Biology subjects

Nili, H.

Publications and source records attributed to Nili, H..

16 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↗

bioRxiv2023

An information-theoretic quantification of the content of communication between brain regions

Quantifying the amount, content and direction of communication between brain regions is key to understanding brain function. Traditional methods to analyze brain activity based on the Wiener-Granger causality principle quantify the overall information propagated by neural activity between simultaneously recorded brain regions, but do not reveal the information flow about specific features of interest (such as sensory stimuli). Here, we develop a new information theoretic measure termed Feature-specific Information Transfer (FIT), quantifying how much information about a specific feature flows between two regions. FIT merges the Wiener-Granger causality principle with information-content specificity. We first derive FIT and prove analytically its key properties. We then illustrate and test them with simulations of neural activity, demonstrating that FIT identifies, within the total information flowing between regions, the information that is transmitted about specific features. We then analyze three neural datasets obtained with different recording methods, magneto- and electro-encephalography, and spiking activity, to demonstrate the ability of FIT to uncover the content and direction of information flow between brain regions beyond what can be discerned with traditional anaytical methods. FIT can improve our understanding of how brain regions communicate by uncovering previously hidden feature-specific information flow.

neuroscience↗

bioRxiv2022

The way of the light: how visual information reaches the auditory cortex in congenitally deaf adults

Human and animal studies on cross-modal plasticity under congenital deafness suggest that early auditory cortex plays a significant role in the processing of visual information when congenitally deprived from its typical (auditory) input. However, the pathway by which early auditory cortex is fed with visual information is still understudied. Here we focused on addressing how visual information reaches the auditory cortex under congenital deafness. We put forth a mechanistic model that proposes that different corticocortical and subcortical connections play a central role in rerouting visual information to the early auditory cortex of congenitally deaf individuals. Specifically, we show, using Representational Connectivity Analysis (RCA) and Dynamic Causal Modeling (DCM), that connections from the right superior colliculus to the right inferior colliculus, as well as connections from right early visual cortical regions to the right early auditory cortex play a role in rerouting visual information to early auditory cortex in congenitally deaf individuals. These findings shed light on how visual information reaches the early auditory cortex of deaf individuals - specifically, they suggest that neuroplasticity reshapes subcortical connections in order to re-route visual information to the auditory stream.

neuroscience↗

bioRxiv2021

Distinct neural representations for prosocial and self-benefitting effort

Prosocial behaviours - actions that benefit others - are central to individual and societal well-being. Most prosocial acts are effortful. Yet, how the brain encodes effort costs when actions benefit others is unknown. Here, using a combination of multivariate representational similarity analysis and model-based univariate analysis during fMRI, we reveal how the costs of prosocial efforts are processed. Strikingly, we identified a unique neural signature of effort in the anterior cingulate gyrus for prosocial acts both when choosing to help others and when exerting force for their benefit. This pattern was absent for similar self-benefitting behaviour and correlated with individual levels of empathy. In contrast, the ventral tegmental area and the ventral insula signalled subjective value preferentially when choosing whether to exert effort to benefit oneself. These findings demonstrate partially distinct brain areas guide the evaluation and exertion of effort costs when acts are prosocial or self-benefitting.

neuroscience↗

bioRxiv2021

Caveats and nuances of model-based and model-free representational connectivity analysis

Brain connectivity analyses have conventionally relied on statistical relationship between one-dimensional summaries of activation in different brain areas. However, summarising activation patterns within each area to a single dimension ignores the potential statistical dependencies between their multi-dimensional activity patterns. Representational Connectivity Analyses (RCA) is a method that quantifies the relationship between multi-dimensional patterns of activity without reducing the dimensionality of the data. We consider two variants of RCA. In model-free RCA, the goal is to quantify the shared information for two brain regions. In model-based RCA, one tests whether two regions have shared information about a specific aspect of the stimuli/task, as defined by a model. However, this is a new approach and the potential caveats of model-free and model-based RCA are still understudied. We first explain how model-based RCA detects connectivity through the lens of models, and then present three scenarios where model-based and model-free RCA give discrepant results. These conflicting results complicate the interpretation of functional connectivity. We highlight the challenges in three scenarios: complex intermediate models, common patterns across regions and transformation of representational structure across brain regions. The paper is accompanied by scripts that reproduce the results. In each case, we suggest potential ways to mitigate the difficulties caused by inconsistent results. The results of this study shed light on some understudied aspects of RCA, and allow researchers to use the method more effectively.

