to be positively correlated with the functional connectivity between the pHTH and ipsiNBM, suggesting worse performance associated with connectivity perturbations. CONCLUSIONS: This study demonstrated perturbed resting-state functional connectivity of arousal regions in surgical mTLE and is one of the first investigations to demonstrate decreased functional connectivity of the pHTH with frontoparietal regions and other arousal regions. Connectivity disturbances in arousal regions may contribute to neurocognitive deficits in surgical mTLE patients.

Localizing seizure onset zones in surgical epilepsy with neurostimulation deep learning.

OBJECTIVE: In drug-resistant temporal lobe epilepsy, automated tools for seizure onset zone (SOZ) localization that use brief interictal recordings could supplement presurgical evaluations and improve care. Thus, the authors sought to localize SOZs by training a multichannel convolutional neural network on stereoelectroencephalography (SEEG) cortico-cortical evoked potentials. METHODS: The authors performed single-pulse electrical stimulation in 10 drug-resistant temporal lobe epilepsy patients implanted with SEEG. Using 500,000 unique poststimulation SEEG epochs, the authors trained a multichannel 1-dimensional convolutional neural network to determine whether an SOZ had been stimulated. RESULTS: SOZs were classified with mean sensitivity of 78.1% and specificity of 74.6% according to leave-one-patient-out testing. To achieve maximum accuracy, the model required a 0- to 350-msec poststimulation time period. Post hoc analysis revealed that the model accurately classified unilateral versus bilateral mesial temporal lobe seizure onset, as well as neocortical SOZs. CONCLUSIONS: This was the first demonstration, to the authors' knowledge, that a deep learning framework can be used to accurately classify SOZs with single-pulse electrical stimulation-evoked responses. These findings suggest that accurate classification of SOZs relies on a complex temporal evolution of evoked responses within 350 msec of stimulation. Validation in a larger data set could provide a practical clinical tool for the presurgical evaluation of drug-resistant epilepsy.

Arousal and salience network connectivity alterations in surgical temporal lobe epilepsy.

OBJECTIVE: It is poorly understood why patients with mesial temporal lobe epilepsy (TLE) have cognitive deficits and brain network changes that extend beyond the temporal lobe, including altered extratemporal intrinsic connectivity networks (ICNs). However, subcortical arousal structures project broadly to the neocortex, are affected by TLE, and thus may contribute to these widespread network effects. The authors' objective was to examine functional connectivity (FC) patterns between subcortical arousal structures and neocortical ICNs, possible neurocognitive relationships, and FC changes after epilepsy surgery. METHODS: The authors obtained resting-state functional magnetic resonance imaging (fMRI) in 50 adults with TLE and 50 controls. They compared nondirected FC (correlation) and directed FC (Granger causality laterality index) within the salience network, default mode network, and central executive network, as well as between subcortical arousal structures; these 3 ICNs were also compared between patients and controls. They also used an fMRI-based vigilance index to relate alertness to arousal center FC. Finally, fMRI was repeated in 29 patients > 12 months after temporal lobe resection. RESULTS: Nondirected FC within the salience (p = 0.042) and default mode (p = 0.0008) networks, but not the central executive network (p = 0.79), was decreased in patients in comparison with controls (t-tests, corrected). Nondirected FC between the salience network and subcortical arousal structures (nucleus basalis of Meynert, thalamic centromedian nucleus, and brainstem pedunculopontine nucleus) was reduced in patients in comparison with controls (p = 0.0028-0.015, t-tests, corrected), and some of these connectivity abnormalities were associated with lower processing speed index, verbal comprehension, and full-scale IQ. Interestingly, directed connectivity measures suggested a loss of top-down influence from the salience network to the arousal nuclei in patients. After resection, certain FC patterns between the arousal nuclei and salience network moved toward control values in the patients, suggesting that some postoperative recovery may be possible. Although an fMRI-based vigilance measure suggested that patients exhibited reduced alertness over time, FC abnormalities between the salience network and arousal structures were not influenced by the alertness levels during the scans. CONCLUSIONS: FC abnormalities between subcortical arousal structures and ICNs, such as the salience network, may be related to certain neurocognitive deficits in TLE patients. Although TLE patients demonstrated vigilance abnormalities, baseline FC perturbations between the arousal and salience networks are unlikely to be driven solely by alertness level, and some may improve after surgery. Examination of the arousal network and ICN disturbances may improve our understanding of the downstream clinical effects of TLE.

