scholarly journals Validating Patient-Specific Finite Element Models of Direct Electrocortical Stimulation

2021 ◽  
Vol 15 ◽  
Author(s):  
Chantel M. Charlebois ◽  
David J. Caldwell ◽  
Sumientra M. Rampersad ◽  
Andrew P. Janson ◽  
Jeffrey G. Ojemann ◽  
...  
Keyword(s):  
Computational Models ◽  
Projection Methods ◽  
Patient Specific ◽  
Clinical Effects ◽  
Cortical Surface ◽  
Brain Shift ◽  
Model Errors ◽  
Computer Interfaces ◽  
Electrode Localization

Direct electrocortical stimulation (DECS) with electrocorticography electrodes is an established therapy for epilepsy and an emerging application for stroke rehabilitation and brain-computer interfaces. However, the electrophysiological mechanisms that result in a therapeutic effect remain unclear. Patient-specific computational models are promising tools to predict the voltages in the brain and better understand the neural and clinical response to DECS, but the accuracy of such models has not been directly validated in humans. A key hurdle to modeling DECS is accurately locating the electrodes on the cortical surface due to brain shift after electrode implantation. Despite the inherent uncertainty introduced by brain shift, the effects of electrode localization parameters have not been investigated. The goal of this study was to validate patient-specific computational models of DECS against in vivo voltage recordings obtained during DECS and quantify the effects of electrode localization parameters on simulated voltages on the cortical surface. We measured intracranial voltages in six epilepsy patients during DECS and investigated the following electrode localization parameters: principal axis, Hermes, and Dykstra electrode projection methods combined with 0, 1, and 2 mm of cerebral spinal fluid (CSF) below the electrodes. Greater CSF depth between the electrode and cortical surface increased model errors and decreased predicted voltage accuracy. The electrode localization parameters that best estimated the recorded voltages across six patients with varying amounts of brain shift were the Hermes projection method and a CSF depth of 0 mm (r = 0.92 and linear regression slope = 1.21). These results are the first to quantify the effects of electrode localization parameters with in vivo intracranial recordings and may serve as the basis for future studies investigating the neuronal and clinical effects of DECS for epilepsy, stroke, and other emerging closed-loop applications.

Impact of Patient-Specific In Vivo Vessel Material Properties on Carotid Atherosclerotic Plaque Stress/Strain Calculations

2019 ◽  
Vol 16 (03) ◽  
pp. 1842002 ◽  
Author(s):  
Qingyu Wang ◽  
Dalin Tang ◽  
Gador Canton ◽  
Thomas S. Hatsukami ◽  
Kristen L. Billiar ◽  
...  
Keyword(s):  
Material Properties ◽  
Carotid Plaque ◽  
Computational Models ◽  
Patient Specific ◽  
Stress And Strain ◽  
Carotid Atherosclerotic Plaque ◽  
Plaque Vulnerability ◽  
Material Models ◽  
Vessel Material

Patient-specific vessel material properties are in general lacking in image-based computational models. Carotid plaque stress and strain conditions with in vivo material and old material models were investigated (8 patients, 16 plaques). Plaque models using patient-specific in vivo vessel material properties showed significant differences from models using old material properties from the literature on stress and strain calculations. These differences demonstrated that models using in vivo material properties could improve the accuracy of stress and strain calculations which could potentially lead to more accurate plaque vulnerability assessment.

scholarly journals Evaluation of a modified Cheatham-Platinum stent for the treatment of aortic coarctation by finite element modelling

2018 ◽  
Vol 7 ◽  
pp. 204800401877395 ◽  
Author(s):  
Barbara EU Burkhardt ◽  
Nicholas Byrne ◽  
Marí Nieves Velasco Forte ◽  
Francesco Iannaccone ◽  
Matthieu De Beule ◽  
...  
Keyword(s):  
Finite Element ◽  
Aortic Coarctation ◽  
Computational Models ◽  
Three Dimensional ◽  
Computational Modelling ◽  
Patient Specific ◽  
Stent Design ◽  
Initial Length ◽  
Element Analysis

