Mechanisms, response heterogeneity and dosing from MRI-derived electric field models in tDCS augmented cognitive training: a secondary data analysis of the ACT study

tDCS 增强认知训练中 MRI 衍生电场模型的机制、反应异质性和剂量：ACT 研究的二次数据分析

• 批准号: 10170947
• 负责人: Ruogu Fang
• 金额: $ 218.1万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10170947
• 关键词: Address; Age; Age-associated memory impairment; Alzheimer' s Disease; Alzheimer' s disease risk; Anatomy; Artificial Intelligence; Atrophic; Behavioral; Bilateral; Brain; Brain region; CCL4 gene; Characteristics; Clinical; Clinical Trials; Cognition; Cognitive; Computer Models; Computing Methodologies; Data; Data Analyses; Data Set; Dose; Educational Intervention; Effectiveness; Elderly; Electrodes; Elements; Foundations; Funding; Future; Goals; Head; Heterogeneity; Image; Impaired cognition; Individual; Individual Differences; Intervention; Learning; Left; Lesion; Liquid substance; Location; Machine Learning; Magnetic Resonance Imaging; Measurement; Measures; Mental Health; Methods; Modeling; Neuroanatomy; Neuronal Plasticity; Outcome; Participant; Pathway interactions; Patients; Persons; Phase; Population; Positioning Attribute; Prevalence; Property; Public Health; Randomized Clinical Trials; Readiness; Research; Sampling; Scalp structure; Structure; System; Thick; Treatment outcome; United States National Institutes of Health; Work; age related; age related neurodegeneration; base; behavioral outcome; cognitive enhancement; cognitive training; comorbidity; cranium; effective intervention; electric field; experience; individual variation; insight; inter-individual variation; machine learning method; mild cognitive impairment; multimodality; neuroimaging; precision medicine; predicting response; prevent; primary outcome; response; sex; support vector machine; targeted delivery; treatment optimization; treatment response; white matter

ABSTRACT There is a pressing need for effective interventions to remediate age-related cognitive decline and alter the trajectory toward Alzheimer’s disease. The NIA Alzheimer’s Disease Initiative funded Phase III Augmenting Cognitive Training in Older Adults (ACT) trial aimed to demonstrate that transcranial direct current stimulation (tDCS) paired with cognitive training could achieve this goal. The present study proposes a state of the art secondary data analysis of ACT trial data that will further this aim by 1) elucidate mechanism of action underlying response to tDCS treatment with CT, 2) address heterogeneity of response in tDCS augmented CT by determining how individual variation in the dose of electrical current delivered to the brain interacts with individual brain anatomical characteristics; and 3) refine the intervention strategy of tDCS paired with CT by evaluating methods for precision delivery targeted dosing characteristics to facilitate tDCS augmented outcomes. tDCS intervention to date, including ACT, apply a fixed dosing approach whereby a single stimulation intensity (e.g., 2mA) and set of electrode positions on the scalp (e.g., F3/F4) is applied to all participants/patients. However, our recent work has demonstrated that age-related changes in neuroanatomy as well as individual variability in head/brain structures (e.g., skull thickness) significantly impacts the distribution and intensity of electrical current induced in the brain from tDCS. This project will use person-specific MRI-derived finite element computational models of electric current characteristics (current intensity and direction of current flow) and new methods for enhancing the precision and accuracy of derived models to precisely quantify the heterogeneity of current delivery in older adults. We will leverage these individualized precision models with state-of-the-art support vector machine learning methods to determine the relationship between current characteristics and treatment response to tDCS and CT. We will leverage the inherent heterogeneity of neuroanatomy and fixed current delivery to provide insight in the not only which dosing parameters were associated with treatment response, but also brain region specific information to facilitate targeted delivery of stimulation in future trials. Further still, the current study will also pioneer new methods for calculation of precision dosing parameters for tDCS delivery to potentially optimize treatment response, as well as identify clinical and demographic characteristics that are associated with response to tDCS and CT in older adults. Leveraging a robust and comprehensive behavioral and multimodal neuroimaging data set for ACT with advanced computational methods, the proposed study will provide critical information for mechanism, heterogeneity of treatment response and a pathway to refined precision dosing approaches for remediating age- related cognitive decline and altering the trajectory of older adults toward Alzheimer’s disease.

摘要

项目成果

DOMINO: Domain-aware loss for deep learning calibration

• DOI: 10.1016/j.simpa.2023.100478
• 发表时间: 2023-02
• 期刊: Software impacts
• 作者: Skylar E. Stolte;Kyle Volle;A. Indahlastari;Alejandro Albizu;A. Woods;K. Brink;Matthew Hale;R. Fang