Functional connectivity between mesial temporal and default mode structures may help lateralize surgical temporal lobe epilepsy.

OBJECTIVE: The most common surgically treatable epilepsy syndrome is mesial temporal lobe epilepsy (mTLE). Preoperative noninvasive lateralization of mTLE is challenging in part due to rapid contralateral seizure spread. Abnormal connections in both the mesial temporal lobe and resting-state networks have been described in mTLE, but it is unclear if connectivity between these networks may aid in lateralization. METHODS: In 52 patients with left mTLE (LmTLE) or right mTLE (RmTLE) and 52 matched control subjects, the authors acquired 20 minutes of resting-state functional MRI (fMRI) and evaluated functional connectivity of bilateral hippocampi and amygdalae with selected resting-state networks. They used Pearson correlation, network-based statistic, and dynamic causal modeling. Also, to evaluate the clinical utility of a resting-state connectivity model in lateralizing unilateral presurgical mTLE patients, they used receiver operating characteristic curve analysis. RESULTS: RmTLE patients demonstrated decreased nondirected connectivity between the right hippocampus and default mode network compared with LmTLE patients and control subjects. Network-based statistic analysis revealed that the network with most decreased connectivity that distinguished LmTLE from RmTLE patients included the right hippocampus and amygdala, right lateral orbitofrontal cortices, and bilateral inferior parietal lobules, precuneus, and medial orbitofrontal cortices. Dynamic causal modeling analysis revealed that cross-hemispheric connectivity between hippocampi and amygdalae was predominantly inward toward the epileptogenic side. A regression model incorporating these connectivity patterns was used to accurately lateralize mTLE patients with an area under the receiver operating characteristic curve of 0.87. CONCLUSIONS: Evaluating fMRI connectivity between mesial temporal structures and default mode network may aid in mTLE lateralization, reduce need for intracranial monitoring, and guide surgical planning.

Temporal lobe epilepsy lateralisation and surgical outcome prediction using diffusion imaging.

OBJECTIVE: We sought to augment the presurgical workup of medically refractory temporal lobe epilepsy by creating a supervised machine learning technique that uses diffusion-weighted imaging to classify patient-specific seizure onset laterality and surgical outcome. METHODS: 151 subjects were included in this analysis: 62 patients (aged 18-68 years, 36 women) and 89 healthy controls (aged 18-71 years, 47 women). We created a supervised machine learning technique that uses diffusion-weighted metrics to classify subject groups. Specifically, we sought to classify patients versus healthy controls, unilateral versus bilateral temporal lobe epilepsy, left versus right temporal lobe epilepsy and seizure-free versus not seizure-free surgical outcome. We then reduced the dimensionality of derived features with community detection for ease of interpretation. RESULTS: We classified the subject groups in withheld testing data sets with a cross-fold average testing areas under the receiver operating characteristic curve of 0.745 for patients versus healthy controls, 1.000 for unilateral versus bilateral seizure onset, 0.662 for left versus right seizure onset, 0.800 for left-sided seizure-free vsersu not seizure-free surgical outcome and 0.775 for right-sided seizure-free versus not seizure-free surgical outcome. CONCLUSIONS: This technique classifies important clinical decisions in the presurgical workup of temporal lobe epilepsy by generating discerning white-matter features. We believe that this work augments existing network connectivity findings in the field by further elucidating important white-matter pathology in temporal lobe epilepsy. We hope that this work contributes to recent efforts aimed at using diffusion imaging as an augmentation to the presurgical workup of this devastating neurological disorder.

SEEG Functional Connectivity Measures to Identify Epileptogenic Zones: Stability, Medication Influence, and Recording Condition.

