Communication between the mediodorsal thalamus and prelimbic cortex regulates timing performance in rats

Abstract

Predicting when future events will occur and adjusting behavior accordingly is critical to adaptive behavior. Despite this, little is known about the brain networks that encode time and how this ultimately impacts decision-making. One established finding is that the prefrontal cortex (PFC) and its non-human analogues (e.g., the rodent prelimbic cortex; PL) mediate timing. This provides a starting point for exploring the networks that support temporal processing by identifying areas that interact with the PFC during timing tasks. For example, substantial work has explored the role of frontostriatal circuits in timing. However, other areas are undoubtedly involved. The mediodorsal nucleus of the thalamus (MD) is an excellent candidate region. It shares dense, reciprocal connections with PFC-areas in both humans and non-human species and is implicated in cognition. However, causal data implicating MD-PFC interactions in cognition broadly is still sparse, and their role in timing specifically is currently unknown. To address this, we trained male rats on a time-based, decision-making task referred to as the 'peak-interval' procedure. During the task, presentation of a cue instructed the rats to respond after a specific interval of time elapsed (e.g., tone-8 seconds). We incorporated two cues; each requiring a response after a distinct time-interval (e.g., tone-8 seconds / light-16 seconds). We tested the effects of either reversibly inactivating the MD or PL individually or functionally disconnecting them on performance. All manipulations caused a comparable timing deficit. Specifically, responses showed little organization in time, as if primarily guided by motivational systems. These data expand our understanding of the networks that support timing and suggest that MD-PL interactions specifically are a core component. More broadly, our results suggest that timing tasks provide a reliable assay for characterizing the role of MD-PL interactions in cognition using rodents, which has been difficult to establish in the past.

The dorsal hippocampus’ role in context-based timing in rodents

“…We also analyzed single-trial patterns of responding, again using 1s bins. Specifically, we identified bursts of responses using a standard approach in which a dual step-function (i.e., rectangular pulse: first step-up, second step-down) is fit to response rates across time during a given trial (Church et al, 1994;De Corte et al, 2021). The algorithm iteratively fits the step function across time, finding the points where absolute residuals are minimized.…”

Temporal scaling and computing time in neural circuits: Should we stop watching the clock and look for its gears?

“…As effects such as temporal averaging, Vierordt’s law, and covariance expectations often operate cross-modally, one might expect a multisensory area to be involved. Functionally, the MD plays a broad role in cognition (Markowitsch, 1982 ; Peräkylä et al, 2017 ), and while data are limited, manipulations of the MD disrupt baseline timing performance (Yu et al, 2010 ; Lusk et al, 2020 ; De Corte et al, 2021 ). As a notable example, Lusk et al ( 2020 ) recently inhibited the MD optogenetically during the peak-procedure and found that peak times shifted later.…”

“…Consistent with this, disorders that preferentially disrupt MD-PFC communication, such as Schizophrenia, also disrupt timing (Ward et al, 2012 ; Singh et al, 2019 ). Furthermore, we recently provided initial causal data suggesting that selectively blocking communication between the MD and the prelimbic cortex—a rodent analog of the PFC—markedly disrupts timing (De Corte et al, 2021 ). Importantly, while monosynaptic MD-PFC projections are particularly relevant to Wang et al’s ( 2018 ) data, the MD is well positioned to modulate scaling in other areas, projecting heavily to the striatum, virtually all association cortices, and communicating with the hippocampus, presumably via reciprocal connections with parahippocampal structures (for excellent reviews see Saunders et al, 2005 ; Mitchell and Chakraborty, 2013 ; Pergola et al, 2018 ; Georgescu et al, 2020 ).…”