Grishma Mehta-Pandejee1, P A Robinson2, James A Henderson3, K M Aquino4, Somwrita Sarkar5. 1. School of Physics, The University of Sydney, Sydney, New South Wales 2006, Australia; Center of Excellence for Integrative Brain Function, The University of Sydney, New South Wales 2006, Australia. Electronic address: grishma.pandejee@sydney.edu.au. 2. School of Physics, The University of Sydney, Sydney, New South Wales 2006, Australia; Center of Excellence for Integrative Brain Function, The University of Sydney, New South Wales 2006, Australia. 3. School of Physics, The University of Sydney, Sydney, New South Wales 2006, Australia; Center of Excellence for Integrative Brain Function, The University of Sydney, New South Wales 2006, Australia; School of Information Technology and Electrical Engineering, University of Queensland, St Lucia, Queensland 4072, Australia. 4. School of Physics, The University of Sydney, Sydney, New South Wales 2006, Australia; Center of Excellence for Integrative Brain Function, The University of Sydney, New South Wales 2006, Australia; Sir Peter Mansfield Imaging Center, University of Nottingham, Nottingham NG7 2RD, United Kingdom. 5. Center of Excellence for Integrative Brain Function, The University of Sydney, New South Wales 2006, Australia; Design Lab, University of Sydney, Sydney, New South Wales 2006, Australia.
Abstract
BACKGROUND: The problem of inferring effective brain connectivity from functional connectivity is under active investigation, and connectivity via multistep paths is poorly understood. NEW METHOD: A method is presented to calculate the direct effective connection matrix (deCM), which embodies direct connection strengths between brain regions, from functional CMs (fCMs) by minimizing the difference between an experimental fCM and one calculated via neural field theory from an ansatz deCM based on an experimental anatomical CM. RESULTS: The best match between fCMs occurs close to a critical point, consistent with independent published stability estimates. Residual mismatch between fCMs is identified to be largely due to interhemispheric connections that are poorly estimated in an initial ansatz deCM due to experimental limitations; improved ansatzes substantially reduce the mismatch and enable interhemispheric connections to be estimated. Various levels of significant multistep connections are then imaged via the neural field theory (NFT) result that these correspond to powers of the deCM; these are shown to be predictable from geometric distances between regions. COMPARISON WITH EXISTING METHODS: This method gives insight into direct and multistep effective connectivity from fCMs and relating to physiology and brain geometry. This contrasts with other methods, which progressively adjust connections without an overarching physiologically based framework to deal with multistep or poorly estimated connections. CONCLUSIONS: deCMs can be usefully estimated using this method and the results enable multistep connections to be investigated systematically.
BACKGROUND: The problem of inferring effective brain connectivity from functional connectivity is under active investigation, and connectivity via multistep paths is poorly understood. NEW METHOD: A method is presented to calculate the direct effective connection matrix (deCM), which embodies direct connection strengths between brain regions, from functional CMs (fCMs) by minimizing the difference between an experimental fCM and one calculated via neural field theory from an ansatz deCM based on an experimental anatomical CM. RESULTS: The best match between fCMs occurs close to a critical point, consistent with independent published stability estimates. Residual mismatch between fCMs is identified to be largely due to interhemispheric connections that are poorly estimated in an initial ansatz deCM due to experimental limitations; improved ansatzes substantially reduce the mismatch and enable interhemispheric connections to be estimated. Various levels of significant multistep connections are then imaged via the neural field theory (NFT) result that these correspond to powers of the deCM; these are shown to be predictable from geometric distances between regions. COMPARISON WITH EXISTING METHODS: This method gives insight into direct and multistep effective connectivity from fCMs and relating to physiology and brain geometry. This contrasts with other methods, which progressively adjust connections without an overarching physiologically based framework to deal with multistep or poorly estimated connections. CONCLUSIONS: deCMs can be usefully estimated using this method and the results enable multistep connections to be investigated systematically.
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