BACKGROUND AND OBJECTIVES: Functional connectivity (FC) measures can be used to differentiate epileptogenic zones (EZs) from non-EZs in patients with medically refractory epilepsy. Little work has been done to evaluate the stability of stereo-EEG (SEEG) FC measures over time and their relationship with antiseizure medication (ASM) use, a critical confounder in epilepsy FC studies. We aimed to answer the following questions: Are SEEG FC measures stable over time? Are they influenced by ASMs? Are they affected by patient data collection state? METHODS: In 32 patients with medically refractory focal epilepsy, we collected a single 2-minute prospective SEEG resting-state (awake, eyes closed) data set and consecutive 2-minute retrospective pseudo-rest (awake, eyes open) data sets for days 1-7 postimplantation. ASM dosages were recorded for days 1-7 postimplantation and drug load score (DLS) per day was calculated to standardize and compare across patients. FC was evaluated using directed and nondirected measures. Standard clinical interpretation of ictal SEEG was used to classify brain regions as EZs and non-EZs. RESULTS: Over 7 days, presumed EZs consistently had higher FC than non-EZs when using between imaginary coherence (ImCoh) and partial directed coherence (PDC) inward strength, without accounting for DLS. These measures were demonstrated to be stable over a short-term period of 3 consecutive days with the same DLS. Between ImCoh FC differences between EZs and non-EZs were reduced with DLS decreases, whereas other measures were not affected by DLS. FC differences between EZs and non-EZs were seen during both resting-state and pseudo-rest conditions; ImCoh values were strongly correlated between the 2 conditions, whereas PDC values were not. DISCUSSION: Inward and nondirected SEEG FC is higher in presumed EZs vs non-EZs and measures are stable over time. However, certain measures may be affected by ASM dose, as between ImCoh differences between EZs and non-EZs are less pronounced with lower doses, and other measures such as PDC are poorly correlated across recording conditions. These findings allow novel insight into how SEEG FC measures may aid surgical localization and how they are influenced by ASMs and other factors.

Impact of brain shift on neural pathways in deep brain stimulation: a preliminary analysis via multi-physics finite element models.

Objective.The effectiveness of deep brain stimulation (DBS) depends on electrode placement accuracy, which can be compromised by brain shift during surgery. While there have been efforts in assessing the impact of electrode misplacement due to brain shift using preop- and postop-imaging data, such analysis using preop- and intraop-imaging data via biophysical modeling has not been conducted. This work presents a preliminary study that applies a multi-physics analysis framework using finite element biomechanical and bioelectric models to examine the impact of realistic intraoperative shift on neural pathways determined by tractography.Approach.The study examined six patients who had undergone interventional magnetic resonance-guided DBS surgery. The modeling framework utilized a biomechanical approach to update preoperative MR to reflect shift-induced anatomical changes. Using this anatomically deformed image and its undeformed counterpart, bioelectric effects from shifting electrode leads could be simulated and neural activation differences were approximated. Specifically, for each configuration, volume of tissue activation was computed and subsequently used for tractography estimation. Total tract volume and overlapping volume with motor regions as well as connectivity profile were compared. In addition, volumetric overlap between different fiber bundles among configurations was computed and correlated to estimated shift.Main results.The study found deformation-induced differences in tract volume, motor region overlap, and connectivity behavior, suggesting the impact of shift. There is a strong correlation (R= -0.83) between shift from intended target and intended neural pathway recruitment, where at threshold of ∼2.94 mm, intended recruitment completely degrades. The determined threshold is consistent with and provides quantitative support to prior observations and literature that deviations of 2-3 mm are detrimental.Significance.The findings support and advance prior studies and understanding to illustrate the need to account for shift in DBS and the potentiality of computational modeling for estimating influence of shift on neural activation.

Role of the Nucleus Basalis as a Key Network Node in Temporal Lobe Epilepsy.

