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1. A distributed circuit for associating environmental context with motor choice in retrosplenial cortex

   https://doi.org/10.1126/sciadv.abf9815  

   Franco, Luis M.; Goard, Michael J. (August 2021, Science Advances)

   During navigation, animals often use recognition of familiar environmental contexts to guide motor action selection. The retrosplenial cortex (RSC) receives inputs from both visual cortex and subcortical regions required for spatial memory and projects to motor planning regions. However, it is not known whether RSC is important for associating familiar environmental contexts with specific motor actions. We test this possibility by developing a task in which motor trajectories are chosen based on the context. We find that mice exhibit differential predecision activity in RSC and that optogenetic suppression of RSC activity impairs task performance. Individual RSC neurons encode a range of task variables, often multiplexed with distinct temporal profiles. However, the responses are spatiotemporally organized, with task variables represented along a posterior-to-anterior gradient along RSC during the behavioral performance, consistent with histological characterization. These results reveal an anatomically organized retrosplenial cortical circuit for associating environmental contexts with appropriate motor outputs. 

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2. Computational mechanisms for context-based behavioral interventions: A large-scale analysis

   https://doi.org/10.1073/pnas.2114914119  

   Zhao, Wenjia Joyce; Coady, Aoife; Bhatia, Sudeep (April 2022, Proceedings of the National Academy of Sciences)

   Choice context influences decision processes and is one of the primary determinants of what people choose. This insight has been used by academics and practitioners to study decision biases and to design behavioral interventions to influence and improve choices. We analyzed the effects of context-based behavioral interventions on the computational mechanisms underlying decision-making. We collected data from two large laboratory studies involving 19 prominent behavioral interventions, and we modeled the influence of each intervention using a leading computational model of choice in psychology and neuroscience. This allowed us to parametrize the biases induced by each intervention, to interpret these biases in terms of underlying decision mechanisms and their properties, to quantify similarities between interventions, and to predict how different interventions alter key choice outcomes. In doing so, we offer researchers and practitioners a theoretically principled approach to understanding and manipulating choice context in decision-making. 

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3. A performance predictor for implementation selection of parallelized static and temporal graph algorithms

   https://doi.org/10.1002/cpe.6267  

   Rehman, Akif; Ahmad, Masab; Khan, Omer (April 2021, Concurrency and Computation: Practice and Experience)

   Abstract Task‐based execution of graph workloads allows various ordered and unordered implementations, with tasks representing dependencies between graph vertices and edges. This work explores graph algorithms in the context of ordered and unordered task‐based implementations, that trade‐off work‐efficiency with parallelism. The monotonicity of convergent graph solutions is the reason behind the trade‐off between work‐efficiency and parallelism. This trade‐off results in variable performance‐based choices within and across different machines (CPUs and GPUs), graph algorithms, implementations (ordered, relaxed, and unordered). Input graphs also augment this choice space, with this work analyzing temporally changing graphs in addition to the static graphs explored by prior works. These algorithmic and architectural choices are first explored in this work, and it is seen that different graph workload‐input combinations perform optimally on diverse architectural configurations. The resulting choice space is analyzed and this work represents it in the form of characteristic variables that correlate with each choice space. Using these characteristic variables, this work proposes analytical and neural network models to correlate these choice spaces to find the best performing implementation. The variables and the prediction models proposed in this work are also integrated with a state‐of‐the‐art performance predictor on a multiaccelerator setup, and shows geometric performance gains of 54% on a CPU, 14% on a GPU, and 31.5% in a multiaccelerator setup over baseline implementations without performance prediction. 

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4. Differential encoding of action selection by orbitofrontal and striatal population dynamics

   https://doi.org/10.1152/jn.00316.2020  

   Yang, Long; Masmanidis, Sotiris C. (August 2020, Journal of Neurophysiology)

   Survival relies on the ability to flexibly choose between different actions according to varying environmental circumstances. Many lines of evidence indicate that action selection involves signaling in corticostriatal circuits, including the orbitofrontal cortex (OFC) and dorsomedial striatum (DMS). While choice-specific responses have been found in individual neurons from both areas, it is unclear whether populations of OFC or DMS neurons are better at encoding an animal’s choice. To address this, we trained head-fixed mice to perform an auditory guided two-alternative choice task, which required moving a joystick forward or backward. We then used silicon microprobes to simultaneously measure the spiking activity of OFC and DMS ensembles, allowing us to directly compare population dynamics between these areas within the same animals. Consistent with previous literature, both areas contained neurons that were selective for specific stimulus-action associations. However, analysis of concurrently recorded ensemble activity revealed that the animal’s trial-by-trial behavior could be decoded more accurately from DMS dynamics. These results reveal substantial regional differences in encoding action selection, suggesting that DMS neural dynamics are more specialized than OFC at representing an animal’s choice of action. NEW & NOTEWORTHY While previous literature shows that both orbitofrontal cortex (OFC) and dorsomedial striatum (DMS) represent information relevant to selecting specific actions, few studies have directly compared neural signals between these areas. Here we compared OFC and DMS dynamics in mice performing a two-alternative choice task. We found that the animal’s choice could be decoded more accurately from DMS population activity. This work provides among the first evidence that OFC and DMS differentially represent information about an animal’s selected action. 

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5. N100 as a generic cortical electrophysiological marker based on decomposition of TMS-evoked potentials across five anatomic locations

   https://doi.org/doi:10.1007/s00221-016-4773-7  

   Xiaoming Du, Fow-Sen Choa (January 2017, Experimental brain research)

   N100, the negative peak of electrical response occurring around 100 ms, is present in diverse functional paradigms including auditory, visual, somatic, behavioral and cognitive tasks. We hypothesized that the presence of the N100 across different paradigms may be indicative of a more general property of the cerebral cortex regardless of functional or anatomic specificity. To test this hypothesis, we combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) to measure cortical excitability by TMS across cortical regions without relying on specific sensory, cognitive or behavioral modalities. The five stimulated regions included left prefrontal, left motor, left primary auditory cortices, the vertex and posterior cerebellum with stimulations performed using supra- and subthreshold intensities. EEG responses produced by TMS stimulation at the five locations all generated N100s that peaked at the vertex. The amplitudes of the N100s elicited by these five diverse cortical origins were statistically not significantly different (all uncorrected p > 0.05). No other EEG response components were found to have this global property of N100. Our findings suggest that anatomy- and modality-specific interpretation of N100 should be carefully evaluated, and N100 by TMS may be used as a bio-marker for evaluating local versus general cortical properties across the brain. 

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