Biology subjects

Richter, D.

Publications and source records attributed to Richter, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Genomics of Educational Attainment Across 80 Years of Social and Political Transformation in Germany

Abrupt social and political transformations in twentieth-century Germany may have shifted how polygenic indices relate to educational outcomes across birth cohorts. Following a prespecified analysis plan, we harmonized genomic, educational, and regional data from four German studies, comprising 13,049 individuals born between 1918 and 1995. Polygenic index associations with educational attainment were robust and strengthened modestly across more recent birth cohorts, with no detectable difference between East and West Germany. Associations with intergenerational educational mobility showed no evidence of such strengthening but were stronger in the East than in the West. Polygenic indices were less predictive of educational attainment and educational mobility for women than for men, with no evidence of regional variation. We discuss the methodological limitations of polygenic indices for examining gene-environment interactions, and emphasize that evaluating whether these differences represent progress towards equal educational opportunity requires a normative framework that social science genetics alone cannot supply.

genomics↗

bioRxiv2026

Expanding the genetic code with diverse backbone structures across diverse sequence contexts

Expanding the genetic code to enable the selective and specific incorporation of non-canonical monomers (ncMs), beyond -L amino acids with variant sidechains, is a key outstanding challenge. Here we discover orthogonal aminoacyl-tRNA synthetases that selectively and specifically acylate their cognate orthogonal tRNA in vivo with eleven new ncMs spanning five different chemical classes: ,-disubstituted-amino acids, malonic acids, carboxylic acids, {beta}2-amino acids and N-cyclic amino acids. We demonstrate that co-translational incorporation of ,-disubstituted-amino acids, {beta}2-amino acids, {beta}3-amino acids and N-cyclic amino acids is strongly dependent on the codons either side of the codon used to direct ncM incorporation, with several ncMs incorporated at less than 1% of sequence contexts. We evolve orthogonal tRNAs that enable the incorporation of previously unincorporated ncMs, enable the incorporation of ncMs at >95% of sequence contexts and, increase the incorporation efficiency at challenging sequence contexts up to 40-fold. We demonstrate the encoded cellular synthesis of proteins and macrocycles containing ncMs and, explicitly demonstrate that our evolved tRNAs provide direct access to a wider range of genetically encoded macrocyclic sequences containing ncMs. Our results provide a foundation for composing, discovering and manufacturing proteins and peptides with functions augmented by ncMs.

synthetic biology↗

bioRxiv2026

Systematic functional drug testing in patient-derived models reveals ex vivo sensitivities associated with clinical outcome in rare solid tumors

Rare cancers are individually uncommon but collectively represent a substantial share of cancer burden, with limited systemic treatment options for many entities. Molecular profiling identifies targetable alterations, but actionable findings are limited and responses can vary despite a matched target. This motivates complementary approaches that directly assess tumor drug response. Here, we establish a biopsy-compatible ex vivo drug sensitivity testing platform optimized for low input and reproducibility. Patient-derived material was tested either directly or following ex vivo expansion. Functional profiling was performed within clinically relevant timelines across models from 126 patients with rare advanced solid tumors. Drug responses were consistent between model types. In most samples, we identified at least one potentially active compound, supporting feasibility at biopsy-scale. High in vitro sensitivity was associated with clinical benefit and progression-free survival. These findings support functional drug sensitivity testing as a complementary component in precision oncology for adults with rare cancers. Statement of SignificanceThis study presents a biopsy-compatible drug sensitivity testing platform for phenotype-based therapy stratification in rare cancers. It identifies actionable ex vivo drug responses and shows associations with clinical outcome in patients treated with screened therapies. These findings support functional testing as a complementary additional layer of stratification for therapeutic prioritization.

cancer biology↗

bioRxiv2025

Attention and Explicit Knowledge Drive Predictive Sharpening in Early Visual Cortex

