Biology subjects

Knight, R.

Publications and source records attributed to Knight, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Replication of Host St6galnac1-Microbiome Association Across Distinct Experimental Conditions in Heterogeneous Stock Rats

The gut microbiome is shaped by both environmental and host genetic factors. Identifying specific host loci is essential for understanding the heritability of microbiome-associated traits. The gene St6galnac1 encodes a sialyltransferase involved in mucin O-glycosylation. Tonnele et al. (2025) identified significant associations between a locus containing St6galnac1 gene, and the abundance of genus Paraprevotella using cecal samples collected from fasted animals. This association was replicated across three cohorts and paralleled with association found in humans between a functionally related sialyltransferase gene ST6GAL1 and Paraprevotella. Given the sensitivity of microbiome analyses to factors including sampling site, feeding status, and analytical framework, independent replication of these findings is important and it is also important to know if the association generalizes across other conditions or is highly specific to certain environments. Here, we performed a replication analysis using stool (not cecal) samples from 174 HS rats that were not fasted prior to collection. Stool samples underwent 16S rRNA gene sequencing, and microbial community structure was analyzed using compositionally aware methods. The rats were genotyped at the top SNP that was associated with Paraprevotella in the Tonnele et al. (2025) study. The association between genotype and Paraprevotella abundance was tested using a linear mixed model accounting for sex, cohort, and genetic relatedness. We successfully replicated (p=0.015) St6galnac1 - Paraprevotella association. These findings support the robustness of the St6galnac1 - Paraprevotella association across different sampling strategies and experimental conditions.

genomics↗

bioRxiv2026

STAMP-LME: A spatiotemporal surface-mapping framework for population-level iEEG

Intracranial EEG (iEEG) affords a unique opportunity to assess human neural activity with millisecond temporal resolution and millimeter spatial precision. However, group-level statistical analyses remain challenging due to sparse and heterogeneous electrode coverage, anatomical variability, and the difficulty of integrating spatiotemporal responses across individuals. Most iEEG studies therefore define brain regions of interest for the group analysis, sacrificing a spatial resolution strength of iEEG and excluding data outside those regions of interest. Here, we present a spatiotemporal analysis and mapping with permutation-based linear mixed-effects models (STAMP-LME), a cortical-mapping framework for population-level iEEG analysis independent of predefined cortical regions of interest. STAMP-LME is a complete processing pipeline spanning electrode localization, mapping to a standardized surface of the cortical mantle, including medial, orbitofrontal, and insular regions, and statistical inference at the cortical level. Electrode-level data from each participant is mapped to a standardized cortical surface using weights that account for electrode-to-cortex distance and confidence in cortical sampling. Brain signals are then combined with a spatial smoothing kernel and entered into LME models at each vertex-node and time point. Data-derived null distributions are generated through controlled permutations and used for spatiotemporal inference with threshold-free cluster enhancement (TFCE) or false-discovery-rate (FDR) correction. We validated STAMP-LME using simulations with known cortical ground truth while varying effect size, interparticipant source-location variability, electrode coverage, and spatial smoothing. The framework reliably detected simulated effects and recovered their spatiotemporal distribution while maintaining low localization error and limiting false-positive detections across different sampling conditions. We then applied the method to intracranial recordings from nine individuals performing an auditory oddball task. The empirical analysis revealed distributed auditory cortical responses consistent with conventional channel-level analyses, while providing more fine-grained group-level activation maps across sampled cortical regions. STAMP-LME provides a flexible, open-source framework for data-driven population-level cortical iEEG analysis that preserves the spatiotemporal strengths of intracranial recordings while accounting for sparse and heterogeneous electrode coverage. The structured workflow enables reproducible and anatomically precise group-level inference on a standardized cortical surface and facilitates comparison with surface-based fMRI and source-localized M/EEG results, therefore providing a practical tool for cognitive and clinical neuroscience research.

neuroscience↗

bioRxiv2026

Genome-resolved ecology and evolution of the microbiome enabled by quantitative long-read metagenomics