OBJECTIVE: To determine whether the nucleus basalis of Meynert (NBM) may be a key network structure of altered functional connectivity in temporal lobe epilepsy (TLE), we examined fMRI with network-based analyses. METHODS: We acquired resting-state fMRI in 40 adults with TLE and 40 matched healthy control participants. We calculated functional connectivity of NBM and used multiple complementary network-based analyses to explore the importance of NBM in TLE networks without biasing our results by our approach. We compared patients to controls and examined associations of network properties with disease metrics and neurocognitive testing. RESULTS: We observed marked decreases in connectivity between NBM and the rest of the brain in patients with TLE (0.91 ± 0.88, mean ± SD) vs controls (1.96 ± 1.13, p < 0.001, t test). Larger decreases in connectivity between NBM and fronto-parietal-insular regions were associated with higher frequency of consciousness-impairing seizures (r = -0.41, p = 0.008, Pearson). A core network of altered nodes in TLE included NBM ipsilateral to the epileptogenic side and bilateral limbic structures. Furthermore, normal community affiliation of ipsilateral NBM was lost in patients, and this structure displayed the most altered clustering coefficient of any node examined (3.46 ± 1.17 in controls vs 2.23 ± 0.93 in patients). Abnormal connectivity between NBM and subcortical arousal community was associated with modest neurocognitive deficits. Finally, a logistic regression model incorporating connectivity properties of ipsilateral NBM successfully distinguished patients from control datasets with moderately high accuracy (78%). CONCLUSIONS: These results suggest that while NBM is rarely studied in epilepsy, it may be one of the most perturbed network nodes in TLE, contributing to widespread neural effects in this disabling disorder.

People with mesial temporal lobe epilepsy have altered thalamo-occipital brain networks.

While temporal lobe epilepsy (TLE) is a focal epilepsy, previous work demonstrates that TLE causes widespread brain-network disruptions. Impaired visuospatial attention and learning in TLE may be related to thalamic arousal nuclei connectivity. Our prior preliminary work in a smaller patient cohort suggests that patients with TLE demonstrate abnormal functional connectivity between central lateral (CL) thalamic nucleus and medial occipital lobe. Others have shown pulvinar connectivity disturbances in TLE, but it is incompletely understood how TLE affects pulvinar subnuclei. Also, the effects of epilepsy surgery on thalamic functional connectivity remains poorly understood. In this study, we examine the effects of TLE on functional connectivity of two key thalamic arousal-nuclei: lateral pulvinar (PuL) and CL. We evaluate resting-state functional connectivity of the PuL and CL in 40 patients with TLE and 40 controls using fMRI. In 25 patients, postoperative images (>1 year) were also compared with preoperative images. Compared to controls, patients with TLE exhibit loss of normal positive connectivity between PuL and lateral occipital lobe (p < 0.05), and a loss of normal negative connectivity between CL and medial occipital lobe (p < 0.01, paired t-tests). FMRI amplitude of low-frequency fluctuation (ALFF) in TLE trended higher in ipsilateral PuL (p = 0.06), but was lower in the lateral occipital (p < 0.01) and medial occipital lobe in patients versus controls (p < 0.05, paired t-tests). More abnormal ALFF in the ipsilateral lateral occipital lobe is associated with worse preoperative performance on Rey Complex Figure Test Immediate (p < 0.05, r = 0.381) and Delayed scores (p < 0.05, r = 0.413, Pearson's Correlations). After surgery, connectivity between PuL and lateral occipital lobe remains abnormal in patients (p < 0.01), but connectivity between CL and medial occipital lobe improves and is no longer different from control values (p > 0.05, ANOVA, post hoc Fischer's LSD). In conclusion, thalamic arousal nuclei exhibit abnormal connectivity with occipital lobe in TLE, and some connections may improve after surgery. Studying thalamic arousal centers may help explain distal network disturbances in TLE.

Seizure-onset regions demonstrate high inward directed connectivity during resting-state: An SEEG study in focal epilepsy.

