Developing network methodologies for disease classification

开发疾病分类的网络方法

• 批准号: MR/S004122/1
• 负责人: Keith Smith
• 金额: $ 35.22万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FS004122%2F1
• 关键词: Developing; network; methodologies; disease; classification

Theme (1)The physiological network is an emerging concept in health data science. Its aim is to integrate a wide range of health-related data across the body to establish the quantification of a person's functional interdependencies, to provide important insights and nuanced classification strategies of different bodily states [1]. In this theme, I will pursue the integration of a physiological network approach with deep learning classification for large health datasets, focusing - at least in the first instance - on the UK Biobank. With this, I aim to develop critical novel methodologies for the advancement of understanding and classification of health and disease.Theme (2)It is of critical societal importance to develop methods for the detection of Alzheimer's Disease (AD) and other dementias in their early stages in the general population. AD is commonly known as a disconnection syndrome, whereby mass neuronal death leads to the disintegration of the brain's functional architecture [2]. I have developed novel complex network methodologies which have been developed and applied in pilot electroencephalography (EEG) data, finding abnormal topological and temporal functional patterns in patients with AD. I aim to build on this promising work, developing, refining and applying these methodologies to larger datasets of structural and functional imaging data (e.g., UK Biobank), building on my existing collaborations in the Alzheimer Scotland Dementia Research Centre. The information gathered will be used to analyse and classify AD in the general population.References: [1] Bashan, A., Bartsch, R.P., Kantelhardt, J.W., Havlin, S., Ivanov, P.C., Network Physiology reveals relations between network topology and physiological function. Nature Comms., 3: 702 (2011).[2] Delbeuck, X., Van der Linden, M., Collette, F., Alzheimer's disease as a disconnection syndrome?, Neuropsychology Review, 13(2): 79-92 (2003).[3] Bronstein, M.M., Bruna, J., LeCun, Y., Szlam, A., Vandergheynst, P., Geometric Deep Learning: going beyond euclidean data, IEEE Signal Processing Magazine, 34(4): 18-42 (2017).[4] Smith, K., Escudero, J., The complex hierarchical topology of EEG functional connectivity, J. Neurosci. Methods, 276: 1-12 (2017).[5] Smith, K., Ricaud, B., Shahid, N., Rhodes, S., Starr, J., Ibanez, A., Parra, M.A., Escudero, J., Vandergheynst, P., Locating temporal functional dynamics of visual short-term memory binding using graph modular Dirichlet energy, Scientific Reports, 7: 42013 (2017).

主题(1)生理网络是健康数据科学中的一个新兴概念。其目的是整合整个身体范围内广泛的健康相关数据，以建立一个人的功能相互依赖性的量化，为不同的身体状态提供重要的见解和细致入微的分类策略[10]。在这个主题中，我将致力于将生理网络方法与大型健康数据集的深度学习分类相结合，至少在第一个实例中，重点关注英国生物银行。有了这个，我的目标是开发关键的新方法，以促进对健康和疾病的理解和分类。主题(2)开发在普通人群中早期检测阿尔茨海默病（AD）和其他痴呆症的方法具有重要的社会意义。阿尔茨海默病通常被称为连接断开综合征，大量神经元死亡导致大脑功能结构[2]的解体。我开发了新的复杂网络方法，这些方法已被开发并应用于先导脑电图（EEG）数据，发现AD患者的异常拓扑和时间功能模式。我的目标是在这项有前途的工作的基础上，开发、完善和应用这些方法到更大的结构和功能成像数据集（例如，英国生物银行），建立在我在阿尔茨海默病苏格兰痴呆症研究中心的现有合作基础上。收集到的信息将用于分析和分类一般人群中的AD。参考文献：[1]Bashan， A., Bartsch, r.p., Kantelhardt, j.w., Havlin， S., Ivanov, p.c.，网络生理学揭示了网络拓扑与生理功能之间的关系。自然审稿。[j] .农业工程学报，2011,31 (2):391 - 391Delbeuck， X., Van der Linden， M., Collette， F.，阿尔茨海默病是一种断开综合症？[j] .中国心理医学杂志，2009,32 (2):379 - 392 (2003).Bronstein， M.M., Bruna， J., LeCun， Y., Szlam， A., Vandergheynst， P.，几何深度学习：超越欧氏数据，IEEE信号处理，34(4):18-42 (2017). bbb10张建军，张建军，张建军，脑电功能连接的复杂层次拓扑结构，神经科学学报。[j] .中国生物医学工程学报，2016,32 (1):1-12Smith， K, Ricaud， B., Shahid， N., Rhodes， S., Starr， J., Ibanez， A., Parra， M.A., Escudero， J., Vandergheynst， P.，基于图模Dirichlet能量的视觉短期记忆绑定的时间功能动态定位，科学报告，7:42013（2017）。

项目成果

Robust Assessment of EEG Connectivity Patterns in Mild Cognitive Impairment and Alzheimer's Disease.

• DOI: 10.3389/fnimg.2022.924811
• 发表时间: 2022
• 期刊: Frontiers in neuroimaging
• 作者: Clark, Ruaridh A;Smith, Keith;Escudero, Javier;Ibanez, Agustin;Parra, Mario A
• 通讯作者: Parra, Mario A

Phenotyping Ex-Combatants From EEG Scalp Connectivity

• DOI: 10.1109/access.2018.2872765
• 发表时间: 2018-09
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Andrés Quintero-Zea;J. López;Keith M. Smith;N. Trujillo;M. Parra;J. Escudero

Graph-Variate Signal Analysis

• DOI: 10.1109/tsp.2018.2881658
• 发表时间: 2017-03
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Keith M. Smith;Loukianos Spyrou;J. Escudero

Explaining the emergence of complex networks through log-normal fitness in a Euclidean node similarity space.

• DOI: 10.1038/s41598-021-81547-3
• 发表时间: 2021-01-21
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Smith KM

Abnormal Functional Hierarchies of EEG Networks in Familial and Sporadic Prodromal Alzheimer's Disease During Visual Short-Term Memory Binding.

• DOI: 10.3389/fnimg.2022.883968
• 发表时间: 2022
• 期刊: Frontiers in neuroimaging
• 作者: Smith, Keith M;Starr, John M;Escudero, Javier;Ibanez, Agustin;Parra, Mario A
• 通讯作者: Parra, Mario A