Genetic and Neuroanatomical Support for Functional Brain Network Dynamics in Epilepsy

Focal epilepsy is a devastating neurological disorder that affects an overwhelming number of patients worldwide, many of whom prove resistant to medication. The efficacy of current innovative technologies for the treatment of these patients has been stalled by the lack of accurate and effective methods to fuse multimodal neuroimaging data to map anatomical targets driving seizure dynamics. Here we propose a parsimonious model that explains how large-scale anatomical networks and shared genetic constraints shape inter-regional communication in focal epilepsy. In extensive ECoG recordings acquired from a group of patients with medically refractory focal-onset epilepsy, we find that ictal and preictal functional brain network dynamics can be accurately predicted from features of brain anatomy and geometry, patterns of white matter connectivity, and constraints complicit in patterns of gene coexpression, all of which are conserved across healthy adult populations. Moreover, we uncover evidence that markers of non-conserved architecture, potentially driven by idiosyncratic pathology of single subjects, are most prevalent in high frequency ictal dynamics and low frequency preictal dynamics. Finally, we find that ictal dynamics are better predicted by white matter features and more poorly predicted by geometry and genetic constraints than preictal dynamics, suggesting that the functional brain network dynamics manifest in seizures rely on - and may directly propagate along - underlying white matter structure that is largely conserved across humans. Broadly, our work offers insights into the generic architectural principles of the human brain that impact seizure dynamics, and could be extended to further our understanding, models, and predictions of subject-level pathology and response to intervention

Mutator/Hypermutable Fetal/Juvenile Metakaryotic Stem Cells and Human Colorectal Carcinogenesis

Adult age-specific colorectal cancer incidence rates increase exponentially from maturity, reach a maximum, then decline in extreme old age. Armitage and Doll (1) postulated that the exponential increase resulted from “n” mutations occurring throughout adult life in normal “cells at risk” that initiated the growth of a preneoplastic colony in which subsequent “m” mutations promoted one of the preneoplastic “cells at risk” to form a lethal neoplasia. We have reported cytologic evidence that these “cells at risk” are fetal/juvenile organogenic, then preneoplastic metakaryotic stem cells. Metakaryotic cells display stem-like behaviors of both symmetric and asymmetric nuclear divisions and peculiarities such as bell shaped nuclei and amitotic nuclear fission that distinguish them from embryonic, eukaryotic stem cells. Analyses of mutant colony sizes and numbers in adult lung epithelia supported the inferences that the metakaryotic organogenic stem cells are constitutively mutator/hypermutable and that their contributions to cancer initiation are limited to the fetal/juvenile period. We have amended the two-stage model of Armitage and Doll and incorporated these several inferences in a computer program CancerFit v.5.0. We compared the expectations of the amended model to adult (15–104 years) age-specific colon cancer rates for European-American males born 1890–99 and observed remarkable concordance. When estimates of normal colonic fetal/juvenile APC and OAT gene mutation rates (∼2–5 × 10[superscript −5] per stem cell doubling) and preneoplastic colonic gene loss rates (∼8 × 10[superscript −3]) were applied, the model was in accordance only for the values of n = 2 and m = 4 or 5.United Therapeutics Corporatio

Fast simulation of E1, B1 and SAR for 7T Magnetic Resonance Imaging with the use of graphical processors

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 71-73).Specific Absorption Rate (SAR) is a dominant constraint in high field MR, and has been a topic of much recent interest with developments of parallel transmission systems (pTx). While real-time estimates of local SAR over large volumes as well as SAR-constrained pTx RF design are highly desirable goals, it is both difficult to control and computationally demanding. Steady advances in graphics cards for game developers have enabled dramatic speedups in computationally heavy tasks for computer graphics, and some of this functionality is applicable for faster numerical SAR simulation compared to general CPUs. In this study, we present the use of Compute Unified Device Architecture (CUDA) enabled graphics cards in Finite Difference Time Domain (FDTD) simulations for SAR computation. We show that using this framework can speed up computation by at least an order of magnitude compared to regular CPU computation. This will allow us to estimate SAR, B1, and E1 fields quickly for instances where SAR estimation for parallel transmission imaging of individual subjects is necessary, or for optimizing coil designs based on these estimates. A fast FDTD computation would also significantly speed up iterative optimizations of coil design over a geometric parameter space. A description is provided of how FDTD with Uniaxial Perfect Matching Layer (UPML) boundary conditions was coded on GPUs using the NVIDIA CUDA framework. FDTD equations were CUDA optimized by use of two kernel functions, one for the E field update equations and another for the B field update equations. FDTD simulations were compared to an analytical validation case of a dielectric sphere under a current loop. In addition, a description is provided of how SAR computation was parallelized for the CUDA framework.by Lohith Ganesh Kini.M.Eng