OBJECTIVE: In patients with medically refractory focal epilepsy, stereotactic-electroencephalography (SEEG) can aid in localizing epileptogenic regions for surgical treatment. SEEG, however, requires long hospitalizations to record seizures, and ictal interpretation can be incomplete or inaccurate. Our recent work showed that non-directed resting-state analyses may identify brain regions as epileptogenic or uninvolved. Our present objective is to map epileptogenic networks in greater detail and more accurately identify seizure-onset regions using directed resting-state SEEG connectivity. METHODS: In 25 patients with focal epilepsy who underwent SEEG, 2 minutes of resting-state, artifact-free, SEEG data were selected and functional connectivity was estimated. Using standard clinical interpretation, brain regions were classified into four categories: ictogenic, early propagation, irritative, or uninvolved. Three non-directed connectivity measures (mutual information [MI] strength, and imaginary coherence between and within regions) and four directed measures (partial directed coherence [PDC] and directed transfer function [DTF], inward and outward strength) were calculated. Logistic regression was used to generate a predictive model of ictogenicity. RESULTS: Ictogenic regions had the highest and uninvolved regions had the lowest MI strength. Although both PDC and DTF inward strengths were highest in ictogenic regions, outward strengths did not differ among categories. A model incorporating directed and nondirected connectivity measures demonstrated an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.88 in predicting ictogenicity of individual regions. The AUC of this model was 0.93 when restricted to patients with favorable postsurgical seizure outcomes. SIGNIFICANCE: Directed connectivity measures may help identify epileptogenic networks without requiring ictal recordings. Greater inward but not outward connectivity in ictogenic regions at rest may represent broad inhibitory input to prevent seizure generation.

Accounting for intraoperative brain shift ascribable to cavity collapse during intracranial tumor resection.

Purpose: For many patients with intracranial tumors, accurate surgical resection is a mainstay of their treatment paradigm. During surgical resection, image guidance is used to aid in localization and resection. Intraoperative brain shift can invalidate these guidance systems. One cause of intraoperative brain shift is cavity collapse due to tumor resection, which will be referred to as "debulking." We developed an imaging-driven finite element model of debulking to create a comprehensive simulation data set to reflect possible intraoperative changes. The objective was to create a method to account for brain shift due to debulking for applications in image-guided neurosurgery. We hypothesized that accounting for tumor debulking in a deformation atlas data framework would improve brain shift predictions, which would enhance image-based surgical guidance. Approach: This was evaluated in a six-patient intracranial tumor resection intraoperative data set. The brain shift deformation atlas data framework consisted of n = 756 simulated deformations to account for effects due to gravity-induced and hyperosmotic drug-induced brain shift, which reflects previous developments. An additional complement of n = 84 deformations involving simulated tumor growth followed by debulking was created to capture observed intraoperative effects not previously included. Results: In five of six patient cases evaluated, inclusion of debulking mechanics improved brain shift correction by capturing global mass effects resulting from the resected tumor. Conclusions: These findings suggest imaging-driven brain shift models used to create a deformation simulation data framework of observed intraoperative events can be used to assist in more accurate image-guided surgical navigation in the brain.

Development and evaluation of a "trackerless" surgical planning and guidance system based on 3D Slicer.

Conventional optical tracking systems use cameras sensitive to near-infrared (NIR) light and NIR illuminated/active-illuminating markers to localize instrumentation and the patient in the operating room (OR) physical space. This technology is widely used within the neurosurgical theater and is a staple in the standard of care for craniotomy planning. To accomplish, planning is largely conducted at the time of the procedure in the OR with the patient in a fixed head orientation. We propose a framework to achieve this in the OR without conventional tracking technology, i.e., a "trackerless" approach. Briefly, we investigate an extension of the 3D Slicer which combines surgical planning and craniotomy designation. While taking advantage of the well-developed 3D Slicer platform, we implement advanced features to aid the neurosurgeon in planning the location of the anticipated craniotomy relative to the preoperatively imaged tumor in a physical-to-virtual setup, and then subsequently aid the true physical procedure by correlating that physical-to-virtual plan with an intraoperative magnetic resonance imaging-to-physical registered field-of-view display. These steps are done such that the craniotomy can be designated without the use of a conventional optical tracking technology. To test this approach, four experienced neurosurgeons performed experiments on five different surgical cases using our 3D Slicer module as well as the conventional procedure for comparison. The results suggest that our planning system provides a simple, cost-efficient, and reliable solution for surgical planning and delivery without the use of conventional tracking technologies. We hypothesize that the combination of this craniotomy planning approach and our past developments in cortical surface registration and deformation tracking using stereo-pair data from the surgical microscope may provide a fundamental realization of an integrated trackerless surgical guidance platform.

