1Behavioral flexibility requires the prefrontal cortex and striatum. Here, we investigate neuronal 2 ensembles in the medial frontal cortex (MFC) and the dorsomedial striatum (DMS) during one 3 form of behavioral flexibility: learning a new temporal interval. We studied corticostriatal 4 neuronal activity as rodents trained to respond after a 12-second fixed interval (FI12) learned to 5 respond at a shorter 3-second fixed interval (FI3). On FI12 trials, we discovered time-related 6 ramping was reduced in the MFC but not in the DMS in two-interval vs. one-interval sessions. 7 We also found that more DMS neurons than MFC neurons exhibited differential interval-related 8 activity on the first day of two-interval performance. Finally, MFC and DMS ramping was 9 similar with successive days of two-interval performance but DMS temporal decoding increased 10 on FI3 trials. These data suggest that the MFC and DMS play distinct roles during temporal 11 learning and provide insight into corticostriatal circuits. 13 Behavioral flexibility requires learning to adapt to uncertainty. Two forebrain structures 14 critical for flexibility are the prefrontal cortex and striatum (Fuster, 2008; Kehagia et al., 2010). 15 Prefrontal cortical neurons densely innervate the striatum (Gabbott et al., 2005; Wall et al., 2013) 16 and disruptions of either structure profoundly impact the learning of new goals, rules, and 17 strategies (Hart et al., 2018; Ragozzino, 2007). Dysfunctional corticostriatal circuits and 18 connectivity are implicated in a range of psychiatric and neurological disorders (Deutch, 1993; 19 Shepherd, 2013). However, the relative roles of prefrontal and striatal networks during 20 behavioral flexibility are unclear. 21One task that provides an ideal window into behavioral flexibility is interval timing, 22 which requires participants to estimate an interval of several seconds via a motor response. 23Across species, interval timing requires the prefrontal cortex and striatum (Coull et al., 2011; Merchant and de 25 Lafuente, 2014). Work from our group and others has shown that both prefrontal and striatal 26 neurons encode temporal information via 'time-related ramping' activity-or monotonic changes 27 in firing rate over a temporal interval (Bakhurin et al., 2017; Donnelly et al., 2015; Emmons et 28 al., 2017; Kim et al., 2018; Narayanan, 2016; Wang et al., 2018). Our past work suggested that 29 ramping activity in neurons of the medial frontal cortex (MFC) and the dorsomedial striatum 30 (DMS) is very similar, with ~40% of neurons in each area exhibiting such activity (Emmons et 31 al., 2017). We have also found that MFC inactivation attenuates DMS ramping (Emmons et al., 32 2019, 2017) and that MFC stimulation is sufficient to increase DMS ramping (Emmons et al., 33 2019). These data suggest that DMS ramping is closely linked to MFC ramping and suggest the 34 hypothesis that MFC and DMS ensembles respond similarly as animals learn new temporal 35 intervals. By contrast, recordings from prima...

Fixed interval, peak interval, and temporal bisection procedures have been used to assess cognitive functions and address questions such as how animals perceive, represent, and reproduce time intervals. They have also been extensively used to test the effects of drugs on behavior, and to describe the neural correlates of interval timing.However, those procedures usually require several weeks of training for behavior to stabilize. Here, we compared three types of training protocols and reported a procedure in which performance by the end of the very first session nearly matches the performance of long-term training. We also discuss this fast-learning protocol in terms of an information-theory approach. This one-day training protocol can be used to investigate temporal learning and may be especially useful to electrophysiological and neuropharmacological studies.

Although time is a fundamental dimension of life, we do not know how brain areas cooperate to keep track and process time intervals. Notably, analyses of neural activity during learning are rare, mainly because timing tasks usually require training over many days. We investigated how the time encoding evolves when animals learn to time a 1.5 s interval. We designed a novel training protocol where rats go from naive- to proficient-level timing performance within a single session, allowing us to investigate neuronal activity from very early learning stages. We used pharmacological experiments and machine-learning algorithms to evaluate the level of time encoding in the medial prefrontal cortex and the dorsal striatum. Our results show a double dissociation between the medial prefrontal cortex and the dorsal striatum during temporal learning, where the former commits to early learning stages while the latter engages as animals become proficient in the task.

Polygenic risk scores (PRS) for breast cancer (BC) have a clear clinical utility in risk prediction. PRS transferability across populations and ancestry groups is hampered by population-specific factors, ultimately leading to differences in variant effects, such as linkage disequilibrium (LD) and differences in variant frequency (AF-diff). Thus, locally-sourced population-based phenotypic and genomic datasets are essential to assess the validity of PRS derived from signals detected across populations. Here, assess the transferability of a BC PRS composed of 313 risk variants (313-PRS) in two Brazilian tri-hybrid admixed ancestries (European, African and Native American) whole-genome sequenced cohorts. We computed 313-PRS in both cohorts (n=753 and n=853) versus the UK Biobank (UKBB, n=264,307) as reference. We show that although the Brazilian cohorts have a high European (EA) component, with AF-diff and to a lesser extent LD patterns similar to those found in EA populations, the 313-PRS distribution is inflated when compared to that of the UKBB, leading to potential overestimation of PRS-based risk if EA is taken as a standard. Interestingly, we find that case-controls lead to equivalent predictive power when compared to UKBB-EA samples with AUROC values of 0.66-0.62 compared to 0.63 for UKBB.

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