Perception is increasingly understood as an inferential process, whereby what we perceive results from the integration of sensory inputs with expectations derived from prior knowledge. Top-down predictions have been shown to alter the encoding of sensory information, from early to late stages of processing. Yet, how such predictions shape neural representations in sensory cortices remains debated. Competing accounts suggest that predictions either sharpen neural representations by enhancing selectivity or dampen activity by broadly suppressing stimulus-driven responses. In a preregistered fMRI study, we tested whether these effects depend on the level of attentional engagement and explicit knowledge of predictive associations. Using a multisensory fMRI paradigm with concurrent but independent visual and auditory probabilistic associations (75% validity) and manipulated attention, we investigated predictive effects in human early visual cortex. Consistent with prior work, expected visual stimuli elicited reduced BOLD activity. Critically, sharpening of expected visual stimuli occurred exclusively when visual inputs were attended and the concurrently presented auditory inputs expected. In addition, the magnitude of the sharpening of visual representations correlated positively with participants explicit knowledge of the visuo-predictive associations. These findings highlight the key roles of attention and explicit knowledge in promoting predictive sharpening and underscore the need to study predictive processing in more ecologically valid, multisensory contexts.

neuroscience↗

bioRxiv2025

Ribosomal frameshifting is a carefully tuned modifier of proteins

Ribosomal frameshifting, occurring during protein translation, is one of the most consequential recoding events, as it leads to large changes to the protein sequence. However, beyond a few well-characterized cases, its prevalence and consequences across organisms remain poorly understood. To address this, we modeled and experimentally tested frameshifting and systematically characterized its evolutionary impact. We developed SLIPPERRS, a mechanistic model that identifies "slippery" frameshift sites achieving [~]70% accuracy. Building on SLIPPERS predictions and on our mass spectrometry and fluorescence-based assay, we characterized 165 short mRNA sequences. We identified dozens of novel sites with frameshift probabilities that often exceed those of known programmed frameshift cases, and found evidence for both translocation and decoding mechanisms. In Gammaproteobacteria, an average of [~]100 genes per species harbor frameshifting sites, which alter proteins by truncating, extending, and fusing canonical proteins. We detect selection for shorter and less disordered frameshifted proteins to mitigate their deleterious impact. We found negative correlation between GC-content and the occurrence of frameshifting sites. In GC-rich bacteria, where frameshift products are expected to be particularly long and disordered, there is selection for cryptic stop codons. While frameshifting is prevalent and expands protein diversity, it is carefully tuned to avoid its harmful consequences.

biochemistry↗

bioRxiv2025

Improving RNA-seq protocols

Bulk and single-cell RNA-seq are powerful tools for transcriptomic analysis, providing insights into many aspects of molecular and cellular phenotypes. Costs constrain the amount of biological insight obtainable within a given budget, and as sequencing prices decline, efficient library protocols have become a decisive factor. In this study, we introduce an approach to systematically optimize the number of usable reads that RNA-seq protocols generate. We applied this "funnel strategy" to prime-seq, an early-barcoding bulk RNA-seq protocol, by systematically testing critical protocol steps totaling 1080 samples in 49 libraries. This resulted in the optimized prime-seq2 protocol that increases the number of usable reads by 60 % and improves one of the most cost-efficient bulk RNA-seq protocols available. Our study also suggests that monitoring the filtering of usable reads can serve as a valuable quality control for many RNA-seq protocols and sheds light on the complexity of the conditions and interactions that shape RNA-seq library composition and their interpretation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/671269v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1bf102dorg.highwire.dtl.DTLVardef@bf36bforg.highwire.dtl.DTLVardef@1a3600aorg.highwire.dtl.DTLVardef@f63470_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2025

Rapid Computation of High-Level Visual Surprise

Predictive processing theories propose that the brain continuously generates expectations about incoming sensory information. Discrepancies between these predictions and actual inputs, sensory prediction errors, guide perceptual inference. A fundamental yet largely unresolved question is which stimulus features the brain predicts, and therefore, what kind of surprise drives neural responses. Here, we investigated this question using EEG and computational modelling based on deep neural networks (DNNs). Participants viewed object images whose identity was probabilistically predicted by preceding cues. We then quantified trial-by-trial surprise at both low-level (early DNN layers) and high-level (late DNN layers) visual feature representations. Results showed that stimulus-evoked responses around 200ms post-stimulus onset over parieto-occipital electrodes were increased by high-level, but not by low-level visual surprise. These findings demonstrate that high-level visual predictions are rapidly integrated into perceptual inference, suggesting that the brains predictive machinery is finely tuned to utilize expectations abstracted away from low-level sensory details to facilitate perception.