Standard metagenomics resolves microbial communities only in relative terms and at coarse taxonomic resolution, obscuring how individual strains change in absolute abundance and evolve within a host. We present long-read quantiomics (LRQ), a quantitative metagenomics platform coupling long-read sequencing with plasmid-based internal standards to recover high-quality genomes, including from degraded or low-input stool, and measure absolute, genome-resolved strain abundances. We validate LRQ using large plasmid standards and bacterial spike-ins, then apply it to eight years of dense sampling from an individual with colonic Crohn's disease. LRQ resolves co-resident strains of the same species following opposing trajectories with inflammation. Escherichia coli, long treated as a single pathobiont, separates into two sub-populations with opposing inflammation responses; the inflammation-associated lineage carries an immune-evasion module of virulence-associated metal-acquisition, biofilm, capsule and toxin genes. We observe genome-wide adaptive sweeps on timescales matched to disease incidence. LRQ therefore converts long-read metagenomics from a genome-recovery tool into a quantitative framework providing absolute, strain-level measurement of microbial ecology and evolution.

microbiology↗

bioRxiv2026

Immersive display systems for simulating natural vision

Vision is traditionally studied using simplified stimuli presented briefly near the center of a flat display while subjects maintain visual fixation. This paradigm contrasts sharply with real-world vision, which is immersive, dynamic and strongly entrained to self-initiated actions. Traditional approaches have allowed researchers to systematically study the neural encoding of visual features and the influence of cognitive operations such as attention. However, other methods are needed to study more holistic and first-person perspectives on vision, such as those related to physical space, continuous time, and self-movement. To enable the study of these and other aspects of real-world vision, we developed hemispherical ("dome") display systems for macaque visual neuroscience. For functional MRI experiments, a compact rear-projection dome display fits within the bore of a clinical MRI scanner. For electrophysiological recordings, a larger front-projection dome display is illuminated from above using a spherical mirror. Both setups enable complete and dynamic stimulation of approximately 180{degrees} of the subjects field of view, thus facilitating studies requiring visual immersion. To ensure accurate angular geometry across the hemispherical display, we present unified rendering and calibration software that supports natural fisheye videos, conventional visual stimuli, and virtual 3D environments. The calibration procedure automatically compensates for projector, mirror, and dome distortions through geometric pre-warping, ensuring correct visual-angle representation across the display. Pilot fMRI experiments demonstrate robust activation of peripheral visual cortex during both conventional pattern stimulation and naturalistic self-movement. Together, these dome systems provide a flexible platform for investigating aspects of vision that are difficult to study with conventional displays, including peripheral processing, visual immersion, optic flow, self-motion, and holistic scene perception.

neuroscience↗

bioRxiv2026

Alkamines reveal a hidden layer of steroid and drug metabolism

Biomedical research overlooks most genes in favor of a well-studied minority, yet whether analogous blind spots exist in metabolomics remains unknown. We show that reductive amination, forming secondary amines from aldehydes or ketones and amines, generates a previously hidden class of metabolites we term alkamines. Multiplexed synthesis of 8,475 alkamines combined with MS/MS searches across 1.7 billion spectra identified 1,626 candidates across multiple species and organs. Of these, 56 were confirmed in biological samples, including 27 steroid- and 12 drug-derived alkamines matching prescription patterns. Notably, 77% of synthesized alkamines are absent from PubChem. This combinatorial logic likely explains why alkamines have evaded detection and suggests drug metabolism frameworks substantially underestimate drug-derived metabolite diversity. Reductive amination is an overlooked route modifying steroids, bile acids, and xenobiotics.

biochemistry↗

bioRxiv2026

An expansive animal gut microbiome dataset elucidates major compositional shifts across bilaterian evolution

