在线看一区二区,国产精品 久久久久久久,夜夜久久av,重口味一区二区

當前位置: > 學術報告 > 文科 > 正文

文科

A Big Data Approach to Understanding Complex Behavioral and Neuroimaging Data

發(fā)布時間:2018-07-06 瀏覽:

Date of event2018-07-11

Time of event: 14:30

LecturerFengqing Zhang

Venue: Rm.303. No.1 Teaching bldg.Yanta Campus

Hosted by: Key Laboratory of Modern Teaching Technology, Ministry of Education, Center for Teacher Professional ability Development

Profile of the Lecturer

Dr. Fengqing Zhang is a tenure-track Associate Professor in the department of Psychology at Drexel University. Her research focuses on the development and application of advanced statistical models to analyze complex and high dimensional data (e.g. neuroimaging data, complex behavioral data). In particular, her lab has been focused on using multimodal neuroimaging (e.g., MRI, DTI, rs-fMRI) to examine neurodegenerative diseases (e.g., Alzheimers disease) and psychiatric disorders (e.g., PTSD, eating disorders). The modeling approach she takes includes machine learning, Bayesian inference, and high dimensional data analysis. In addition, she collaborates with the Weight, Eating, and Lifestyle Science (WELL) center on projects related to treatment development for weight loss maintenance and eating disorders.

As data can be produced and stored more massively and cheaply from various sources, we are entering the era of Big Data. Many traditional statistical models that perform well for moderate sample size do not scale to massive heterogeneous data. Therefore, new statistical thinking and modeling are required.

One important application of big data integration is multimodal neuroimaging. The use of multimodal neuroimaging is a promising and recent approach to study complex brain disorders by utilizing complementary physical and physiological sensitivities. At the same time, however, the advent of multimodal neuroimaging has brought the need to analyze and integrate neuroimaging data with advanced statistical methods that can make full usage of their informational complexity. Using data from the Philadelphia Neurodevelopmental Cohort (PNC) study, we identify three distinct groups, people with trauma exposure and no PTSD symptoms, people with trauma exposure and long-lasting PTSD symptoms as well as healthy controls. A large number of imaging features from different modalities including MRI, DTI, and resting-state fMRI are derived. We then develop an integrative probabilistic model to combine heterogeneous data from multiple modalities and select predictive multimodal imaging signatures of PTSD.

The integrated measurement of diet, physical activity, and the built environment is another important application of big data integration. Recent advances in wearable computing through the use of accelerometers, smartphones, and other devices for tracking individuals and individual behavior, have created a rich opportunity for the integrated measurement of environmental context and behavior. In our weight loss maintenance studies, we develop a smartphone app that utilizes just-in-time adaptive intervention and machine learning to predict and prevent dietary lapses. Data integration strategies using features derived from different sensors to predict affect liability for patients with eating disorders will also be discussed.

大大香蕉国产| 岛国精品综合一区| 国产乱码精品一区二区三区蜜柚| 亚洲欧美一区二区三区99| 日本综合免费久久| 草B了视频| 日韩伦理片八区| 日本亚洲日本亚洲不卡| 日韩国产精品欧美一区二区| 亚洲图片另类综合| 久porn| 伊人不卡在线麻烦| 老湿机精品视频无码| 日老骚逼日老骚逼日老骚逼 | 国产欧美精品在线不卡| 亚洲美女精品网站| 欧美图片二三区| 日韩 欧美 国产小说 视频| 日本夜运动精品网站| 天天舔天天射天天上| 91久久人人爽| 精品无码人妻一区二区三粉嫩av| 99精品88| 久久久久久欧美日韩一区二区 | 国产精品白浆无码流出九色| 大片按摩网站在线观看| 美女激情黄色| 99精品久久三区| 欧美在线人人天天| 青青操免费视频在线观看| 插日本美女| 国产大鸡巴操逼操逼| 久久久久久久久久九九九九| 88AV一区二区三区在线观看| 美女免费黄网站久久| 精品美腿熟女| 中文字幕一区二区亚洲一区| 日韩精品99在线视频| aⅴ色国产欧美一本大道| 91夜色九色色| 无码专区人妻系列日韩精品|