Biophysical model-based parameters to classify tumor recurrence from radiation-induced necrosis for brain metastases.

PURPOSE: Stereotactic radiosurgery (SRS) is used for local control treatment of patients with intracranial metastases. As a result of SRS, some patients develop radiation-induced necrosis. Radiographically, radiation-induced necrosis can appear similar to tumor recurrence in magnetic resonance (MR) T1 -weighted contrast-enhanced imaging, T2 -weighted MR imaging, and Fluid-Attenuated Inversion Recovery (FLAIR) MR imaging. Radiographic ambiguities often necessitate invasive brain biopsies to determine lesion etiology or cause delayed subsequent therapy initiation. We use a biomechanically coupled tumor growth model to estimate patient-specific model parameters and model-derived measures to noninvasively classify etiology of enhancing lesions in this patient population. METHODS: In this initial, preliminary retrospective study, we evaluated five patients with tumor recurrence and five with radiation-induced necrosis. Longitudinal patient-specific MR imaging data were used in conjunction with the model to parameterize tumor cell proliferation rate and tumor cell diffusion coefficient, and Dice correlation coefficients were used to quantify degree of correlation between model-estimated mechanical stress fields and edema visualized from MR imaging. RESULTS: Results found four statistically relevant parameters which can differentiate tumor recurrence and radiation-induced necrosis. CONCLUSIONS: This preliminary investigation suggests potential of this framework to noninvasively determine the etiology of enhancing lesions in patients who previously underwent SRS for intracranial metastases.

In vivo modeling of interstitial pressure in a porcine model: approximation of poroelastic properties and effects of enhanced anatomical structure modeling.

The purpose of this investigation is to test whether a poroelastic model with enhanced structure can capture in vivo interstitial pressure dynamics in a brain undergoing mock surgical loads. Using interstitial pressure data from a porcine study, we use an inverse model to reconstruct material properties in an effort to capture these in vivo brain tissue dynamics. Four distinct models for the reconstruction of parameters are investigated (full anatomical condition description, condition without dural septa description, condition without ventricle boundary description, and the conventional fully saturated model). These models are systematic in their development to isolate the influence of three model characteristics: the dural septa, the treatment of the ventricles, and the treatment of the brain as a saturated media. This study demonstrates that to capture appropriate pressure compartmentalization, interstitial pressure gradients, pressure transient effects, and deformations within the brain, the proposed boundary conditions and structural enhancement coupled with a heterogeneous description invoking partial saturation are needed in a biphasic poroelastic model. These findings suggest that with enhanced anatomical modeling and appropriate model assumptions, poroelastic models can be used to capture quite complex brain deformations and interstitial pressure dynamics.

Stereovision-based integrated system for point cloud reconstruction and simulated brain shift validation.

Intraoperative soft tissue deformation, referred to as brain shift, compromises the application of current image-guided surgery navigation systems in neurosurgery. A computational model driven by sparse data has been proposed as a cost-effective method to compensate for cortical surface and volumetric displacements. We present a mock environment developed to acquire stereoimages from a tracked operating microscope and to reconstruct three-dimensional point clouds from these images. A reconstruction error of 1 mm is estimated by using a phantom with a known geometry and independently measured deformation extent. The microscope is tracked via an attached tracking rigid body that facilitates the recording of the position of the microscope via a commercial optical tracking system as it moves during the procedure. Point clouds, reconstructed under different microscope positions, are registered into the same space to compute the feature displacements. Using our mock craniotomy device, realistic cortical deformations are generated. When comparing our tracked microscope stereo-pair measure of mock vessel displacements to that of the measurement determined by the independent optically tracked stylus marking, the displacement error was [Formula: see text] on average. These results demonstrate the practicality of using tracked stereoscopic microscope as an alternative to laser range scanners to collect sufficient intraoperative information for brain shift correction.