Animal gut microbiomes provide key physiological functions and are critical for host health. They vary dramatically across the animal kingdom, and are shaped by factors including host diet, evolutionary history and environment. However, analyses of gut microbiomes spanning the entire metazoan clade are lacking, limiting our understanding of the fundamental principles governing gut microbiomes. Here we present the Gut Microbiome Tree of Life (GMToL), a curated 16S amplicon dataset of 17,366 samples from 1,553 host species across 26 host classes from 284 studies, enabling analysis of large-scale evolutionary trends. Using ancestral state reconstruction, we provide a critical baseline calculation of major compositional shifts in gut microbiomes throughout animal evolution. We show that the ancestral animal gut was likely dominated by Pseudomonadota. A major shift to Bacteroidota occurred during the evolution of tetrapods, followed by the emergence of Bacillota-dominated guts in mammals and birds. We identify conserved core gut microbes and demonstrate how GMToL can be leveraged to contextualize the evolutionary history of specific microbial taxa. Ultimately, this framework enables the predictive mapping of microbial symbionts across uncharacterized host lineages, and establishes a quantitative baseline for comparative microbiome research at scale.

microbiology↗

bioRxiv2026

Farnesoid X receptor-dependent microbiome-bile acid signaling mediates obstructive sleep apnea-induced atherosclerosis

Intermittent hypoxia and hypercapnia (IHC), a hallmark of obstructive sleep apnea (OSA), accelerates atherosclerosis, yet the underlying mechanisms remain unclear. The gut microbiota and metabolites, specifically bile acids, change with IHC and thus the bile acid receptor farnesoid X receptor (FXR) might mediate IHC-induced atherosclerosis. In this study, ApoE-/- and ApoE-/- FXR-/- mice were exposed to IHC or room air and fed with a high-fat, high-cholesterol diet for 10 weeks. Markers of atherosclerosis, fecal microbiome, and metabolome were then examined via Sudan IV staining, absolute abundance shotgun metagenomics, and untargeted liquid chromatography tandem mass spectrometry (LC-MS/MS). IHC markedly increased aortic atherosclerosis in ApoE-/-mice, an increase that was abolished by FXR deficiency. In addition, IHC reshaped gut microbial composition, promoting enrichment of bile acid-modifying taxa and increasing levels of microbial hydroxysteroid dehydrogenase (hsdh). The bile acid pool was also remodeled and associated with aortic atherosclerosis via FXR-dependent metabolic signals in ApoE-/- mice. Knockout of FXR disrupted microbiome shift under IHC and uncoupled microbial bile acid metabolism from vascular lesion development, thereby protecting against aortic atherosclerosis. These findings show that FXR has a central role in linking IHC, microbial bile acid metabolism, and cardiovascular pathology.

microbiology↗

bioRxiv2026

Antibiotic Exposure Through Human Milk Influences the Infant Gut Microbiome

Infant antibiotic treatment is associated with increased risk of developing non-communicable diseases, potentially through disruption of the gut microbiome. However, the impact of indirect antibiotic exposure via human milk remains largely unexplored. Here, we investigate a cohort (n=80) of antibiotic-treated breastfeeding mother-infant dyads and untreated matching controls using integrative multi-omics analyses of fecal, milk, and skin samples (n=1,455). Maternal antibiotic treatment was associated with different infant fecal microbiome and metabolome profiles, including lower abundance of Bacteroides, Lactobacillus, and Bifidobacterium, and higher levels of antimicrobial resistance gene reads. Further, fecal metabolic alterations associated with indirect antibiotic exposure were exacerbated by formula milk supplementation. In a subset of infants (n=61), indirect exposure was associated with higher body mass index (BMI). These findings suggest that maternal antibiotic treatment during lactation may influence the early-life infant gut microbiome with potential long-term implications.

microbiology↗

bioRxiv2026

A Multi-Omics Processing Pipeline (MOPP) for Extracting Taxonomic and Functional Insights from Metaribosome Profiling (metaRibo-Seq) data