Objectives Stent implantation for the treatment of aortic coarctation has become a standard approach for the management of older children and adults. Criteria for optimal stent design and construction remain undefined. This study used computational modelling to compare the performance of two generations of the Cheatham-Platinum stent (NuMED, Hopkinton, NY, USA) deployed in aortic coarctation using finite element analysis. Design Three-dimensional models of both stents, reverse engineered from microCT scans, were implanted in the aortic model of one representative patient. They were virtually expanded in the vessel with a 16 mm balloon and a pressure of 2 atm. Results The conventional stent foreshortened to 96.5% of its initial length, whereas the new stent to 99.2% of its initial length. Diameters in 15 slices across the conventional stent were 11.6–15 mm (median 14.2 mm) and slightly higher across the new stent: 10.7–15.3 mm (median 14.5 mm) (p= 0.021). Apposition to the vessel wall was similar: conventional stent 31.1% and new stent 28.6% of total stent area. Conclusions The new design Cheatham-Platinum stent showed similar deployment results compared to the conventional design. The new stent design showed slightly higher expansion, using the same delivery balloon. Patient-specific computational models can be used for virtual implantation of new aortic stents and promise to inform subsequent in vivo trials.

scholarly journals Effects of Fibrosis Morphology on Reentrant Ventricular Tachycardia Inducibility and Simulation Fidelity in Patient-Derived Models

2014 ◽  
Vol 8s1 ◽  
pp. CMC.S15712 ◽  
Author(s):  
Jordan Ringenberg ◽  
Makarand Deo ◽  
David Filgueiras-Rama ◽  
Gonzalo Pizarro ◽  
Borja Ibañez ◽  
...  
Keyword(s):  
Ventricular Tachycardia ◽  
Computational Models ◽  
Border Zone ◽  
Patient Specific ◽  
Narrow Channels ◽  
Simulation Fidelity ◽  
Strong Indicator ◽  
Significant Step ◽  
Magnetic Resonance Imaging Mri

Myocardial fibrosis detected via delayed-enhanced magnetic resonance imaging (MRI) has been shown to be a strong indicator for ventricular tachycardia (VT) inducibility. However, little is known regarding how inducibility is affected by the details of the fibrosis extent, morphology, and border zone configuration. The objective of this article is to systematically study the arrhythmogenic effects of fibrosis geometry and extent, specifically on VT inducibility and maintenance. We present a set of methods for constructing patient-specific computational models of human ventricles using in vivo MRI data for patients suffering from hypertension, hypercholesterolemia, and chronic myocardial infarction. Additional synthesized models with morphologically varied extents of fibrosis and gray zone (GZ) distribution were derived to study the alterations in the arrhythmia induction and reentry patterns. Detailed electrophysiological simulations demonstrated that (1) VT morphology was highly dependent on the extent of fibrosis, which acts as a structural substrate, (2) reentry tended to be anchored to the fibrosis edges and showed transmural conduction of activations through narrow channels formed within fibrosis, and (3) increasing the extent of GZ within fibrosis tended to destabilize the structural reentry sites and aggravate the VT as compared to fibrotic regions of the same size and shape but with lower or no GZ. The approach and findings represent a significant step toward patient-specific cardiac modeling as a reliable tool for VT prediction and management of the patient. Sensitivities to approximation nuances in the modeling of structural pathology by image-based reconstruction techniques are also implicated.

Determination of Human Carotid Atherosclerotic Plaque Material Properties Non-Invasively Using In Vivo Cine and 3D Magnetic Resonance Imaging and Image-Based Modeling Techniques

2011 ◽  
Author(s):  
Haofei Liu ◽  
Gador Canton ◽  
Chun Yuan ◽  
Marina Ferguson ◽  
Chun Yang ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Atherosclerotic Plaque ◽  
Material Properties ◽  
Computational Models ◽  
Plaque Rupture ◽  
Patient Specific ◽  
Material Strength ◽  
Modeling Techniques ◽  
Human Carotid

Atherosclerotic plaque rupture is believed to be associated with high critical stress exceeding plaque cap material strength. In vivo magnetic resonance image (MRI)-based computational models have been introduced to calculate critical plaque stress and assess plaque vulnerability [1–5]. However, accuracy of computational stress predictions is heavily dependent on the data used by the models. Patient-specific plaque material properties are desirable for accurate stress predictions but are not currently available. In this paper, non-invasive in vivo Cine and 3D multicontrast MRI data and modeling techniques were combined to obtain patient-specific plaque material properties to improve model prediction accuracies. A 2D human carotid plaque model was used to demonstrate impact of material stiffness on computational stress predictions.

