Proceedings of The Physiological Society

Advances in Bio-Imaging (Warwick, UK) (2016) Proc Physiol Soc 36, C05

Poster Communications

Exploring visual network connectivity in mice using DCM fMRI

A. Niranjan1, P. Zeidman2, J. Wells1, M. F. Lythgoe1

1. Centre for Advanced Biomedical Imaging, University College London, London, United Kingdom. 2. Institute of Neurology, University College London, London, United Kingdom.

  • (A-C) RFX Bayesian model selection using family comparisons. A) Family 8 corresponds to fully connected models. B) Family 2 corresponds to f modulating LGd→VISp. C) Family 4 corresponds to models where the S drives LGd and SCs only. D) Network representation of results.

Introduction: The visual pathway is an important neuroscience research target. In previous work [1], we characterised the BOLD response to visual stimulation in the mouse brain and modulated the BOLD response with respect to temporal frequency of visual stimulation [1]. Statistical parametric mapping successfully identified the lateral geniculate nuclei (LGd), the superior colliculus (SCs) and the primary visual cortex (VISp). However, standard mapping analysis provides no insight into casual mechanisms of visual processing. Dynamic causal modelling (DCM) [2] is an analysis technique that uses a Bayesian framework to compare models of effective connectivity between brain regions. There is limited use of DCM fMRI in the rat [3] and none in mouse. This work explores the use of DCM in healthy mice, and opens up the possibility of examining effective network connectivity in transgenic mice. Methods: 8 female C57BL6/J mice weighing (20.7 ± 0.7) g were used. Medetomidine anaesthesia (0.4 mg/kg bolus, 0.8 mg/kg constant infusion) was used during functional imaging. Respiration was approximately (170 ± 20) breaths per minute, and core body temperature maintained at (37.0 ± 0.2) °C. Visual stimulation was conducted bilaterally with blue laser light (445 nm, Omicron). A block design was used (40 seconds of rest, 20 seconds of stimulus, 3 repeats per session). During stimulus blocks, the laser was pulsed at 1, 3, 5 or 10 Hz, with 10ms pulse duration. Subjects were scanned using a 9.4T MRI Scanner (Agilent Inc.). GE-EPI was used with 4 compressed segments. Analysis: For each subject, all sessions of pre-processed EPI data were modelled using a general linear model, including regressors modelling the onset of optic stimulation (S) and the parametric effect of stimulus temporal frequency (f). BOLD time series were extracted from LGd, SCs and VISp. DCMs were created for each subject, to explore which connections were modulated by f and which regions were driven by S. Connections between regions were considered to be bi-directional, and f was considered to either modulate nothing, LGd→VISp, SCs→VISp, or VISp itself. The driving effect of S was considered for all combinations of regions. In total this created a space of 168 models to be compared. A series of 3 random effects family model comparisons were conducted, with the model space partitioned into ‘families' according to the question being addressed. Results: There was evidence for a fully connected network (Fig.1A), with f modulating the LGd→VISp connection (Fig.1B), and S driving LGd and SCs (Fig.1C). These results are summarized by the network diagram in Fig.1D, which is consistent with studies tracing projections of retinal ganglion cells throughout the mouse brain [4]. We demonstrate the ability of DCM to make inferences of effective connectivity in the mouse brain which are consistent with tracer studies [4].

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