Utilizing changes in human brain connectivity to establish a dose-response relationship involved in the therapeutic actions of prefrontal brain stimulation on depression symptoms
利用人脑连接的变化建立剂量反应关系,参与前额叶脑刺激对抑郁症状的治疗作用
基本信息
- 批准号:10542288
- 负责人:
- 金额:$ 11.7万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-04-01 至 2025-02-28
- 项目状态:未结题
- 来源:
- 关键词:AlgorithmsBrainClinicalClinical assessmentsComputer softwareDataData ScienceDevelopmentDisciplineDisease remissionDoctor of PhilosophyDoseFailureFundingGoalsHealth systemHumanImageIntelligenceInterventionK-Series Research Career ProgramsMachine LearningMajor Depressive DisorderMedicineMental DepressionModelingOutcomeParentsParticipantPatientsProbabilityResearchResearch PersonnelResearch Project GrantsResourcesRestSeveritiesSupervisionTechniquesTechnologyTestingTherapeuticTrainingUnited States National Institutes of HealthUniversitiesWorkcareercohortdepressive symptomsfunctional MRI scanhealth economicsimprovedineffective therapiesmachine learning algorithmmachine learning classifierneuroimagingneuroregulationnovelparent grantpersonalized medicinepredict clinical outcomepredicting responseresponsetreatment planningtreatment responsetreatment-resistant depression
项目摘要
PROJECT SUMMARY/ABSTRACT
This is a Diversity Supplement Proposal for Azeezat K. Azeez, Ph.D., entitled “Machine Learning for Predictive
Clinical Outcomes to Neuromodulation Therapy for Treatment-Resistant Depression”. It is a Supplement to the
Parent R01, held by Nolan Williams, MD titled “5R01MH122754-02: Utilizing changes in human brain
connectivity to establish a dose-response relationship involved in the therapeutic actions of prefrontal brain
stimulation on depression symptoms”. The goal of the Parent Grant is to (1) test changes in resting-state
functional connectivity (rsFC) using functional magnetic resonance imaging (fMRI) scans daily and (2) examine
how rsFC changes relate to clinical improvement due to a novel and effective neuromodulation intervention,
Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT). This will improve our understanding of the
underlying mechanism of Major Depressive Disorder (MDD), particularly Treatment-Resistant Depression.
Notwithstanding the high efficacy of SAINT, relative to existing therapeutics a substantial number of
participants do fail to respond. Failure to respond, particularly in TRD, leads to detrimental health and
economic effects on the participant as well as on the health system. Our lack of ability to predict who will
respond to treatment constitutes a major translational gap in the SAINT technology. Therefore, the goal of the
current diversity supplement is to employ machine learning algorithms on neuroimaging data to predict who is
most likely to respond to treatment. Data science, neuroimaging, and neurostimulation are converging at an
exciting junction, the intersection of these disciplines is where the Diversity Supplement lies. A combination of
Machine Learning classifier models (supervised and unsupervised) and selection of appropriate imaging
features, trained on training data, then tested, and validated will yield a model with high accuracy for predicting
clinical outcomes. A combination of these parts will allow us the highest probability of developing a successful
algorithm that can be packaged into software to accompany neuromodulation intervention. The current
Supplement aims to 1) classify Treatment Response between cohorts; Active, Sham, and Neurotypical Control,
and 2) accurately predict remission and response outcomes in Treatment Severity classes. The Diversity
Supplement would allow Dr.Azeez to gain proficiency in 1) Machine Learning Techniques, 2) Clinical
Assessments, and 3) Professional Development while under the 2-year funding period. Training and research
for the project will be conducted at Stanford University which offers excellent intellectual and physical
resources to complete the proposed work. The research proposed in the Supplement will help to launch Dr.
Azeez’s career in developing Computational Aids for Clinicians in Psychiatric Medicine. This is a major goal of
the supplement application and one that will prepare the candidate, Dr. Azeez, for the short-term goal of
preparing a competitive NIH K- Award, and the long-term goal for a career as an independent academic
researcher. This proposed work has the potential to improve the lives of patients suffering with depression.