Validation of Computational Knee Models Using a Dynamic Knee Simulator

2008 ◽  
Author(s):  
Trent M. Guess ◽  
Mohammad Kia ◽  
Katherine Weimer ◽  
Kevin Dodd ◽  
Lorin Maletsky
Keyword(s):  
Computational Models ◽  
Patient Specific ◽  
Specific Reference ◽  
Knee Biomechanics ◽  
Computational Simulations ◽  
Body Model ◽  
Knee Simulator ◽  
Multi Body ◽  
Models And Modeling

Computational models of the knee provide valuable information on knee biomechanics, but validation of these models is challenging as in-vivo parameters such as muscle forces and tissue loading cannot be measured. Machines that simulate the dynamic loading and motion of physiological activities on cadaver knees can provide a means for validating computational knee models and modeling methods. In this approach, all forces applied to cadaver knees are known and can be replicated in computational simulations. The resulting experimental and computational kinematics can then be compared. Presented here is the development and use of a modeling platform comprised of a multi-body computational model of a cadaver knee and dynamic knee simulator and experimental measurements from the cadaver knee loaded in the machine. This modeling platform has been used to study: 1) patient specific reference lengths versus literature obtained reference lengths [1], 2) inclusion of ligament and tendon wrapping [2] and, 3) the development of a multi-body model of the meniscus [3].

Automated System for Epileptic Seizures Prediction based on Multi-Channel Recordings of Electrical Brain Activity

2018 ◽  
pp. 115-122 ◽  
Author(s):  
V. A. Maksimenko ◽  
A. A. Harchenko ◽  
A. Lüttjohann
Keyword(s):  
Epileptic Seizure ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Automated System ◽  
Brain Computer Interfaces ◽  
Electrical Brain Activity ◽  
Computer Interfaces ◽  
Prediction And Prevention ◽  
The Brain

Introduction: Now the great interest in studying the brain activity based on detection of oscillatory patterns on the recorded data of electrical neuronal activity (electroencephalograms) is associated with the possibility of developing brain-computer interfaces. Braincomputer interfaces are based on the real-time detection of characteristic patterns on electroencephalograms and their transformation  into commands for controlling external devices. One of the important areas of the brain-computer interfaces application is the control of the pathological activity of the brain. This is in demand for epilepsy patients, who do not respond to drug treatment.Purpose: A technique for detecting the characteristic patterns of neural activity preceding the occurrence of epileptic seizures.Results:Using multi-channel electroencephalograms, we consider the dynamics of thalamo-cortical brain network, preceded the occurrence of an epileptic seizure. We have developed technique which allows to predict the occurrence of an epileptic seizure. The technique has been implemented in a brain-computer interface, which has been tested in-vivo on the animal model of absence epilepsy.Practical relevance:The results of our study demonstrate the possibility of epileptic seizures prediction based on multichannel electroencephalograms. The obtained results can be used in the development of neurointerfaces for the prediction and prevention of seizures of various types of epilepsy in humans. 

scholarly journals mRNA-Driven Generation of Transgene-Free Neural Stem Cells from Human Urine-Derived Cells

Cells ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1043 ◽  
Author(s):  
Phil Jun Kang ◽  
Daryeon Son ◽  
Tae Hee Ko ◽  
Wonjun Hong ◽  
Wonjin Yun ◽  
...  
Keyword(s):  
Stem Cells ◽  
Neural Stem Cells ◽  
Human Urine ◽  
Neurological Disorders ◽  
Therapeutic Potential ◽  
Embryonic Stem ◽  
Global Gene Expression ◽  
Patient Specific ◽  
Human Neural Stem Cells

Human neural stem cells (NSCs) hold enormous promise for neurological disorders, typically requiring their expandable and differentiable properties for regeneration of damaged neural tissues. Despite the therapeutic potential of induced NSCs (iNSCs), a major challenge for clinical feasibility is the presence of integrated transgenes in the host genome, contributing to the risk for undesired genotoxicity and tumorigenesis. Here, we describe the advanced transgene-free generation of iNSCs from human urine-derived cells (HUCs) by combining a cocktail of defined small molecules with self-replicable mRNA delivery. The established iNSCs were completely transgene-free in their cytosol and genome and further resembled human embryonic stem cell-derived NSCs in the morphology, biological characteristics, global gene expression, and potential to differentiate into functional neurons, astrocytes, and oligodendrocytes. Moreover, iNSC colonies were observed within eight days under optimized conditions, and no teratomas formed in vivo, implying the absence of pluripotent cells. This study proposes an approach to generate transplantable iNSCs that can be broadly applied for neurological disorders in a safe, efficient, and patient-specific manner.

scholarly journals Cardiovascular disease and osteoporosis: is there a link between lipids and bone?

