Bayesian Mapping of the Striatal Microcircuit Reveals Robust Asymmetries in the Probabilities and Distances of Connections

Journal article

The striatum’s complex microcircuit is made by connections within and between its D1- and D2-receptor expressing projection neurons and at least five species of interneuron. Precise knowledge of this circuit is likely essential to understanding striatum’s functional roles and its dysfunction in a wide range of movement and cognitive disorders. We introduce here a Bayesian approach to mapping neuron connectivity using intracellular recording data, which lets us simultaneously evaluate the probability of connection between neuron types, the strength of evidence for it, and its dependence on distance. Using it to synthesise a complete map of the mouse striatum, we find strong evidence for two asymmetries: a selective asymmetry of projection neuron connections, with D2 neurons connecting twice as densely to other projection neurons than do D1 neurons, but neither subtype preferentially connecting to another; and a length-scale asymmetry, with interneuron connection probabilities remaining non-negligible at more than twice the distance of projection neuron connections. We further show our Bayesian approach can evaluate evidence for wiring changes, using data from the developing striatum and a mouse model of Huntington’s disease. By quantifying the uncertainty in our knowledge of the microcircuit, our approach reveals a wide range of potential striatal wiring diagrams consistent with current data

Authors: Cinotti, F; Humphries, MD

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Society for Neuroscience
• Journal: The Journal of Neuroscience
• Volume: 42
• Issue: 8
• Pages: 1417-1435
• Publication date: 2021-12-10
• DOI: 10.1523/jneurosci.1487-21.2021
• EISSN: 1529-2401
• ISSN: 0270-6474
• Language: English
• Keywords: Neuron; Dopamine; Bayesian probability; Inhibitory postsynaptic potential; Bayesian inference; Computer science; Striatum; Interneuron; Artificial intelligence; Neuroscience; Biology; Projection (relational algebra); Algorithm