%0 Conference Paper
%B 2015 International Joint Conference on Neural Networks (IJCNN)
%D 2015
%T Reconstructing fMRI BOLD signals arising from cerebellar granule neurons - comparing GLM and balloon models
%A Chaitanya Medini
%A Giovanni Naldi
%A Dr. Bipin G. Nair
%A Egidio D'Angelo
%A Dr. Shyam Diwakar
%K balloon model
%K biomedical MRI
%K Biophysical model
%K BOLD
%K cerebellar granule neuron
%K cerebellum
%K circuit behavior
%K cognitive function
%K Computational neuroscience
%K Convolution
%K Correlation
%K fMRI
%K fMRI BOLD signal reconstruction
%K GLM
%K haemodynamics
%K hemodynamic response function
%K linear convolution
%K mathematical model
%K MATLAB
%K medical signal processing
%K neuronal activity
%K physiology
%K Radio frequency
%K rat cerebellum granular layer
%K Signal reconstruction
%K sociology
%K spiking information
%K stimuli spike trains
%K Volterra kernels
%X Understanding the relationship between fMRI BOLD and underlying neuronal activity has been crucial to connect circuit behavior to cognitive functions. In this paper, we modeled fMRI BOLD reconstructions with general linear model and balloon modeling using biophysical models of rat cerebellum granular layer and stimuli spike trains of various response times. Linear convolution of the hemodynamic response function with the known spiking information reconstructed activity similar to experimental BOLD-like signals with the limitation of short stimuli trains. Balloon model through Volterra kernels gave seemingly similar results to that of general linear model. Our main goal in this study was to understand the activity role of densely populated clusters through BOLD-like reconstructions given neuronal responses and by varying response times for the whole stimulus duration.
%B 2015 International Joint Conference on Neural Networks (IJCNN)
%I IEEE
%C Killarney, Ireland
%8 July
%G eng
%U https://ieeexplore.ieee.org/document/7280638/
%R 10.1109/IJCNN.2015.7280638