Developing network methodologies for disease classification

主题（1）生理网络是健康数据科学中的一个新兴概念。它的目的是整合整个身体的各种健康相关数据，以建立一个人的功能相互依赖性的量化，提供重要的见解和不同身体状态的细微差别的分类策略[1]。在这个主题中，我将追求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。因此，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）开发用于检测阿尔茨海默病（AD）和其他痴呆症早期阶段的方法具有至关重要的社会重要性。一般人群。AD通常被称为断开综合征，由此大量神经元死亡导致大脑功能结构的解体[2]。我开发了新的复杂网络方法，这些方法已被开发并应用于飞行员脑电图（EEG）数据，发现AD患者的异常拓扑和时间功能模式。我的目标是建立在这一有前途的工作，开发，完善和应用这些方法，以更大的数据集的结构和功能成像数据（例如，英国生物银行），建立在我现有的合作，在阿尔茨海默病苏格兰痴呆症研究中心。收集的信息将用于分析和分类AD在一般人群中。参考文献：[1] Bartsch，R.P.，Kantelhardt，J.W.，Havlin，S.，伊万诺夫，P.C.，网络生理学揭示了网络拓扑结构与生理功能之间的关系。自然通信，3：702（2011）。[2]Delbeuck，X.，货车德林登，M.，Collette，F.，阿尔茨海默病是一种分离综合征？Neuropsychology Review，13（2）：79-92（2003）. [3]布朗斯坦，M. M.，布鲁纳，J.，Y.莱昆，Szlam，A.，Vandergheynst，P.，Geometric Deep Learning：going beyond Euclidean data，IEEE Signal Processing杂志，34（4）：18-42（2017）. [4]史密斯，K.，Escudero，J.，EEG功能连接的复杂层次拓扑，J. Neurosci。方法，276：1-12（2017）。[5]史密斯，K.，Ricaud，B.，Shahid，N.，罗兹，S.，Starr，J.，Ibanez，A.，Parra，文学硕士，Escudero，J.，Vandergheynst，P.，使用图形模块化Dirichlet能量定位视觉短期记忆绑定的时间功能动力学，科学报告，7：42013（2017）。

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).