neuroscience↗

bioRxiv2025

An Updated Polygenic Index Repository: Expanded Phenotypes, New Cohorts, and Improved Causal Inference

Polygenic indexes (PGIs) -- DNA-based predictors of individual phenotypes -- have become essential tools across biomedical and social sciences. We introduce Version 2 of the Polygenic Index Repository, which expands phenotype coverage from 47 to 61, increases the number of participating datasets from 11 to 20, and adopts a more consistent and improved methodology for PGI construction. For 16 phenotypes, we leverage summary statistics from an updated GWAS meta-analysis with greater statistical power compared to the original release, thereby improving the PGIs predictive power. To improve power for family-based analyses, we provide imputed parental PGIs in all datasets with first-degree relatives and offer a framework for interpreting results from analyses that control for parental PGIs. We illustrate the utility of parental PGIs using two applications: (1) comparing PGI associations with and without parental PGI controls for all phenotypes in two Repository datasets with family data, and (2) for BMI and diastolic blood pressure, exploring the contribution of causal versus non-causal components of PGI associations to the imperfect portability of PGIs across subgroups within a genetic ancestry. Collectively, the updates enhance predictive performance, broaden the Repositorys scope, and introduce novel resources that reduce confounding bias and improve interpretability.

genomics↗

bioRxiv2024

Abstract rule generalization for composing novel meaning recruits the frontoparietal control network

The ability to generalize previously learned knowledge to novel situations is crucial for adaptive behavior, representing a form of cognitive flexibility that is particularly relevant in language. Humans excel at combining linguistic building blocks to infer the meanings of novel compositional words, such as "un-reject-able-ish". How do we accomplish this? While recent research on compositional generalization in relational memory, action planning and vision strongly implicates a medial prefrontal-hippocampal network (Baram et al., 2021; Barron et al., 2020; Schwartenbeck et al., 2023), it remains unclear whether the same network supports compositional inference in language. To this end, we trained participants on an artificial language in which the meanings of compositional words could be derived from known stems and unknown affixes, using abstract affixation rules (e.g., "good-kla" which means "bad", where "-kla" reverses the meaning of the stem word "good"). According to these rules, word meaning depended on the sequential relation between the stem and the affix (i.e., pre- vs. post-stem). During fMRI, participants performed a semantic priming task, with novel compositional words as either sequential order congruent (e.g., "white-kla") or incongruent primes (e.g., "kla-white"), and synonyms of the composed meaning as targets ("black"). Our results show that the compositional process engages a broad temporoparietal network, including the hippocampus, while the composed meanings are represented in left-lateralized language areas. Notably, newly composed meanings were decodable already at the time of the prime. Finally, we found that the composition process recruits abstract structure rule representations in a lateral frontoparietal network, rather than the predicted medial frontotemporal network, perhaps because abstract rules in language are more readily formatted as production rules rather than as relational map structures.