neuroscience↗

bioRxiv2021

Topic: Hyper immune Bovine Colostrum as a Low-Cost, Large-Scale Source of Antibodies against COVID-19

Many different strategies have been used to fight against COVID-19 pandemic as a therapeutics or prophylaxis approaches. However, none of them so far have used, passive immune transfer using products from immunized farm animals. Hyper immune bovine colostrums (HBC) have been used against many different respiratory and gastrointestinal tracts infections during past decades. Six mixed Holstein X Semental dairy cattles in their 6-7 months of gestation period years were chosen for hyper immunization with COVID-19 vaccine. An isolated and very well protected site was selected and equipped according to animal husbandry code of practice, used for animal experimentation. Specific IgG level against SARS-CoV-2 virus was measured before and after vaccination in the sera, and in the colostrum following parturition. Very high specific IgG level was detected one week following second vaccination in the sera and in first colostrums after parturition. Safety of the product was approved following phase 1 of clinical trials in 40 healthy volunteers. Phase 2 of the clinical trials is underway. Early results show effectiveness of the product in reducing sore throat and cough in early stages of SARS-CoV-2 infection.

microbiology↗

bioRxiv2020

Using distance on the Riemannian manifold to compare representations in brain and models

Representational similarity analysis (RSA) summarizes activity patterns for a set of experimental conditions into a matrix composed of pairwise comparisons between activity patterns. Two examples of such matrices are the condition-by-condition inner product matrix or the correlation matrix. These representational matrices reside on the manifold of positive semidefinite matrices, called the Riemannian manifold. We hypothesize that representational similarities would be more accurately quantified by considering the underlying manifold of the representational matrices. Thus, we introduce the distance on the Riemannian manifold as a metric for comparing representations. Analyzing simulated and real fMRI data and considering a wide range of metrics, we show that the Riemannian distance is least susceptible to sampling bias, results in larger intra-subject reliability, and affords searchlight mapping with high sensitivity and specificity. Furthermore, we show that the Riemannian distance can be used for measuring multi-dimensional connectivity. This measure captures both univariate and multivariate connectivity and is also more sensitive to nonlinear regional interactions compared to the state-of-the-art measures. Applying our proposed metric to neural network representations of natural images, we demonstrate that it also possesses outstanding performance in quantifying similarity in models. Taken together, our results lend credence to the proposition that RSA should consider the manifold of the representational matrices to summarize response patterns in the brain and models.

neuroscience↗

bioRxiv2020

Medial frontal cortex activity predicts information sampling in economic choice

Decision-making not only requires agents to decide what to choose, but also how much information to sample before committing to a choice. Previously established frameworks for economic choice argue for a deliberative process of evidence accumulation across time. These tacitly acknowledge a role of information sampling, in that decisions are only made once sufficient evidence is acquired, yet few experiments have explicitly placed information sampling under the participants control. Here, we use functional MRI to investigate the neural basis of information sampling in economic choice, by allowing participants to actively sample information in a multi-step decision task. We show that medial frontal cortex (MFC) activity is predictive of further information sampling prior to choice. Choice difficulty (inverse value difference) was also encoded in MFC, but this effect was explained away by the inclusion of information sampling as a co-regressor in the general linear model. A distributed network of regions across prefrontal cortex encoded key features of the sampled information at the time it was presented. We propose that MFC is an important controller of the extent to which information is gathered before committing to an economic choice. This role may explain why MFC activity has been associated with evidence accumulation in previous studies, in which information sampling was an implicit rather than explicit feature of the decision.

