Biology subjects

Samborska, V.

Publications and source records attributed to Samborska, V..

5 recordsLinked to original sources

bioRxiv2024

An abstract relational map emerges in the human medial prefrontal cortex with consolidation

Understanding the structure of a problem, such as the relationships between stimuli, supports fast learning and flexible reasoning. Rodent work has suggested that abstraction of structure away from sensory details occurs over the course of multiple days, in cortex. However, direct evidence of such explicit relational representations in humans is scarce, and it is unclear whether they emerge on similar timescales. Here, we combine a graph-learning paradigm with functional magnetic resonance imaging (fMRI) to look for such a relational map in the human brain. We first trained participants on two associative graphs with the same structure. We then scanned participants twice while they used this knowledge, with several days between scanning sessions. Using fMRI repetition suppression, we found an abstract relational representation in the medial prefrontal cortex (mPFC), that emerged across the two scanning sessions. This finding was also replicated using representational similarity analysis (RSA). These results shed new light on how neural representations organising relational knowledge change with time.

neuroscience↗

bioRxiv2024

A computational approach to understanding effort-based decision-making in depression

BackgroundMotivational dysfunction is a core feature of depression, and can have debilitating effects on everyday function. However, it is unclear which disrupted cognitive processes underlie impaired motivation, and whether impairments persist following remission. Decision-making concerning exerting effort to obtain rewards offers a promising framework for understanding motivation, especially when examined with computational tools which can offer precise quantification of latent processes. MethodsEffort-based decision-making was assessed using the Apple Gathering Task, where participants decide whether to exert effort via a grip-force device to obtain varying levels of reward; effort levels were individually calibrated and varied parametrically. We present a comprehensive computational analysis of decision-making, initially validating our model in healthy volunteers (N=67), before applying it in a case-control study including current (N=41) and remitted (N=46) unmedicated depressed individuals, and healthy volunteers with (N=36) and without (N=57) a family history of depression. ResultsFour fundamental computational mechanisms that drive patterns of effort-based decisions, which replicated across samples, were identified: overall bias to accept effort challenges; reward sensitivity; and linear and quadratic effort sensitivity. Traditional model-agnostic analyses showed that both depressed groups showed lower willingness to exert effort. In contrast with previous findings, computational analysis revealed that this difference was primarily driven by lower effort acceptance bias, but not altered effort or reward sensitivity. ConclusionsThis work provides insight into the computational mechanisms underlying motivational dysfunction in depression. Lower willingness to exert effort could represent a trait-like factor contributing to symptoms, and might represent a fruitful target for treatment and prevention.

neuroscience↗

bioRxiv2023

Flexible and abstract neural representations of structural knowledge

Humans ability for generalisation is outstanding. It is flexible enough to identify cases where knowledge from prior tasks is relevant, even when many features of the current task are different, such as the sensory stimuli or the size of the task state space. We have previously shown that in abstract tasks, humans can generalise knowledge in cases where the only cross-task shared feature is the statistical rules that govern the tasks state-state relationships. Here, we hypothesized that this capacity is associated with generalisable representations in the entorhinal cortex (EC). This hypothesis was based on the ECs generalisable representations in spatial tasks and recent discoveries about its role in the representation of abstract tasks. We first develop an analysis method capable of testing for such representations in fMRI data, explain why other common methods would have failed for our task, and validate our method through a combination of electrophysiological data analysis, simulations and fMRI sanity checks. We then show with fMRI that EC representations generalise across complex non-spatial tasks that share a hexagonal grid structural form but differ in their size and sensory stimuli, i.e. their only shared feature is the rules governing their statistical structure. There was no clear evidence for such generalisation in EC for non-spatial tasks with clustered, as opposed to planar, structure.

neuroscience↗

bioRxiv2021

Retrosplenial cortex is necessary for spatial and non-spatial latent learning in mice

Latent learning occurs when associations are formed between stimuli in the absence of explicit reinforcement. Traditionally, latent learning in rodents has been associated with the creation internal models of space. However, increasing evidence points to roles of internal models also in non-spatial decision making. Whether the same brain structures and processes support the creation of spatially-anchored or non-spatial internal models via latent learning, is an open question. To address this question, we developed a novel operant box task that allows to test spatial and non-spatial versions of a flavour-based sensory preconditioning paradigm. We probed the role of the retrosplenial cortex, a brain area associated with spatial cognition and subjective value representation, in this task using precise, closed-loop optogenetic silencing during different task phases. We show that the retrosplenial cortex is necessary for both spatial and non-spatial latent learning in mice. We further demonstrate that the requirement of retrosplenial cortex is limited to the preconditioning phase of the task. Our results provide insight into the specific role of the retrosplenial cortex in latent learning, demonstrate that latent learning plays a general part in the creation of internal models, independent of spatial anchors, and provide a novel avenue for studying model-based decision making.

neuroscience↗

bioRxiv2021

Complementary task representationsin hippocampus and prefrontal cortexfor generalising the structure of problems

Few situations in life are completely novel. We effortlessly generalise prior knowledge to solve novel problems, abstracting common structure and mapping it onto new sensorimotor specifics. Here we trained mice on a series of reversal learning problems that shared the same structure but had different physical implementations. Performance improved across problems, demonstrating transfer of knowledge. Neurons in medial prefrontal cortex (mPFC) maintained similar representations across multiple problems, despite their different sensorimotor correlates, whereas hippocampal (dCA1) representations were more strongly influenced by the specifics of each problem. Critically, this was true both for representations of the events that comprised each trial, and those that integrated choices and outcomes over multiple trials to guide subjects decisions. These data suggest that PFC and hippocampus play complementary roles in generalisation of knowledge, with the former abstracting the common structure among related problems, and the latter mapping this structure onto the specifics of the current situation.

neuroscience↗