neuroscience↗

bioRxiv2024

Transportome remodeling of a symbiotic microalga inside a planktonic host

Metabolic exchange is one of the foundations of symbiotic associations between organisms and is a driving force in evolution. In the ocean, photosymbiosis between heterotrophic host and microalgae is powered by photosynthesis and relies on the transfer of organic carbon to the host (e.g. sugars). Yet, the identity of transferred carbohydrates as well as the molecular mechanisms that drive this exchange remain largely unknown, especially in unicellular photosymbioses that are widespread in the open ocean. Combining genomics, single-holobiont transcriptomics and environmental metatranscriptomics, we revealed the transportome of the marine microalga Phaeocystis in symbiosis within acantharia, with a focus on sugar transporters. At the genomic level, the sugar transportome of Phaeocystis is comparable to non-symbiotic haptophytes. By contrast, we found significant remodeling of the expression of the transportome in symbiotic microalgae compared to the free-living stage. More particularly, 32% of sugar transporter genes were differentially expressed. Several of them, such as GLUTs, TPTs and aquaporins, with glucose, triose-phosphate sugars and glycerol as potential substrates, were upregulated at the holobiont and community level. We also showed that algal sugar transporter genes exhibit distinct temporal expression patterns during the day. This reprogrammed transportome indicates that symbiosis has a major impact on sugar fluxes within and outside the algal cell, and highlights the complexity and the dynamics of metabolic exchanges between partners. This study improves our understanding of the molecular players of the metabolic connectivity underlying the ecological success of planktonic photosymbiosis and paves the way for more studies on transporters across photosymbiotic models.

cell biology↗

bioRxiv2024

Endothelial cannabinoid CB1 receptor deficiency reduces arterial inflammation and lipid uptake in response to atheroprone shear stress

BackgroundPeripherally restricted cannabinoid CB1 receptor antagonists without central side effects hold promise for treating metabolic disorders including diabetes and obesity. In atherosclerosis, the specific effects of peripheral CB1 signaling in vascular endothelial cells (ECs) remain incompletely understood. Methods and resultsEndothelial expression of the CB1 encoding gene CNR1 was detectable in human plaque single-cell RNA sequencing data. In situ hybridization of Cnr1 in murine aortas revealed a significantly increased endothelial expression within atheroprone compared to atheroresistant regions. In vitro, CNR1 was upregulated by oscillatory shear stress (OSS) in human aortic endothelial cells (HAoECs). Endothelial CB1 deficiency (Cnr1EC-KO) in female mice on atherogenic background resulted in pronounced endothelial phenotypic changes, with reduced vascular inflammation and permeability. This resulted in attenuated plaque development with reduced lipid content in female mice, while reduced white and brown adipose tissue mass and liver steatosis were observed in both males and females. Ex vivo imaging of carotid arteries via two-photon microscopy revealed less labeled low density lipoprotein (Dil-LDL) uptake in Cnr1EC-KO. This was accompanied by a significant reduction of aortic endothelial caveolin-1 (CAV1) expression, a key structural protein involved in lipid transcytosis, in female Cnr1EC-KO mice. In vitro, pharmacological blocking with CB1 antagonist AM281 reproduced the inhibition of CAV1 expression and Dil-LDL uptake in response to atheroprone OSS in HAoECs, which was dependent on cAMP-mediated PKA activation. Conversely, the CB1 agonist ACEA increased Dil-LDL uptake and CAV1 expression in HAoECs. Finally, treatment of atherosclerotic mice with the peripheral CB1 antagonist JD-5037 reduced plaque progression, CAV1 and endothelial adhesion molecule expression in female mice. ConclusionsThese results confirm an essential role of endothelial CB1 to the pathogenesis of atherosclerosis. Peripheral CB1 antagonists may hold promise as an effective therapeutic strategy for treating atherosclerosis and related metabolic disorders. NOVELTY AND SIGNIFICANCEWhat is known? O_LIEnhanced endocannabinoid-cannabinoid CB1 receptor signaling has been implicated in metabolic disorders, atherosclerosis, and hypertension, but the cell-specific role of endothelial CB1 in atherosclerosis is not well understood. C_LIO_LIGlobal CB1 antagonists improve metabolic function and inhibit atherosclerotic plaque development in mouse models, but have failed in the clinic due to centrally mediated psychiatric side effects. C_LI What is new? O_LIBased on human single-cell RNA sequencing data, CB1 is expressed in human plaque ECs. C_LIO_LIUsing transgenic mouse models and human primary aortic endothelial cells, we provide evidence for a key role of CB1 in endothelial shear stress response, inflammatory gene expression, and LDL uptake. C_LIO_LIThe underlying signaling pathway of CB1-induced endothelial LDL uptake involves a cAMP-PKA-dependent regulation of caveolin 1 (CAV1) expression, a structural protein of the shear stress sensitive signaling domains of the plasma membrane. C_LIO_LIBy limiting endothelial CAV1 and VCAM1 expression, peripherally restricted CB1 antagonists confer atheroprotection in mice. C_LI Our findings reveal that endothelial CB1 expression is induced by atheroprone shear stress responses and contributes to impaired vascular barrier function, inflammation, and lipid uptake, thereby promoting atherosclerotic lesion formation and progression. By elucidating the transcriptomic pathways regulated by endothelial CB1 and its broad influence on lipid uptake and metabolism in arteries, liver, and brown and white adipose tissue, our study provides insights into novel pathways and potential interventions for the treatment of atherosclerosis and metabolic disorders. The use of peripherally restricted CB1 antagonists that specifically target vascular inflammation and tissue lipid storage could be a complementary and safe therapeutic avenue to treat cardiovascular and metabolic disease comorbidities without altering CB1 signaling in the brain.