2002 ◽  
Vol 39 (3) ◽  
pp. 203-210 ◽  
Author(s):  
John R Burnett ◽  
Samuel D Vasikaran
Keyword(s):  
Cardiovascular Disease ◽  
Bone Density ◽  
Vascular Calcification ◽  
Cholesterol Biosynthesis ◽  
Hormone Replacement ◽  
Plasma Lipid ◽  
Clinical Effects ◽  
Cardiovascular Morbidity And Mortality ◽  
The Relationship

Atherosclerotic heart disease and osteoporosis are both diseases of old age. Evidence is accumulating for a link between vascular and bone disease. Calcification is a common feature of atherosclerotic plaques, and osteoporosis is associated with both atherosclerosis and vascular calcification. However, the relationship of vascular calcification to the pathogenesis of atherosclerosis remains incompletely understood. Hormone replacement therapy has beneficial effects in the prevention of both atherosclerosis and osteoporosis. Bisphosphonates inhibit bone resorption and are used in the treatment of osteoporosis, whereas the statins inhibit cholesterol biosynthesis and are used for the treatment of atherosclerosis. We have reviewed recent advances in the knowledge of the actions of bisphosphonates and statins at the cellular, molecular and end-organ levels in order to examine the relationship between cardiovascular disease and osteoporosis and to explore the link between lipids and bones. These studies suggest that the mechanism of actions of these two classes of drugs at the cellular level may not be mutually exclusive. There are some early clinical data to complement these findings, suggesting that statins increase bone density and bisphosphonates may have a beneficial effect in vivo on plasma lipid levels and on the atherosclerotic process. Properly designed prospective studies that examine the effect of statins on bone density and fractures, as well as the effects of bisphosphonates on lipid profiles, atherosclerotic progression and cardiovascular morbidity and mortality are needed to define clearly the clinical effects and potential new roles for these agents.

scholarly journals A Simple Method to Avoid Brain Shift during Neuronavigation: Technical Note

2020 ◽  
Author(s):  
Jair Leopoldo Raso
Keyword(s):  
Dura Mater ◽  
Brain Area ◽  
Conventional Technique ◽  
Technical Note ◽  
Cortical Surface ◽  
Brain Shift ◽  
Simple Method ◽  
Cortex Area ◽  
Neuronavigation System ◽  
The Brain

Abstract Introduction The precise identification of anatomical structures and lesions in the brain is the main objective of neuronavigation systems. Brain shift, displacement of the brain after opening the cisterns and draining cerebrospinal fluid, is one of the limitations of such systems. Objective To describe a simple method to avoid brain shift in craniotomies for subcortical lesions. Method We used the surgical technique hereby described in five patients with subcortical neoplasms. We performed the neuronavigation-guided craniotomies with the conventional technique. After opening the dura and exposing the cortical surface, we placed two or three arachnoid anchoring sutures to the dura mater, close to the edges of the exposed cortical surface. We placed these anchoring sutures under microscopy, using a 6–0 mononylon wire. With this technique, the cortex surface was kept close to the dura mater, minimizing its displacement during the approach to the subcortical lesion. In these five cases we operated, the cortical surface remained close to the dura, anchored by the arachnoid sutures. All the lesions were located with a good correlation between the handpiece tip inserted in the desired brain area and the display on the navigation system. Conclusion Arachnoid anchoring sutures to the dura mater on the edges of the cortex area exposed by craniotomy constitute a simple method to minimize brain displacement (brain-shift) in craniotomies for subcortical injuries, optimizing the use of the neuronavigation system.

scholarly journals A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2111
Author(s):  
Bo-Wei Zhao ◽  
Zhu-Hong You ◽  
Lun Hu ◽  
Zhen-Hao Guo ◽  
Lei Wang ◽  
...  
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Computational Models ◽  
Structural Information ◽  
Characteristic Curve ◽  
Representation Learning ◽  
Graph Representation ◽  
Convolutional Network ◽  
Novel Method

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

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