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Computational reconstruction of fMRI-BOLD from Neural Activity

Publication Type : Conference Paper

Thematic Areas : Learning-Technologies, Medical Sciences, Biotech

Publisher : 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, Jaipur, India .

Source : 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, Jaipur, India (2016)

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Keywords : Balloon model, Biological system modeling, biology computing, biomedical MRI, biophysical factors, biophysical granular layer model, Blood, blood flow, Blood Oxygen Level Dependent, BOLD responses, BOLD signal, Brain, brain activation, brain circuits, Brain modeling, Cerebellar granular layer, cerebellum, cerebellum granular layer, Cerebral blood flow, Cerebral Blood Volume, clinical measurements, computational modeling, Computational neuroscience, computational reconstruction, deoxy-hemoglobin content, fMRI, fMRI measures, fMRI-BOLD, function magnetic resonance imaging signals, mathematical model, metabolic oxygen, network activity, neural activity, neural nets, oxygenation, reconstructed signals, Solid modeling, spike raster patterns, Visual Cortex, visual cortex blood oxygen-level dependent responses, Windkessel model

Campus : Amritapuri

School : School of Biotechnology, Department of Computer Science and Engineering, School of Engineering

Center : Amrita Mind Brain Center, Biotechnology, Computational Neuroscience and Neurophysiology

Department : Computer Science, biotechnology

Year : 2016

Abstract : In this paper, we model function magnetic resonance imaging signals generated by neural activity (fMRI). fMRI measures changes in metabolic oxygen in blood in brain circuits based on changes in biophysical factors like concentration of total cerebral blood flow, oxy-hemoglobin and deoxy-hemoglobin content. A modified version of the Windkessel model by incorporating compliance has been used with a balloon model to generate cerebellar granular layer and visual cortex blood oxygen-level dependent (BOLD) responses. Spike raster patterns were adapted from a biophysical granular layer model as input. The model fits volume changes in blood flow to predict the BOLD responses in the cerebellum granular layer and in visual cortex. As a comparison, we tested the balloon model and the modified Windkessel model with the mathematically reconstructed BOLD response under the same input condition. Delayed compliance contributed to BOLD signal and reconstructed signals were compared to experimental measurements indicating the usability of the approach. The current study allows to correlate dynamic changes of flow and oxygenation during brain activation which connects single neuron and network activity to clinical measurements.

Cite this Research Publication : Chaitanya Nutakki, Ahalya Nair, Chaitanya Medini, Manjusha Nair, Dr. Bipin G. Nair, and Dr. Shyam Diwakar, “Computational reconstruction of fMRI-BOLD from neural activity”, in 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, 2016

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