immunology↗

bioRxiv2024

Proactive distractor suppression in early visual cortex

Avoiding distraction by salient yet irrelevant stimuli is critical when accomplishing daily tasks. One possible mechanism to accomplish this is by suppressing stimuli that may be distracting such that they no longer compete for attention. While the behavioral benefits of distractor suppression are well-established, its neural underpinnings are not yet fully understood. In an fMRI study, we examined whether and how sensory responses in early visual areas show signs of distractor suppression after incidental learning of spatial statistical regularities. Participants were exposed to an additional singleton task where, unbeknownst to them, one location more frequently contained a salient distractor. We analyzed whether visual responses in terms of fMRI BOLD were modulated by this distractor predictability. Our findings indicate that implicit spatial priors shape sensory processing even at the earliest stages of cortical visual processing, evident in early visual cortex as a suppression of stimuli at locations which frequently contained distracting information. Notably, while this suppression was spatially (receptive field) specific, it did extend to nearby neutral locations, and occurred regardless of whether distractors, nontarget items or targets were presented at this location, suggesting that suppression arises before stimulus identification. Crucially, we observed similar spatially specific neural suppression even if search was only anticipated, but no search display was presented. Our results highlight proactive modulations in early visual cortex, where potential distractions are suppressed preemptively, before stimulus onset, based on learned expectations. Combined, our study underscores how the brain leverages implicitly learned prior knowledge to optimize sensory processing and attention allocation.

neuroscience↗

bioRxiv2023

High-level prediction errors in low-level visual cortex

Perception and behaviour are significantly moulded by expectations derived from our prior knowledge. Hierarchical predictive processing theories provide a principled account of the neural mechanisms underpinning these processes, casting perception as a hierarchical inference process. While numerous studies have shown stronger neural activity for surprising inputs, in line with this account, it is unclear what predictions are made across the cortical hierarchy, and therefore what kind of surprise drives this upregulation of activity. Here we leveraged fMRI and visual dissimilarity metrics derived from a deep neural network to arbitrate between two hypotheses: prediction errors may signal a local mismatch between input and expectation at each level of the cortical hierarchy, or prediction errors may incorporate feedback signals and thereby inherit complex tuning properties from higher areas. Our results are in line with this second hypothesis. Prediction errors in both low- and high-level visual cortex primarily scaled with high-level, but not low-level, visual surprise. This scaling with high-level surprise in early visual cortex strongly diverges from feedforward tuning, indicating a shift induced by predictive contexts. Mechanistically, our results suggest that high-level predictions may help constrain perceptual interpretations in earlier areas thereby aiding perceptual inference. Combined, our results elucidate the feature tuning of visual prediction errors and bolster a core hypothesis of hierarchical predictive processing theories, that predictions are relayed top-down to facilitate perception.

