Pediatric Head Models for Improved Imaging of Neurological Development

用于改善神经发育成像的儿科头部模型

基本信息

批准号：
8034215
负责人：
Sergei Turovets
金额：
$ 34.84万
依托单位：
ELECTRICAL GEODESICS, INC.
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-03-01 至 2013-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8034215
关键词：
2 year old 4 year old Address Adult Age Attention deficit hyperactivity disorder Autistic Disorder Biological Neural Networks Birth Brain Brain imaging Brain region Cerebrospinal Fluid Child Childhood Clinical Clinical Research Cognition Cognition Disorders Cognitive Computer Architectures Computer software Data Set Dependence Development Disease Electroencephalography Functional Imaging Functional Magnetic Resonance Imaging Goals Head Image Imaging Device Imaging Techniques Institutes Ionizing radiation Knowledge Life Magnetic Resonance Imaging Magnetoencephalography Manufacturer Name Measurement Measures Medical Imaging Methods Modeling Morphology Movement Neurobiology Neurologic Patients Phase Population Radiology Specialty Relative (related person)Research Personnel Resolution Scalp structure Schizophrenia Seizures Shapes Simulate Solutions Source Techniques Technology Testing Tissues Uncertainty Universities Variant Washington Work X-Ray Computed Tomography age group base brain morphology brain size brain tissue cognitive control cognitive function computing resources cranium density diffuse optical tomography early childhood electric impedance gray matter image reconstruction improved innovation member neuroimaging optical imaging phase 1 study public health relevance sensor tomography tool white matter

项目摘要

DESCRIPTION (provided by applicant): While it is well known that the brain undergoes rapid developmental changes from birth to early childhood, remarkably little is understood about the relationship between changes in brain size and composition and normal cognitive development. Yet we now know that several potentially debilitating disorders, among them the Autism Spectral Disorders, Attention Deficit Hyperactivity Disorder and Schizophrenia, are a consequence of delays or abnormalities in brain development. In children, the study of normal cognitive and brain development is best accomplished using non-invasive techniques that are not overly restrictive of movement and do not require ionizing radiation. Of available techniques, electroencephalography (EEG), particularly with the advent of high density sensor arrays, provides the ability to assess cognitive function safely and non-invasively. However, to provide functional localization of cognitively important brain regions and networks requires an accurate model of head tissue geometry and conductivity, particularly in the first years of life, when skull and brain change rapidly in composition and size. This Phase I project will create age-group head models based on measured conductivity values of skull and brain for five age groups to determine when changes in head shape, size and composition significantly impact the ability to accurately localize seizure activity. By introducing advanced computational resources for creating patient-specific head models, we will allow to optimize the use of non-invasive dense-array EEG to elucidate the developmental trajectory of neural networks underlying cognition in normal children. PUBLIC HEALTH RELEVANCE: The product innovation proposed in this project will create age-group pediatric head models for neuroimaging . By introducing advanced computational resources for creating age-specific child head models, this technology will provide clinicians and researchers with a tool for optimal use of non-invasive high density EEG in localizing seizure activities and elucidating the developmental trajectory of neural networks underlying cognition in normal children.

描述（由申请人提供）：虽然众所周知，大脑从出生到幼儿期经历快速的发育变化，但人们对大脑大小和组成的变化与正常认知发展之间的关系知之甚少。然而我们现在知道，一些潜在的使人衰弱的疾病，其中包括自闭症谱系障碍、注意力缺陷多动障碍和精神分裂症，是大脑发育迟缓或异常的结果。对于儿童来说，正常认知和大脑发育的研究最好使用非侵入性技术来完成，这种技术不会过度限制运动，也不需要电离辐射。在现有技术中，脑电图（EEG），特别是随着高密度传感器阵列的出现，提供了安全、非侵入性评估认知功能的能力。然而，为了提供对认知重要的大脑区域和网络的功能定位，需要头部组织几何形状和电导率的精确模型，特别是在生命的最初几年，此时头骨和大脑的成分和大小迅速变化。该第一阶段项目将根据测量的五个年龄组的头骨和大脑电导率值创建年龄组头部模型，以确定头部形状、大小和成分的变化何时显着影响准确定位癫痫活动的能力。通过引入先进的计算资源来创建特定于患者的头部模型，我们将优化非侵入性密集阵列脑电图的使用，以阐明正常儿童认知基础的神经网络的发育轨迹。公共健康相关性：该项目提出的产品创新将为神经影像创建不同年龄组的儿科头部模型。通过引入先进的计算资源来创建特定年龄的儿童头部模型，该技术将为临床医生和研究人员提供一种工具，以优化使用非侵入性高密度脑电图来定位癫痫活动并阐明正常认知下神经网络的发育轨迹。孩子们。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Sources of non-physiologic noise in simultaneous EEG-fMRI data: a phantom study.

同时 EEG-fMRI 数据中的非生理噪声来源：一项模型研究。

DOI：
发表时间：
2010
期刊：
影响因子：
0
作者：
Politte, David;Prior, Fred;Ponton, Curtis;Nolan, Tracy;Larson
通讯作者：
Larson

Utility of linear mixed effects models for event-related potential research with infants and children.

线性混合效应模型在婴儿和儿童事件相关电位研究中的实用性。

DOI：
发表时间：
2022-04
期刊：
影响因子：
4.7
作者：
Heise, Megan J;Mon, Serena K;Bowman, Lindsay C
通讯作者：
Bowman, Lindsay C

Automated measurement of pediatric cranial bone thickness and density from clinical computed tomography.

通过临床计算机断层扫描自动测量儿童颅骨厚度和密度。

DOI：
发表时间：
2012
期刊：
影响因子：
0
作者：
Smith, Kirk;Politte, David;Reiker, Gregory;Nolan, Tracy S;Hildebolt, Charles;Mattson, Chelsea;Tucker, Don;Prior, Fred;Turovets, Sergei;Larson
通讯作者：
Larson

Automated measurement of skull circumference, cranial index, and braincase volume from pediatric computed tomography.

通过儿科计算机断层扫描自动测量颅骨周长、颅骨指数和脑壳体积。

DOI：
发表时间：
2013
期刊：
影响因子：
0
作者：
Smith, Kirk;Politte, David;Reiker, Gregory;Nolan, Tracy S;Hildebolt, Charles;Mattson, Chelsea;Tucker, Don;Prior, Fred;Turovets, Sergei;Larson
通讯作者：
Larson