• 复旦大学

纪鹏

纪鹏,1984年生,理学博士,研究员,上海高校特聘教授,博士研究生导师复旦大学类脑智能科学与技术研究院个人简介:2015年获得德国柏林洪堡大学理论物理博士学位,之后在德国波茨坦气候影响研究所工作,2017年加入复旦大学,先后担任青年研究员和研究员,获得了浦江人才(2017)、上海高校特聘教授-东方学者(2018)、东方学者-跟踪计划(2021)等荣誉称号。目前从事的研究涉及人脑和斑马鱼成像分析、计算神经科学、复杂系统、神经元网络建模、机器学习等交叉研究方向。以第一或通讯发表在Nature Physics、Nature Communications、Physics Reports、Physical Review Letters、Physics of Life Reviews等期刊上。 社会服务:第六届上海非线性科...

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Research 中文主页 > Research

Zebrafish brain structural and functional analysis

Working with ION on the structural and functional whole brain analysis of Zebrafish, from the perspectives of topological features, functional integration in response to external stimulations, and neuronal dynamics. Quantifying the spatio-temporal spreading patterns. 

                   


  Functional MRI resting-state connectivity

Functional connectivity captures the temporal association between brain regions, and characterizes how different regions are integrated and segregated to facilitate brain function. We attempt to understand its emergence from a complex-systems perspective:

  • Predicting resting-state functional connectivity from structural connectivity

  • Inferring structural connectivity from resting-state functional connectivity

  • Controllability of structural brain networks

Cascading failures in brain/power networks

In networked systems, a local perturbation can propagate by following paths along the network of interactions between the system’s units. Such behaviour can lead to a large-scale cascade of interaction failures. We adapt a classical load-redistribution model and based on brain networks that have been investigated with diffusion MRI, conduct an analysis of the vulnerability and cosusceptibility of the corresponding brain networks. We find a group of nodes that can, potentially, fail simultaneously. The cascade model advances our understanding of linked failures in brain networks, and our results provide new insights in understanding disease progression in, e.g., Dementia.

  • Evaluating dynamical/ structural perturbations;

  • Identifying cosusceptibility of components and its cascading sequence;

Collective dynamics on complex networks

The interplay between structure and dynamics is sufficient to induce the emergence of collective behavior among coupled oscillators. Our interests lie in understanding how the structure of complex networks shapes the collective dynamics; analysis of spectral properties of complex networks; and statistical characterization of real-world networks.

Brainbundler

                                   

 Based on previous brainbundler software, we are developing in-house software for 3D brain graph and dynamic perturbation spreading in networks.