项目摘要/摘要
这是一份针对Azeezat K.Azeez博士的多样性补充提案,题为《机器学习的预测性
神经调节疗法治疗难治性抑郁症的临床结果“。这是一份补充
父母R01,由医学博士诺兰·威廉姆斯持有,标题为“5R01MH122754-02:利用人脑的变化”
建立与额叶前脑治疗活动有关的剂量-反应关系的连通性
对抑郁症状的刺激“。父GRANT的目标是(1)测试静息状态下的变化
使用功能磁共振成像(FMRI)的功能连接(RsFC)每天扫描和(2)检查
RsFC的变化如何与一种新颖有效的神经调节干预的临床改善有关,
斯坦福加速智能神经调节疗法(SAINT)。这将有助于我们更好地理解
主要抑郁障碍(MDD)的潜在机制,特别是治疗难治性抑郁症。
尽管SAINT的疗效很高,但相对于现有的治疗方法,相当数量的
参与者确实没有做出回应。未能做出反应,特别是在TRD中,会导致有害的健康和
对参与者和卫生系统的经济影响。我们缺乏预测谁会的能力
对治疗的反应构成了SAINT技术中的一个重大翻译差距。因此,我们的目标是
目前的多样性补充是对神经成像数据使用机器学习算法来预测谁是
最有可能对治疗有反应。数据科学、神经成像和神经刺激正以
令人兴奋的结合点,这些学科的交叉点就是多样性补充的所在。一种组合
机器学习分类器模型(监督和非监督)和适当成像的选择
要素、根据训练数据进行训练、然后进行测试和验证将生成具有高预测精度的模型
临床结果。这些部分的组合将使我们能够开发出成功的
可以打包到软件中的算法,以伴随神经调节干预。海流
补充剂旨在1)对队列之间的治疗反应进行分类;积极的、假的和神经典型的对照,
2)准确预测治疗严重程度的缓解和应答结果。多样性
补充剂将使Azeez博士精通1)机器学习技术,2)临床
评估,以及3)在两年期资助期内的专业发展。培训和研究
因为该项目将在斯坦福大学进行,该大学提供优秀的智力和体能
完成拟议工作所需的资源。附录中提出的研究将有助于启动Dr。
Azeez在为精神病医学临床医生开发计算辅助工具方面的职业生涯。这是一个主要的目标
补充剂申请和一个将为候选人阿齐兹博士为短期目标做准备的申请
准备一个有竞争力的NIH K奖,以及作为一名独立学者的长期目标
研究员。这项拟议的工作有可能改善抑郁症患者的生活。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Nolan R. Williams其他文献
Repetitive Transcranial Magnetic Stimulation to the Dorsolateral Prefrontal Cortex Increases Connectivity Between the Dorsolateral Prefrontal Cortex and the Dorsal Striatum but Not the Ventral Striatum in Participants with Cannabis Use Disorder
重复经颅磁刺激背外侧前额叶皮层可增加患有大麻使用障碍的参与者背外侧前额叶皮层与背侧纹状体之间的连通性,但不增加腹侧纹状体之间的连通性
- DOI:
10.1016/j.brs.2024.12.628 - 发表时间:
2025-01-01 - 期刊:
- 影响因子:8.400
- 作者:
Seigo Ninomiya;Daniel M. McCalley;Wiebke B. Struckmann;Andrew D. Geoly;Brendan L. Wong;Masataka Wada;Nolan R. Williams;Mark S. George;Aimee L. McRea-Clark;Gregory L. Sahlem - 通讯作者:
Gregory L. Sahlem
Durability of clinical benefit with Stanford Neuromodulation Therapy (SNT) in treatment-resistant depression
斯坦福神经调节疗法(SNT)在难治性抑郁症中临床获益的持久性
- DOI:
10.1016/j.brs.2025.04.006 - 发表时间:
2025-05-01 - 期刊:
- 影响因子:8.400
- 作者:
Andrew D. Geoly;Katy H. Stimpson;Flint M. Espil;Brandon S. Bentzley;Nolan R. Williams - 通讯作者:
Nolan R. Williams
Interoception Biomarkers for Precision Neuromodulation
用于精准神经调节的内感受生物标志物
- DOI:
10.1016/j.bpsc.2025.03.002 - 发表时间:
2025-05-01 - 期刊:
- 影响因子:4.800
- 作者:
Martijn Arns;Nolan R. Williams - 通讯作者:
Nolan R. Williams
Towards accredited clinical training in brain stimulation: Proceedings from the brain stimulation subspecialty summits
迈向脑刺激领域经认证的临床培训:脑刺激亚专业峰会会议记录
- DOI:
10.1016/j.brs.2025.02.012 - 发表时间:
2025-03-01 - 期刊:
- 影响因子:8.400
- 作者:
Shan H. Siddiqi;Leo Chen;Nicholas T. Trapp;Noreen Bukhari-Parlakturk;Joseph J. Taylor;Aaron D. Boes;Joshua C. Brown;Tracy Barbour;Joan A. Camprodon;Michael D. Fox;Brian H. Kopell;Carlene MacMillan;Alfonso Fasano;Robert S. Fisher;Ziad Nahas;Gonzalo J. Revuelta;Patricio Riva-Posse;John D. Rolston;Katherine Scangos;Mouhsin M. Shafi;Nolan R. Williams - 通讯作者:
Nolan R. Williams
Sustained Efficacy of Stanford Neuromodulation Therapy (SNT) in Open-Label Repeated Treatment.
斯坦福神经调节疗法 (SNT) 在开放标签重复治疗中的持续疗效。
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:17.7
- 作者:
Andrew D. Geoly;Ian H Kratter;Pouya Toosi;Eleanor J. Cole;Gregory L Sahlem;Nolan R. Williams - 通讯作者:
Nolan R. Williams
Nolan R. Williams的其他文献
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{{ truncateString('Nolan R. Williams', 18)}}的其他基金
The Effects of Stanford Accelerated Intelligent Neuromodulation Therapy on Explicit and Implicit Suicidal Cognition
斯坦福大学加速智能神经调节疗法对外显和内隐自杀认知的影响
- 批准号:
10263271 - 财政年份:2020
- 资助金额:
$ 11.7万 - 项目类别:
Utilizing changes in human brain connectivity to establish a dose-response relationship involved in the therapeutic actions of prefrontal brain stimulation on depression symptoms
利用人脑连接的变化建立剂量反应关系,参与前额叶脑刺激对抑郁症状的治疗作用
- 批准号:
10560493 - 财政年份:2020
- 资助金额:
$ 11.7万 - 项目类别:
Utilizing changes in human brain connectivity to establish a dose-response relationship involved in the therapeutic actions of prefrontal brain stimulation on depression symptoms
利用人脑连接的变化建立剂量反应关系,参与前额叶脑刺激对抑郁症状的治疗作用
- 批准号:
10772313 - 财政年份:2020
- 资助金额:
$ 11.7万 - 项目类别:
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