Quantifying the role of the connectome in resiliency to multiple sclerosis

量化连接组在多发性硬化症恢复力中的作用

• 批准号: 9435991
• 负责人: Amy Kuceyeski
• 金额: $ 25.43万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-30 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9435991
• 关键词: Anatomy; Biological Markers; Brain; Brain Injuries; Brain region; Classification; Cognitive; Data; Development; Diffusion; Disease; Disease Progression; Future; Goals; Impairment; Individual; Injury; Innovative Therapy; Knowledge; Lead; Link; Machine Learning; Magnetic Resonance Imaging; Mathematics; Measures; Methods; Mission; Modeling; Multiple Sclerosis; National Institute of Neurological Disorders and Stroke; Neurologic; Outcome; Pathway Analysis; Pathway interactions; Patients; Pattern; Physically Handicapped; Process; Public Health; Publishing; Quality of life; Recovery; Rehabilitation therapy; Research; Role; Signal Transduction; Structure; Structure-Activity Relationship; Techniques; Testing; Therapeutic Intervention; Time; Traumatic Brain Injury; Work; base; burden of illness; clinically relevant; cognitive disability; connectome; disability; driving force; imaging biomarker; improved; insight; mathematical methods; mathematical model; multiple sclerosis patient; multiple sclerosis treatment; nervous system disorder; neuroimaging; novel; novel therapeutics; outcome forecast; personalized medicine; prognostic; repaired; resilience; response; tool; translational impact; white matter; white matter damage

MS damages white matter pathways that connect brain regions, i.e. the structural connectome (SC), disrupting flow of electrical signals, i.e. the functional connectome (FC), causing cognitive and physical disability. Howev- er, the burden of disease in the brain is not always proportional to an individual's disability. How the brain com- pensates for damage in resilient patients remains a mystery, making it difficult to develop accurate prognoses and treatments that can leverage this process in less fortunate patients. Without this knowledge it will not be possible to create individualized therapies based on the brain's natural resiliency mechanism or to establish reliable ways to predict potential for recovery. The thriving field of brain connectivity network analysis, or con- nectomics, provides a promising tool with which to capture, model and understand mechanisms of resiliency. The long-term goal is to develop novel, personalized rehabilitation methods that mimic and enhance the brain's resiliency process to restore cognitive and physical abilities after damage due to neurological conditions. The overall objective of this work is to identify connectome-based imaging biomarkers of resiliency in MS, i.e. those that separate patients with high disease burden and low disability (high-adapters) from those with similar dis- ease burden and high disability (low-adapters). Our central hypothesis, the functional rerouting hypothesis, states that resilient patients' brains recover from injury by restoring normal functional connections using alter- nate white matter pathways to circumvent irrevocably damaged structural connections. This hypothesis is based on published and preliminary work in simulated studies, severe brain injury and mild to moderate trau- matic brain injury. The rationale for the proposed research is that insight into the brain's ability to compensate for injury would allow for more accurate prognostication and enable the development of novel therapeutic strategies for MS. Guided by strong preliminary data, this hypothesis will be tested by pursuing two specific aims: to identify global and regional metrics of the 1) structural and functional connectomes and 2) structure- function relationship between the connectomes that differentiate high-adapting and low-adapting MS patients. We will collect functional, diffusion and anatomical MRIs from 25 controls and 42 high- and 42 low-adapting MS patients to extract structural and functional connectomes and test central hypotheses. The approach is in- novative, in the applicant's opinion, as it implements cutting-edge machine learning techniques and a novel mathematical model to formalize the relationship between structural and functional connectomes and capture network-level functional rerouting in resiliency to MS-related damage. The proposed research is significant in that it is expected to have broad translational impact on the development of more accurate prognoses as well as targeted, personalized treatments for patients with MS and other neurological disorders. Ultimately, such knowledge has the potential to open up new horizons for innovative therapies, e.g. through non-invasive brain stimulation, that can dramatically improve the quality of life for patients with debilitating neurological disease.

MS损害连接大脑区域的白色物质通路，即结构连接体（SC）， 电信号的流动，即功能性连接体（FC），导致认知和身体残疾。然而， 呃，大脑的疾病负担并不总是与个人的残疾成正比。大脑是如何- 恢复力强的患者的损伤补偿仍然是一个谜，因此很难开发出准确的诊断方法。 以及可以在不幸的患者中利用这一过程的治疗方法。如果没有这些知识， 可以根据大脑的自然弹性机制创建个性化治疗，或者建立 预测恢复潜力的可靠方法。大脑连接网络分析（brain connectivity network analysis，简称con）是一个蓬勃发展的领域， nectomics，提供了一个很有前途的工具，捕捉，建模和理解的弹性机制。 长期目标是开发新颖的，个性化的康复方法，模仿和增强大脑的 复原力过程，以恢复认知和身体能力后，由于神经条件的损害。的 这项工作的总体目标是确定MS中弹性的基于连接体的成像生物标志物，即那些 将高疾病负担和低残疾（高适应者）的患者与具有类似疾病的患者区分开来， 减轻负担和高残疾（低适应者）。我们的核心假设，功能性改道假说， 指出，有弹性的患者的大脑从损伤中恢复，恢复正常的功能连接使用改变， 天然的白色物质通道，以绕过可修复的受损结构连接。这种假设是 根据已发表的和模拟研究的初步工作，严重脑损伤和轻度至中度创伤， 自动脑损伤这项研究的基本原理是，深入了解大脑的补偿能力 将允许更准确的测量，并使新的治疗方法的发展成为可能。 在强有力的初步数据的指导下，这一假设将通过追求两个具体的 目的：确定1）结构和功能连接体和2）结构- 区分高适应和低适应MS患者的连接体之间的功能关系。 我们将从25名对照组和42名高适应和42名低适应患者中收集功能、弥散和解剖MRI。 MS患者提取结构和功能连接体和测试中心假设。方法是- 在申请人看来，这是创新的，因为它实现了尖端的机器学习技术和新颖的 数学模型来形式化结构和功能连接体之间的关系， 网络级功能重新路由，对MS相关损坏具有弹性。该研究具有重要意义， 预计它也将对更准确的翻译产生广泛的影响， 作为MS和其他神经系统疾病患者的靶向个性化治疗。最终，这样的 知识有可能为创新疗法开辟新的视野，例如通过非侵入性脑 刺激，可以显着提高患者的生活质量与衰弱的神经系统疾病。