Computational Models Of Resection In Drug-Resistant Epilepsy

Epilepsy is the second most common neurological disorder worldwide, and severely limits the quality of life of those who suffer from it. Patients who do not respond to standard anti-epileptic medications often require surgery and resection of large volumes of brain tissue. This standard surgical therapy, which has remained largely unchanged in the past 30 years, is based on epileptologists identifying regions of seizure activity visually on intracranial electrophysiology recordings. Despite extensive attempts and research to improve surgical efficacy, a significant number of patients still fail to achieve complete post-surgical seizure freedom and suffer significant neuropsychological deficits. With the advent of newer neurostimulation technologies and focal laser-based ablation, minimally invasive therapies are possible, but the challenge is where to apply them. There is great promise, however, in new techniques to identify optimal resection targets and merge them with quantitative multi-modal neuroimaging and network dynamics derived from electrophysiology data. In this thesis, I present a novel approach to combining multimodal neuroimaging, expertly annotated clinical neurophysiology, and quantitative imaging and network measures to inform clinical decision making in an integrated fashion. I find that network-based dynamics hold promise for identifying focal and disparate targets in the epileptic network that respond well to localized therapies. I also find that translating these findings from bench to bedside requires large-sample clinical trials that span multiple institutions, multiple imaging modalities, and clinical expertise. Open-source data sharing platforms, such as the tools I have created as part of the following work, will enable these big data studies to take place. My hope is that the work presented in this thesis will herald a new approach to epilepsy interventions, such as highly targeted devices or ablation to minimize side effects, and help patients who suffer from this destructive illness

Computational analysis in epilepsy neuroimaging: A survey of features and methods

Epilepsy affects 65 million people worldwide, a third of whom have seizures that are resistant to anti-epileptic medications. Some of these patients may be amenable to surgical therapy or treatment with implantable devices, but this usually requires delineation of discrete structural or functional lesion(s), which is challenging in a large percentage of these patients. Advances in neuroimaging and machine learning allow semi-automated detection of malformations of cortical development (MCDs), a common cause of drug resistant epilepsy. A frequently asked question in the field is what techniques currently exist to assist radiologists in identifying these lesions, especially subtle forms of MCDs such as focal cortical dysplasia (FCD) Type I and low grade glial tumors. Below we introduce some of the common lesions encountered in patients with epilepsy and the common imaging findings that radiologists look for in these patients. We then review and discuss the computational techniques introduced over the past 10 years for quantifying and automatically detecting these imaging findings. Due to large variations in the accuracy and implementation of these studies, specific techniques are traditionally used at individual centers, often guided by local expertise, as well as selection bias introduced by the varying prevalence of specific patient populations in different epilepsy centers. We discuss the need for a multi-institutional study that combines features from different imaging modalities as well as computational techniques to definitively assess the utility of specific automated approaches to epilepsy imaging. We conclude that sharing and comparing these different computational techniques through a common data platform provides an opportunity to rigorously test and compare the accuracy of these tools across different patient populations and geographical locations. We propose that these kinds of tools, quantitative imaging analysis methods and open data platforms for aggregating and sharing data and algorithms, can play a vital role in reducing the cost of care, the risks of invasive treatments, and improve overall outcomes for patients with epilepsy

Genetic and neuroanatomical support for functional brain network dynamics in epilepsy

Focal epilepsy is a devastating neurological disorder that affects an overwhelming number of patients worldwide, many of whom prove resistant to medication. The efficacy of current innovative technologies for the treatment of these patients has been stalled by the lack of accurate and effective methods to fuse multimodal neuroimaging data to map anatomical targets driving seizure dynamics. Here we propose a parsimonious model that explains how large-scale anatomical networks and shared genetic constraints shape inter-regional communication in focal epilepsy. In extensive ECoG recordings acquired from a group of patients with medically refractory focal-onset epilepsy, we find that ictal and preictal functional brain network dynamics can be accurately predicted from features of brain anatomy and geometry, patterns of white matter connectivity, and constraints complicit in patterns of gene coexpression, all of which are conserved across healthy adult populations. Moreover, we uncover evidence that markers of non-conserved architecture, potentially driven by idiosyncratic pathology of single subjects, are most prevalent in high frequency ictal dynamics and low frequency preictal dynamics. Finally, we find that ictal dynamics are better predicted by white matter features and more poorly predicted by geometry and genetic constraints than preictal dynamics, suggesting that the functional brain network dynamics manifest in seizures rely on - and may directly propagate along - underlying white matter structure that is largely conserved across humans. Broadly, our work offers insights into the generic architectural principles of the human brain that impact seizure dynamics, and could be extended to further our understanding, models, and predictions of subject-level pathology and response to intervention

