bioRxiv Subject Collection: Neuroscience's Journal
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Thursday, February 6th, 2025
| Time |
Event |
| 9:21a |
A neural basis for flexible perceptual inference
What seems obvious in one context can take on an entirely different meaning if that context shifts. While context-dependent inference has been widely studied, a fundamental question remains: how does the brain simultaneously infer both the meaning of sensory input and the underlying context itself, especially when the context is changing? Here, we study flexible perceptual inference--the ability to adapt rapidly to implicit contextual shifts without trial and error. We introduce a novel change-detection task in dynamic environments that requires tracking latent state and context. We find that mice exhibit first-trial behavioral adaptation to latent context shifts driven by inference rather than reward feedback. By deriving the Bayes-optimal policy under a partially observable Markov decision process, we show that rapid adaptation emerges from sequential updates of an internal belief state. In addition, we show that artificial neural networks trained via reinforcement learning achieve near-optimal performance, implementing Bayesian inference-like mechanisms within their recurrent dynamics. These networks develop flexible internal representations that enable real-time adjustments to the inference model. Our findings establish flexible perceptual inference as a core principle of cognitive flexibility, offering computational and neural-mechanistic insights into adaptive behavior in uncertain environments. | | 9:21a |
Responses to oddball communication sequences in the bat frontal and auditory cortices
Stimulus-specific adaptation (SSA) is a ubiquitous phenomenon in the animal kingdom across sensory modalities, but this type of neural response has rarely been studied using natural sounds in the auditory brain. Here, we leveraged the well-documented acoustic repertoire of the bat species Carollia perspicillata to study adaptation in the bat brain using natural communication sounds. We searched for SSA in single neuron spiking activity measured in two brain areas simultaneously: the auditory cortex and the frontal auditory field. The stimuli consisted of natural distress syllables, a form of vocalization produced by bats under duress. Bat distress vocalizations signal different degrees of urgency based on their amplitude modulation pattern, without large differences in their spectral structure. These distress vocalizations make an ideal test case for exploring the limits of neural deviance detection when considering naturalistic soundscapes with low stimulus contrast. The results show limited evidence for stimulus-specific adaptation in response to natural sound sequences in the majority of neurons studied. Many neurons did show a prominent effect related to context-dependent changes, caused by the type of sounds that occurred most frequently within each oddball sequence. Context-dependent responses were strongest in frontal neurons. Decoding analysis showed the existence of neural populations in both frontal and auditory cortices, which could distinguish between deviants and standards occurring within the same sequence, without large changes in evoked spike counts. Taken together, our results highlight the diversity of neural mechanisms complementing classical stimulus-specific adaptation when encoding natural vocalizations that do not differ markedly in their spectral composition. | | 9:21a |
BabyPy: a brain-age model for infancy, childhood and adolescence
Background: Brain-age models aim to provide a single metric reflecting overall brain health by estimating an individual's age based on neuroimaging data. Although extensively applied in adults, their use in early development has been limited due to data heterogeneity and model accessibility. Aim: To develop BabyPy, a robust and shareable brain-age model tailored for individuals from infancy to adolescence, capable of accurate predictions despite heterogeneity in age, study, and preprocessing differences Methods: A total of 4,021 structural T1-weighted MRI scans from individuals aged 0-17 years were used for training, sourced from collaborative multi-site datasets. An external test set of 1,143 scans (ages 0-16 years) was used for validation. Coarse neuroimaging features, grey matter volume (GMV), white matter volume (WMV), and subcortical grey matter volume (sGMV), were used. An ensemble machine learning approach was employed, combining Extra Trees Regression, Support Vector Machine, and Multilayer Perceptron base models. Model performance was evaluated via 5-fold cross-validation and external testing. Results: The ensemble meta-model explained 80% of the variance in age (training set, MAE = 1.55 years) and 46% of the variance in the external test set (MAE = 1.72 years). Conclusion: BabyPy is a shareable framework that estimates brain-age across a broad developmental range without requiring separate models. Despite limitations due to data heterogeneity, it demonstrates robust predictive performance and facilitates cross-study comparisons. Future improvements in harmonisation will further enhance the utility of generic brain-age models like BabyPy. | | 9:21a |