neuroscience↗

bioRxiv2023

An adversarial collaboration to critically evaluate theories of consciousness

Different theories explain how subjective experience arises from brain activity1,2. These theories have independently accrued evidence, yet, confirmation bias and dependence on design choices hamper progress in the field3. Here, we present an open science adversarial collaboration which directly juxtaposes Integrated Information Theory (IIT)4,5 and Global Neuronal Workspace Theory (GNWT)6-10, employing a theory-neutral consortium approach11,12. We investigate neural correlates of the content and duration of visual experience. The theory proponents and the consortium developed and preregistered the experimental design, divergent predictions, expected outcomes, and their interpretation12. 256 human subjects viewed suprathreshold stimuli for variable durations while neural activity was measured with functional magnetic resonance imaging, magnetoencephalography, and electrocorticography. We find information about conscious content in visual, ventro-temporal and inferior frontal cortex, with sustained responses in occipital and lateral temporal cortex reflecting stimulus duration, and content-specific synchronization between frontal and early visual areas. These results confirm some predictions of IIT and GNWT, while substantially challenging both theories: for IIT, a lack of sustained synchronization within posterior cortex contradicts the claim that network connectivity specifies consciousness. GNWT is challenged by the general lack of ignition at stimulus offset and limited representation of certain conscious dimensions in prefrontal cortex. Beyond challenging the theories themselves, we present an alternative approach to advance cognitive neuroscience through a principled, theory-driven, collaborative effort. We highlight the challenges to change peoples mind 13 and the need for a quantitative framework integrating evidence for systematic theory testing and building.

neuroscience↗

bioRxiv2023

ENVIRONMENT MODULATES PROTEIN HETEROGENEITY THROUGH TRANSCRIPTIONAL AND TRANSLATIONAL STOP CODON MISCODING

Stop codon miscoding events give rise to longer proteins, which may alter the proteins function and thereby generate short-lasting phenotypic variability from a single gene. In order to systematically assess the frequency and origin of stop codon miscoding events, we designed a library of reporters. We introduced premature stop codons into mScarlet that enabled high-throughput quantification of protein synthesis termination errors in E.coli using fluorescent microscopy. We found that under stress conditions, stop codon miscoding may occur with a rate as high as 80%, depending on the nucleotide context, suggesting that evolution frequently samples stop codon miscoding events. The analysis of selected reporters by mass spectrometry and RNA-seq showed that not only translation but also transcription errors contribute to stop codon miscoding. The RNA polymerase is more likely to misincorporate a nucleotide at premature stop codons. Proteome-wide detection of stop codon miscoding by mass spectrometry revealed that temperature regulates the expression of cryptic sequences generated by stop codon miscoding in E.coli. Overall, our findings suggest that the environment influences the accuracy of protein production, which increases protein heterogeneity when the organisms need to adapt to new conditions.

systems biology↗

bioRxiv2022

Associations of Socioeconomic Disparities With Buccal DNA-Methylation Measures Of Biological Aging

BackgroundIndividuals who are socioeconomically disadvantaged are at increased risk for aging-related diseases and perform less well on tests of cognitive function. The Weathering Hypothesis proposes that these disparities in physical and cognitive health arise from an acceleration of biological processes of aging. Theories of how life adversity is biologically embedded identify epigenetic alterations, including DNA methylation (DNAm), as a mechanistic interface between the environment and health. Consistent with the Weathering hypothesis and theories of biological embedding, recently developed DNAm algorithms have revealed profiles reflective of more advanced aging and lower cognitive function among socioeconomically-at-risk groups. These DNAm algorithms were developed using blood-DNA, but social and behavioral science research commonly collect saliva or cheek-swab DNA. This discrepancy is a potential barrier to research to elucidate mechanisms through which socioeconomic disadvantage affects aging and cognition. We therefore tested if social gradients observed in blood-DNAm measures could be reproduced using buccal-cell DNA obtained from cheek swabs. ResultsWe analyzed three DNAm measures of biological aging and one DNAm measure of cognitive performance, all of which showed socioeconomic gradients in previous studies: the PhenoAge and GrimAge DNAm clocks, DunedinPACE, and Epigenetic-g. We first computed blood-buccal cross-tissue correlations in n=21 adults (GEO111165). Cross-tissue correlations were low-to-moderate across (r=.25 to r=.48). We next conducted analyses of socioeconomic gradients using buccal DNAm data from SOEP-G (n=1128, 57% female; age mean=42 yrs, SD=21.56, range 0-72). Associations of socioeconomic status with DNAm measures of aging were in the expected direction, but were smaller as compared to reports from blood DNAm datasets (r=-.08 to r=-.13). ConclusionsOur findings are consistent with the hypothesis that socioeconomic disadvantage is associated with DNAm indicators of worse physical and cognitive health. However, relatively low cross-tissue correlations and attenuated effect-sizes for socioeconomic gradients in buccal DNAm compared with reports from analysis of blood DNAm suggest that, in order to take full advantage of buccal-DNA samples, DNAm algorithms customized to buccal DNAm are needed.