Clinical validation of automated hippocampal segmentation in temporal lobe epilepsy

Objective: To provide a multi-atlas framework for automated hippocampus segmentation in temporal lobe epilepsy (TLE) and clinically validate the results with respect to surgical lateralization and post-surgical outcome. Methods: We retrospectively identified 47 TLE patients who underwent surgical resection and 12 healthy controls. T1-weighted 3 T MRI scans were acquired for all subjects, and patients were identified by a neuroradiologist with regards to lateralization and degree of hippocampal sclerosis (HS). Automated segmentation was implemented through the Joint Label Fusion/Corrective Learning (JLF/CL) method. Gold standard lateralization was determined from the surgically resected side in Engel I (seizure-free) patients at the two-year timepoint. ROC curves were used to identify appropriate thresholds for hippocampal asymmetry ratios, which were then used to analyze JLF/CL lateralization. Results: The optimal template atlas based on subject images with varying appearances, from normal-appearing to severe HS, was demonstrated to be composed entirely of normal-appearing subjects, with good agreement between automated and manual segmentations. In applying this atlas to 26 surgically resected seizure-free patients at a two-year timepoint, JLF/CL lateralized seizure onset 92% of the time. In comparison, neuroradiology reads lateralized 65% of patients, but correctly lateralized seizure onset in these patients 100% of the time. When compared to lateralized neuroradiology reads, JLF/CL was in agreement and correctly lateralized all 17 patients. When compared to nonlateralized radiology reads, JLF/CL correctly lateralized 78% of the nine patients. Significance: While a neuroradiologist's interpretation of MR imaging is a key, albeit imperfect, diagnostic tool for seizure localization in medically-refractory TLE patients, automated hippocampal segmentation may provide more efficient and accurate epileptic foci localization. These promising findings demonstrate the clinical utility of automated segmentation in the TLE MR imaging pipeline prior to surgical resection, and suggest that further investigation into JLF/CL-assisted MRI reading could improve clinical outcomes. Our JLF/CL software is publicly available at https://www.nitrc.org/projects/ashs/. Keywords: TLE, Segmentation, Automated, Hippocampu

Remote effects of temporal lobe epilepsy surgery: Long‐term morphological changes after surgical resection

Abstract Objective Epilepsy surgery is an effective treatment for drug‐resistant patients. However, how different surgical approaches affect long‐term brain structure remains poorly characterized. Here, we present a semiautomated method for quantifying structural changes after epilepsy surgery and compare the remote structural effects of two approaches, anterior temporal lobectomy (ATL), and selective amygdalohippocampectomy (SAH). Methods We studied 36 temporal lobe epilepsy patients who underwent resective surgery (ATL = 22, SAH = 14). All patients received same‐scanner MR imaging preoperatively and postoperatively (mean 2 years). To analyze postoperative structural changes, we segmented the resection zone and modified the Advanced Normalization Tools (ANTs) longitudinal cortical pipeline to account for resections. We compared global and regional annualized cortical thinning between surgical treatments. Results Across procedures, there was significant cortical thinning in the ipsilateral insula, fusiform, pericalcarine, and several temporal lobe regions outside the resection zone as well as the contralateral hippocampus. Additionally, increased postoperative cortical thickness was seen in the supramarginal gyrus. Patients treated with ATL exhibited greater annualized cortical thinning compared with SAH cases (ATL: −0.08 ± 0.11 mm per year, SAH: −0.01 ± 0.02 mm per year, t = 2.99, P = 0.006). There were focal postoperative differences between the two treatment groups in the ipsilateral insula (P = 0.039, corrected). Annualized cortical thinning rates correlated with preoperative cortical thickness (r = 0.60, P < 0.001) and had weaker associations with age at surgery (r = −0.33, P = 0.051) and disease duration (r = −0.42, P = 0.058). Significance Our evidence suggests that selective procedures are associated with less cortical thinning and that earlier surgical intervention may reduce long‐term impacts on brain structure