CAREER: Small Data in a Big World: Balancing Interpretability and Generalizability for Data Integration in Clinical Neuroscience

职业：大世界中的小数据：平衡临床神经科学数据集成的可解释性和概括性

• 批准号: 2322823
• 负责人: Archana Venkataraman
• 金额: $ 50万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2322823&HistoricalAwards=false
• 关键词: CAREER; Small; Data; Big; World

Neurological and neuropsychiatric disorders affect millions of people worldwide and carry a staggering societal cost. Despite ongoing efforts, clinicians have a bare-bones understanding of these disorders, and hence, a limited ability to treat them. From an analytics perspective, clinical neuroscience is a field of high-dimensional datasets, small sample sizes, massive patient variability, and most importantly, an arguable lack of ground truth information. These challenges have led to a trade-off between the interpretability of a given model and its generalizability to new data. At one extreme, classical statistics allows us to formulate and test interpretable hypotheses about the brain, but it cannot make patient-specific generalizations. At the other extreme, conventional machine learning algorithms are geared towards patient generalizability but rarely illuminate a brain-basis for the prediction. This CAREER program will develop a Coupled Network Optimization (CNO) framework that balances the two analytical extremes. The resulting algorithms will reveal interpretable system-level interactions in the brain that can predict the behavioral and cognitive deficits of a given disorder. In parallel, the investigators have formulated a diverse range of educational initiatives to train the next generation of interdisciplinary data scientists.Mathematically, the CNO framework estimates a low-dimensional network manifold for functional neuroimaging data. The elemental bases of this manifold will correspond to interpretable group-level features, whereas the patient-specific projections will capture predictive information. The technical exploration of this award will unfold in three modular stages, each of which tackles an open challenge in the field. Thrust I will improve the CNO interpretability by imposing a patient-specific graph topology to guide the salient functional interactions. Thrust II will advance the CNO generalizability by introducing nonlinear and nonparametric regression models. Finally, Thrust III will leverage an equivalent Bayesian representation to tackle the challenges of multisite analysis. The CNO framework will be applied to two markedly different application testbeds: a large multi-site repository of neuroimaging, behavioral and genetic data for autism, and a focused clinical trial of functional electrical stimulation for spinal cord injury rehabilitation. Beyond the scientific goals, this award includes a three-pronged educational plan to build strong technical foundations, foster interdisciplinary collaborations through engagement and communication, and finally, motivate young women into the STEM fields. The investigators have outlined a comprehensive schedule of activities, ranging from curriculum development at the high school, undergraduate and graduate levels, to organizing student networking events, to mentoring high school women through the Johns Hopkins Women in Science and Engineering outreach.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

神经和神经精神疾病影响着全球数百万人，并带来惊人的社会成本。尽管正在进行努力，临床医生对这些疾病有一个基本的了解，因此治疗它们的能力有限。从分析学的角度来看，临床神经科学是一个高维数据集、小样本量、大量患者变异性的领域，最重要的是，有争议的缺乏基本事实信息。这些挑战导致了对给定模型的可解释性和对新数据的普适性之间的权衡。在一个极端情况下，经典统计学允许我们制定和测试关于大脑的可解释的假设，但它不能做出针对患者的概括。在另一个极端，传统的机器学习算法着眼于患者的泛化，但很少阐明预测的大脑基础。这一职业计划将开发一个耦合网络优化(CNO)框架，以平衡这两个分析极端。由此产生的算法将揭示大脑中可解释的系统级交互作用，可以预测给定疾病的行为和认知缺陷。与此同时，研究人员制定了一系列不同的教育举措，以培养下一代跨学科数据科学家。从数学上讲，CNO框架估计了一个功能神经成像数据的低维网络流形。这个流形的基本基础将对应于可解释的组级特征，而患者特定的投影将捕获预测性信息。该奖项的技术探索将分三个模块阶段展开，每个阶段都要解决该领域的一个公开挑战。推力I将通过实施特定于患者的图拓扑来指导显著的功能交互，从而提高CNO的可解释性。推力II将通过引入非线性和非参数回归模型来提高CNO的泛化能力。最后，推力III将利用等价的贝叶斯表示法来应对多站点分析的挑战。CNO框架将被应用于两个截然不同的应用试验床：一个是自闭症的神经成像、行为和遗传数据的大型多站点存储库，另一个是用于脊髓损伤康复的功能性电刺激的重点临床试验。除了科学目标之外，该奖项还包括一个三管齐下的教育计划，旨在建立强大的技术基础，通过参与和交流促进跨学科合作，最后激励年轻女性进入STEM领域。调查人员勾勒出了一份全面的活动时间表，从高中、本科和研究生层面的课程开发，到组织学生网络活动，再到通过约翰·霍普金斯大学女性科学与工程外展指导高中女性。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds

• DOI: 10.48550/arxiv.2303.14986
• 发表时间: 2023-03
• 期刊: ArXiv
• 作者: N. S. D'Souza;A. Venkataraman

DeepSOZ: A Robust Deep Model for Joint Temporal and Spatial Seizure Onset Localization from Multichannel EEG Data

DeepSOZ：基于多通道脑电图数据的联合时空癫痫发作定位的鲁棒深度模型

• 发表时间: 2023
• 期刊: MICCAI: Medical Image Computing and Computer Assisted Intervention
• 作者: Shama, Deeksha M.;Jing, Jiasen;Venkataraman, Archana
• 通讯作者: Venkataraman, Archana

GIRUS-net: A Multimodal Deep Learning Model Identifying Imaging and Genetic Biomarkers Linked to Alzheimer's Disease Severity.

GIRUS-net：一种多模式深度学习模型，可识别与阿尔茨海默病严重程度相关的影像和遗传生物标志物。

• DOI: 10.1109/embc40787.2023.10341000
• 发表时间: 2023
• 期刊: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
• 作者: Wu,Sarah;Venkataraman,Archana;Ghosal,Sayan
• 通讯作者: Ghosal,Sayan