neuroscience↗

bioRxiv2020

Metric and chronological time in human episodic memory

The relative contributions of metric and chronological time in the encoding of episodic memories are unknown. One hundred one healthy young adults viewed 48 unique episodes of visual events and were later tested on recall of the order of events (chronological time) and the precise timing of events (metric time). The behavioral results show that metric recall accuracy correlates with chronological accuracy for events within episodes, but does not play a role on larger time-scales across episodes. Functional magnetic resonance imaging during encoding and recall showed that metric time was represented in the posterior medial entorhinal cortex, as well as the temporal pole and the cerebellum, whereas chronological time was represented in a widespread brain network including the anterior lateral entorhinal cortex, hippocampus, parahippocampal cortex and the prefrontal cortex. We conclude that metric time has a role in episodic memory on short time-scales and is mainly subserved by medial temporal lobe structures.

neuroscience↗

bioRxiv2020

Private-public mappings in human prefrontal cortex

A core feature of human cognition is an ability to separate private states of mind - what we think or believe - from public actions - what we say or do. This ability is central to navigating social interactions in which different contexts often require different mappings between private states and public actions in order to minimise conflict and facilitate communication. Here we investigated how the human brain supports private-public mappings, using an interactive task which required subjects to adapt how they communicated their confidence about a perceptual decision to the social context. Univariate and multivariate analysis of fMRI data revealed that a private-public distinction is reflected in a medial-lateral division of prefrontal cortex - with lateral frontal pole (FPl) supporting the context-dependent mapping from a private sense of confidence to a public report. The concept of private-public mappings provides a promising framework for understanding flexible social behaviour.

neuroscience↗

bioRxiv2019

Entorhinal and ventromedial prefrontal cortices abstract and generalise the structure of reinforcement learning problems

Knowledge of the structure of a problem, such as relationships between stimuli, enables rapid learning and flexible inference. Humans and other animals can abstract this structural knowledge and generalise it to solve new problems. For example, in spatial reasoning, shortest-path inferences are immediate in new environments. Spatial structural transfer is mediated by grid cells in entorhinal and (in humans) medial prefrontal cortices, which maintain their structure across different environments. Here, using fMRI, we show that entorhinal and ventromedial prefrontal cortex (vmPFC) representations perform a much broader role in generalising the structure of problems. We introduce a task-remapping paradigm, where subjects solve multiple reinforcement learning (RL) problems differing in structural or sensory properties. We show that, as with space, entorhinal representations are preserved across different RL problems only if task structure is preserved. In vmPFC, representations of standard RL signals such as prediction error also vary as a function of task structure.

neuroscience↗

bioRxiv2019

Map making: Constructing, combining, and navigating abstract cognitive maps

Cognitive maps are thought to enable model-based inferences from limited experience that can guide novel decisions-a hallmark of behavioral flexibility. We tested whether the hippocampus (HC), entorhinal cortex (EC), and ventromedial prefrontal cortex (vmPFC)/medial orbitofrontal cortex (mOFC) organize abstract and discrete relational information into a cognitive map to guide novel inferences. Subjects learned the status of people in two separate unseen 2-D social hierarchies defined by competence and popularity piecemeal from binary comparisons, with each dimension learned on a separate day. Although only one dimension was ever behaviorally relevant, multivariate activity patterns in HC, EC and vmPFC/mOFC were linearly related to the Euclidean distance between people in the mentally reconstructed 2-D space. Hubs created unique comparisons between the two hierarchies, enabling inferences between novel pairs of people. We found that both behavior and neural activity in EC and vmPFC/mOFC reflected the Euclidean distance to the retrieved hub, which was reinstated in HC. These findings reveal how abstract and discrete relational structures are represented, combined, and enable novel inferences in the human brain.

neuroscience↗

bioRxiv2019

Distributed neural replay of decision confidence mediates informational conformity

