1 / 5

Dynamic Neural Network Model for fMRI: Theoretical Neurobiology Advances in Brain Research

Explore the cutting-edge Dynamic Neural Network Model for fMRI, delving into the intersection of brain science, physics, and statistical methods, offering insights into memory, vision, and language functions. Discover opportunities in computer science and engineering at UCL's CoMPLEX.

dlouis
Download Presentation

Dynamic Neural Network Model for fMRI: Theoretical Neurobiology Advances in Brain Research

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Will Penny Wellcome Centre for Neuroimaging at UCL Attention Emotion Language MEG Vision Theoretical Neurobiology fMRI Memory Physics Methods

  2. Statistical Parametric Mapping (SPM) Statistical parametric map Design matrix Image time-series Kernel Realignment Smoothing General linear model Random Field Theory Statistical inference Normalisation p <0.05 Template Parameter estimates

  3. Dynamic Models of Brain Interactions Hemodynamicforward model:neural activityBOLD (nonlinear) Electric/magnetic forward model:neural activityEEGMEG LFP (linear) Neural state equation: fMRI MEG Neural model: 1 state variable per region bilinear state equation no propagation delays Neural model: 8 state variables per region nonlinear state equation propagation delays inputs

  4. u1 u2 c u1 a11 z1 u2 b21 a12 z1 a21 z2 z2 a22 Dynamical Neural Network Model for fMRI

  5. Opportunities Computer Science Engineering Physics Statistics UCL CoMPLEX

More Related