Cognitive maps for hierarchical spaces in the human brain
Many of the environments that we navigate through every day are hierarchically organized - they consist of spaces nested within other spaces. How do our mind/brains represent such environments? To address this question, we familiarized participants with a virtual environment consisting of a building within a courtyard, with objects distributed throughout the courtyard and building interior. We then scanned them with fMRI while they performed a memory task that required them to think about spatial relationships within and across the subspaces. Behavioral responses were less accurate and response times were longer on trials requiring integration across the subspaces compared to trials not requiring integration. fMRI response differences between integration and non-integration trials were observed in scene-responsive and medial temporal lobe brain regions, which were correlated the behavioral integration effects in retrosplenial complex, occipital place area, and hippocampus. Multivoxel pattern analyses provided additional evidence for representations in these brain regions that reflected the hierarchical organization of the environment. These results indicate that people form cognitive maps of nested spaces by dividing them into subspaces and using an active cognitive process to integrate the subspaces. Similar mechanisms might be used to support hierarchical coding in memory more broadly. | | 9:21a |
Neural signatures of stream segregation: From childhood to adulthood
When faced with noisy environments, listeners perform auditory scene analysis, which allows them to parse the auditory target from concurrent interferences. Stream segregation involves organizing similar sound waves into a coherent stream, while distinguishing dissimilar acoustic components and attributing them to distinct sources. Two event-related potential components have been identified as "neural signatures" of stream segregation: the Object-Related Negativity (ORN) and the P400. Our study aims to examine (i) the maturation of neural and behavioural correlates of stream segregation and (ii) the development of the relationship between stream segregation and speech perception in noise. ORN/P400 were recorded while 8-23 year-olds performed an active stream segregation task based on temporal coherence. Participants also performed speech identification in noise tasks (behaviourally). Behavioral results indicate an improvement in both stream segregation and speech perception in noise from childhood to adulthood. Amplitude of the ORN (but not P400) decreases, and latency of both ORN and P400 decreases throughout development. Critically, P400 amplitude significantly predicts stream segregation performance. Overall, our results suggest that the neural mechanisms underlying stream segregation follow a prolonged maturation trajectory, and support the progressive maturation of auditory scene analysis and speech perception in noise. | | 9:21a |
Friend Request Accepted: Fundamental Features of Social Environments Determine Rate of Social Affiliation
Humans start new friendships and social connections throughout their lives and such relationships foster mental and physical well-being. While friendship initiation may depend on alignment of subtle and complex personal variables, here we investigated whether it also depends on basic features of social environments. This would be analogous to other fundamental behaviours like foraging which depend on basic features of the environment such as the density of opportunities and the likelihood of success. In a pre-registered online study (n=783), we found people were more likely to send friend requests as the density of friendship opportunities decreased and frequency of success increased. Further, we found task-related measures, like overall friend requests, were correlated with personality-related factors like social thriving and anhedonia. Next, in an ultra-high-field fMRI study (n=24), we found that both fundamental features of social environments, opportunity density and frequency of success, affected neural activity across a network of regions linked to foraging including dorsal raphe nucleus, substantia nigra, and anterior insula. Finally, in resting-state fMRI data (n=400), we showed that model predicted estimates of anhedonia were related to functional connectivity between components of the same network. Thus, humans consider the background statistics of an environment while making social decisions and these decisions are linked to activity in ancient subcortical circuits mediating the influence of environmental statistics on other aspects of behaviour. Moreover, individual differences in how environmental features influence social behaviour are associated with variation in personality and psychiatric traits, offering new insights into inter-individual variability in social functioning. |
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