Mouse dLGN receives input from a diverse population of retinal ganglion cells with limited convergence
Preprint posted on 15 May 2018 https://www.biorxiv.org/content/early/2018/05/15/322164
Article now published in Neuron at http://dx.doi.org/10.1016/j.neuron.2019.01.040
How much does the thalamus process visual information before it reaches the cortex? This preprint from Román Rosón et al. sheds light on this question by looking at the functional connectivity between the retina and the thalamus.Polona Jager
In vertebrates, visual perception begins in the retina of the eye, and this information is relayed to the brain by retinal output neurons – the retinal ganglion cells (RGCs). There exist at least 30 distinct RGC types, each transmitting information about a specific visual feature.
In the mammalian brain RGCs transfer information to more than 50 different regions, including the dorsal lateral geniculate nucleus (dLGN) in the thalamus. The dLGN extensively connects with the primary visual cortex, and is part of the pathway that gives rise to conscious visual perception or sight.
How then are visual inputs from the retina processed in the thalamus, the first stop in the brain’s visual pathway? To answer this question, studies have looked at “RGC convergence” – the number and diversity of RGCs that project to individual dLGN thalamocortical (TC) neurons. Low convergence would suggest a more relay-like transmission, whereas high convergence implies that a more complex transformation or integration of visual information may take place in the thalamus, before reaching the cortex.
Prior physiological work in the dLGN across mammalian species suggested only limited RGC convergence onto target dLGN cells. However, more recent structural electron microscopy imaging, transsynaptic tracing, and functional calcium imaging of retinal axonal boutons in the mouse dLGN all showed that much higher RGC convergence is possible, with up to 90 RGCs from multiple classes innervating a single dLGN neuron. In their recent preprint, Román Rosón et al. also address these questions.
The authors examine connectivity between the retina and the thalamus by first defining the overall diversity of RGC classes targeting the dLGN. Using a combination of mouse genetics and viral manipulations in the dLGN, a genetically encoded calcium indicator was selectively expressed in RGCs projecting to the dLGN. Two-photon calcium imaging was then used to measure the responses of labelled RGCs to a standardized set of stimuli.
On the basis of their response properties, the labelled dLGN-projecting RGCs were then assigned to previously characterised functional RGC classes (Baden et al., 2016). This showed that 75% of all RGC types target the dLGN, and that certain classes are over- or under-represented within the dLGN projecting population.
To look into how this wealth of information is processed by the thalamus, the authors were interested in the degree of functional RGC convergence onto single TC neurons in the dLGN. Here, functional convergence is defined as the number of different RGC types that meaningfully drive the responses of a TC cell to visual stimuli.
To this end, they first performed extracellular single-unit recordings of geniculate neurons in head-fixed mice, which were presented the same set of stimuli as the retinas. From this data, they observed that both TC and RGC responses showed similar diversity.
Finally, the study used a linear model to predict dLGN responses as a weighted sum of excitatory RGC inputs. This modelling implies that on average just 2 different RGC groups drive the response of a dLGN TC neuron. The estimate, however, varied depending on what threshold was chosen for inputs to be considered functionally relevant. Taken together, this would therefore suggest a limited functional retinal convergence in the visual thalamus.
Interestingly, the model also showed that RGC classes overrepresented in the dLGN projecting RGC population contributed significantly to dLGN TC neuron responses. This would suggest that the information these RGCs relay is particularly significant for subsequent visual computations.
What I like about this work
I think this work addresses the question of the relation between structure and function with an elegant and comprehensive experimental design, which combines viral-based anatomical tracing with calcium imaging. To my knowledge it is also the most complete functional characterisation of RGC classes that target the dLGN.
At a structural level, connectivity between the RGCs and dLGN TC neurons appears complex and ‘fuzzy’, as reported by many studies, with evidence of high RGC convergence onto some thalamic targets. The results here however suggest that this structural complexity does not necessarily translate functionally, as only few RGC types seem to dominate the functional responses of TC neurons. Therefore, this preprint could provide an explanation for opposing findings in the field, arrived at by using very different methodologies (e.g. electrophysiology vs electron microscopy and rabies virus tracing).
One caveat, however, is that the resolution is low. For example, tracing from the dLGN is done at a population level, with no distinction between excitatory and inhibitory dLGN neurons. Below, I also discuss some considerations that may require experiments at a finer scale, or using more complex visual paradigms, to address.
As also pointed out by the authors, in addition to TC cells, local circuits in the dLGN include inhibitory interneurons, and projections from the visual cortex, the reticular nucleus, midbrain and brainstem nuclei. RGCs are known to also target dLGN interneurons. It remains largely unknown how all of these local circuit features affect TC responses to visual inputs, especially with regards to these recent findings on structural and functional organization of retinogeniculate connectivity.
Additionally, in mice (and even more prominently in carnivores and primates), the dLGN is composed of parallel visual pathways. For example there is a significant distinction between its shell and core regions, in terms of the inputs they receive and their projection patterns in the cortex. It would thus be interesting to look if there are any differences in functional RGC convergence between different dLGN microcircuits.
Baden, T. et al. (2016) The functional diversity of retinal ganglion cells in the mouse. Nature, 529, 345-50.
Posted on: 4 July 2018Read preprint
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