A change of mind in response to social influence could be driven by informational conformity to increase accuracy, or by normative conformity to comply with social norms such as reciprocity. Disentangling the behavioural, cognitive and neurobiological underpinnings of informational and normative conformity have proven elusive. Here, participants underwent fMRI while performing a perceptual task that involved both advice-taking and advice-giving to human and computer partners. The concurrent inclusion of two different social roles and two different social partners revealed distinct behavioural and neural markers for informational and normative conformity. dACC BOLD response tracked informational conformity towards both human and computer but tracked normative conformity only when interacting with human. A network of brain areas (dmPFC and TPJ) that tracked normative conformity increased their functional coupling with the dACC when interacting with humans. These findings enable differentiating the neural mechanisms by which different types of conformity shape social changes of mind.

neuroscience↗

bioRxiv2018

The hippocampus and neocortical inhibitory engrams protect against memory interference

Our experiences often overlap with each other, sharing features, stimuli or higher-order information. But despite this overlap, we are able to selectively recall individual memories to guide our decisions and future actions. The neural mechanisms that support such precise memory recall, however, remain unclear. Here, using ultra-high field 7T MRI we reveal two distinct mechanisms that protect memories from interference. The first mechanism involves the hippocampus, where the BOLD signal predicts behavioural measures of memory interference, and contextual representations that aid separation of overlapping memories are organised using a relational code. The second mechanism involves neocortical inhibition: when we reduce the concentration of neocortical GABA using trans-cranial direct current stimulation (tDCS) neocortical memory interference increases in proportion to the reduction in GABA, which in turn predicts behavioural performance. Together these findings suggest that memory interference is mediated by both the hippocampus and neocortex, where the hippocampus aids separation of memories by coding context-dependent relational information, while neocortical inhibition prevents unwanted co-activation between overlapping memories.

neuroscience↗

bioRxiv2018

Neural structure mapping in human probabilistic reward learning

Humans can learn abstract concepts that describe invariances over relational patterns in data. One such concept, known as magnitude, allows stimuli to be compactly represented by a single dimension (i.e. on a mental line), for example according to their cardinality, size or value. Here, we measured representations of magnitude in humans by recording neural signals whilst they viewed symbolic numbers. During a subsequent reward-guided learning task, the neural patterns elicited by novel complex visual images reflected their pay-out probability in a way that suggested they were encoded onto the same mental number line. Our findings suggest that in humans, learning about values is accompanied by structural alignment of value representations with neural codes for the concept of magnitude.

neuroscience↗

bioRxiv2018

Focused learning promotes continual task performance in humans

Humans can learn to perform multiple tasks in succession over the lifespan (\"continual\" learning), whereas current machine learning systems fail. Here, we investigated the cognitive mechanisms that permit successful continual learning in humans. Unlike neural networks, humans that were trained on temporally autocorrelated task objectives (focussed training) learned to perform new tasks more effectively, and performed better on a later test involving randomly interleaved tasks. Analysis of error patterns suggested that focussed learning permitted the formation of factorised task representations that were protected from mutual interference. Furthermore, individuals with a strong prior tendency to represent the task space in a factorised manner enjoyed greater benefit of focussed over interleaved training. Building artificial agents that learn to factorise tasks appropriately may be a promising route to solving continual task performance in machine learning.\n\nSignificance StatementHumans learn to perform many different tasks over the lifespan, such as speaking both French and Spanish. The brain has to represent task information without mutual interference. In machine learning, this \"continual learning\" is a major unsolved challenge. Here, we studied the patterns of errors made by humans and state-of-the-art deep networks whilst they learned new tasks from scratch and without instruction. Humans, but not machines, seem to benefit from training regimes that focussed on one task at a time, especially when they had a prior bias to represent stimuli in a way that facilitated task separation. Machines trained to exhibit the same prior bias suffered less interference between tasks, suggesting new avenues for solving continual learning in artificial systems.

neuroscience↗