developmental biology↗

bioRxiv2022

Conceptual associations generate sensory predictions

A crucial ability of the human brain is to learn and exploit probabilistic associations between stimuli to facilitate perception and behavior by predicting future events. While studies have shown how perceptual relationships are used to predict sensory inputs, relational knowledge is often between concepts rather than percepts (e.g., we learned to associate cats with dogs, rather than specific images of cats and dogs). Here we asked if and how sensory responses to visual input may be modulated by predictions derived from conceptual associations. To this end we exposed participants to arbitrary word-word pairs (e.g., car - dog) repeatedly, creating an expectation of the second word, conditional on the occurrence of the first. In a subsequent session, we exposed participants to novel word-picture pairs, while measuring fMRI BOLD responses. All word-picture pairs were equally likely, but half of the pairs conformed to the previously formed conceptual (word-word) associations, whereas the other half violated this association. Results showed suppressed sensory responses throughout the ventral visual stream, including early visual cortex, to pictures that corresponded to the previously expected words compared to unexpected words. This suggests that the learned conceptual associations were used to generate sensory predictions that modulated processing of the picture stimuli. Moreover, these modulations were tuning-specific, selectively suppressing neural populations tuned towards the expected input. Combined, our results suggest that recently acquired conceptual priors are generalized across domains and used by the sensory brain to generate feature specific predictions, facilitating processing of expected visual input.

neuroscience↗

bioRxiv2022

Updating contextual sensory expectations for adaptive behaviour

The brain has the extraordinary capacity to construct predictive models of the environment by internalizing statistical regularities in the sensory inputs. The resulting sensory expectations shape how we perceive and react to the world; at the neural level, this relates to decreased neural responses to expected than unexpected stimuli ( expectation suppression). Crucially, expectations may need revision as context changes. However, existing research has often neglected this issue. Further, it is unclear whether contextual revisions apply selectively to expectations relevant to the task at hand, hence serving adaptive behaviour. The present fMRI study examined how contextual visual expectations spread throughout the cortical hierarchy as participants update their beliefs. We created a volatile environment with two state spaces presented over separate contexts and controlled by an independent contextualizing signal. Participants attended a training session before scanning to learn contextual temporal associations among pairs of object images. The fMRI experiment then tested for the emergence of contextual expectation suppression in two separate tasks, respectively with task-relevant and task-irrelevant expectations. Behavioural and neural effects of contextual expectation emerged progressively across the cortical hierarchy as participants attuned themselves to the context: expectation suppression appeared first in the insula, inferior frontal gyrus and posterior parietal cortex, followed by the ventral visual stream, up to early visual cortex. This applied selectively to task-relevant expectations. Taken together, the present results suggest that an insular and frontoparietal executive control network may guide the flexible deployment of contextual sensory expectations for adaptive behaviour in our complex and dynamic world. Significance statementThe world is structured by statistical regularities, which we use to predict the future. This is often accompanied by suppressed neural responses to expected compared with unexpected events ( expectation suppression). Crucially, the world is also highly volatile and context-dependent: expected events may become unexpected when the context changes, thus raising the crucial need for belief updating. However, this issue has generally been neglected. By setting up a volatile environment, we show that expectation suppression emerges first in executive control regions, followed by relevant sensory areas, only when observers use their expectations to optimise behaviour. This provides surprising yet clear evidence on how the brain controls the updating of sensory expectations for adaptive behaviour in our ever-changing world.

neuroscience↗