Incorporating the connectome into large-scale neuro-computational models to simulate neuroimaging experiments of visual and auditory short-term memory
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1
Neural Bytes LLC, United States
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2
National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Section on Brain Imaging and Modeling, United States
Introduction
Large-scale neural models (LSNMs) can relate neuroscience data from different temporal and spatial scales to investigate the neurocomputational mechanisms responsible for carrying out cognitive tasks. The availability of structural connectivity maps of the whole brain (i.e., connectomes) offers an opportunity to incorporate this macro-scale level information into LSNMs. In this work, we demonstrate how to merge LSNMs with a connectome to simulate multi-subject auditory and visual short-term memory neuroimaging experiments.
Methods
The original visual and auditory short-term memory models (Tagamets and Horwitz, 1998, Husain et al, 2004; Horwitz et al., 2005) were composed of interconnected neural populations that represent, in the visual model, primary and secondary visual (V1/V2, V4), inferotemporal (IT), and prefrontal cortex (PFC), and in the auditory model, primary and secondary auditory cortices (A1, A2), superior temporal gyrus/sulcus (ST), and PFC. Half the neural populations within both LSNMs were 'non task-specific’ (NS) neurons (Horwitz et al, 2005) that provided noise to ‘task-specific’ neurons that dealt with either shapes (visual model) or tonal patterns (auditory model) during delayed match-to-sample (DMS) tasks. In the current version, the NS neurons were replaced with an anatomical connectome model that contained 998 regions of interest (ROI) interconnected by white matter fiber tract weights (Hagmann et al., 2008) obtained from The Virtual Brain open-source software (TVB, Sanz-Leon et al, 2013). We devised an automated procedure to generate bidirectional links between visual and auditory LSNM nodes and TVB connectome nodes. To compare the simulations with actual neuroimaging experiments, we varied the weights among LSNM brain regions to generate different “subjects” and we converted the simulated neural activity into neuroimaging (fMRI, MEG) time-series.
Results and conclusions
We simulated multi-subject auditory and visual short-term memory neuroimaging experiments using the newly combined LSNM/TVB model. The TVB connectome provided neural noise to the neural activities of task-specific neuronal populations of the visual and auditory LSNMs. The LSNMs, in turn, incorporated extra connectivity into the connectome, which is a refinement of the anatomical connectivity provided by white-matter fiber tract connections and are necessary for a computational model to perform a cognitive task. Our work shows how to merge LSNMs with structural connectomes to simulate multi-subject neuroimaging experiments of cognitive tasks.
Acknowledgements
This research was funded by the Division of Intramural Research of the National Institute on Deafness and Other Communication Disorders.
References
Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, Sporns O (2008). Mapping the structural core of the human cerebral cortex. PLoS Biol. Jul 1;6(7):e159.
Horwitz B, Warner B, Fitzer J, Tagamets MA, Husain FT, Long TW (2005). Investigating the neural basis for functional and effective connectivity. Application to fMRI. Philos Trans R Soc Lond B Biol Sci. May 29;360(1457):1093-108.
Husain FT, Tagamets MA, Fromm SJ, Braun AR, Horwitz B (2004). Relating neuronal dynamics for auditory object processing to neuroimaging activity: a computational modeling and an fMRI study. Neuroimage. Apr;21(4):1701-20.
Sanz-Leon P, Knock SA, Woodman MM, Domide L, Mersmann J, McIntosh AR, Jirsa V (2013). The Virtual Brain: a simulator of primate brain network dynamics. Front Neuroinform. Jun 11;7:10.
Tagamets MA, Horwitz B. (1998). Integrating electrophysiological and anatomical experimental data to create a large-scale model that simulates a delayed match-to-sample human brain imaging study. Cereb Cortex. Jun;8(4)310-20.
Keywords:
connectome,
Neuroimaging,
neuronal population,
visual short-term memory,
auditory short-term memory,
computational neural network model,
large-scale neural simulation
Conference:
Neuroinformatics 2016, Reading, United Kingdom, 3 Sep - 4 Sep, 2016.
Presentation Type:
Poster
Topic:
Large-scale modeling
Citation:
Ulloa
A and
Horwitz
B
(2016). Incorporating the connectome into large-scale neuro-computational models to simulate neuroimaging experiments of visual and auditory short-term memory.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2016.
doi: 10.3389/conf.fninf.2016.20.00082
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Received:
24 May 2016;
Published Online:
18 Jul 2016.
*
Correspondence:
Dr. Antonio Ulloa, Neural Bytes LLC, Washington, DC, United States, aup@bu.edu