Metaribosome profiling (metaRibo-Seq) enables genome-wide measurement of translation across complex microbial communities by sequencing ribosome-protected mRNA fragments, but the short length of these footprints creates substantial nonspecific mapping against large reference genome collections, leading to spurious taxonomic and functional assignments. Here we present MOPP (Multi-Omics Processing Pipeline), a modular reference-based workflow that denoises metaRibo-Seq data by leveraging matched metagenomic coverage breadth to identify genomes likely to be truly present in a sample before aligning metatranslatomic and optional metatranscriptomic reads. MOPP generates taxon-by-gene count tables across genomic, transcriptional and translational layers, enabling integrated downstream analyses of microbial function. We evaluated MOPP using a defined 79-member synthetic human gut community profiled by metagenomics and metaRibo-Seq. Coverage breadth filtering markedly improved detection accuracy relative to a standard baseline workflow, with performance remaining robust across a broad intermediate threshold range and peaking at 92-95% coverage breadth. At a 92% threshold, MOPP reduced the number of distinct detected operational genomic units by 99.4% while retaining 87.8% of aligned metaRibo-Seq reads on average, and increased the F1 score from 0.02 to 0.61. Residual false positives were predominantly attributable to genomes with extremely high nucleotide similarity to true community members, whereas false negatives were enriched among low-abundance taxa, indicating that remaining errors are driven primarily by biological similarity and detection limits rather than widespread nonspecific mapping. Together, these results establish MOPP as a high-throughput workflow for robust processing of metaRibo-Seq in the context of matched metagenomics and position it as a scalable framework for integrated taxonomic and functional analysis of microbial communities across genomic, transcriptional and translational layers.

bioinformatics↗

bioRxiv2026

Pan-repository analysis reveals a drug-activating function of microbial bile acid conjugation

Microbially modified bile acids shape host physiology by regulating nutrient absorption, glucose homeostasis, circadian rhythms and thermoregulation. Here we identify a previously unrecognized drug-activating function of microbial bile acid conjugation. By systematically mining human LC-MS/MS datasets across public repositories and linking uncharacterized bile acid spectra to health-associated metadata, we discovered conjugates of the >75-year-old anti-inflammatory drug 5-aminosalicylic acid (5-ASA) with primary and secondary bile acids, including cholic, deoxycholic and lithocholic acids. These bile acid-drug conjugates were detected specifically in individuals treated with 5-ASA or its prodrugs. Multiple gut bacteria, including members of the Bacteroidota and Bacillota, generated cholyl-5-ASA in vitro, and bile salt hydrolase-associated transaminase activity was required for conjugate formation. In a mouse model of colitis, cholyl-5-ASA was associated with reduced intestinal inflammatory pathology and showed markedly enhanced activation of PPAR-{gamma} in cell-based reporter assays compared with 5-ASA alone. Consistent with this activity, cholyl-5-ASA elicited selective immunophenotypic changes in CD4 T cells in vitro, including increased Foxp3+ regulatory T cells. Together with prior evidence that 5-ASA efficacy depends on the microbiome, these findings support a model in which microbial bile acid conjugation represents a key activation step for 5-ASA therapy. More broadly, this work demonstrates how pan-repository metabolomics can uncover previously unrecognized microbiome-dependent chemical functions with direct therapeutic relevance.

biochemistry↗

bioRxiv2026

Enabling Megascale Microbiome Analysis with DartUniFrac

We introduce a new algorithm, DartUniFrac, and a near-optimal implementation with GPU acceleration, up to three orders of magnitude faster than the state of the art and scaling to millions of samples (pairwise) and billions of taxa. DartUniFrac connects UniFrac with weighted Jaccard similarity and exploits sketching algorithms for fast computation. We benchmark DartUniFrac against exact UniFrac implementations, demonstrating that DartUniFrac is statistically indistinguishable from them on real-world microbiome and metagenomic datasets.

bioinformatics↗

bioRxiv2026

Multi-omics Analyses of Facial Skin in Acne Identify Distinct Microbial and Metabolic Features at Lesional and Non-lesional Sites

The microbial and biochemical landscape of clinically normal-appearing skin in individuals with acne remains uncharacterized. Here, we performed longitudinal multi-omics profiling of facial skin from 10 individuals with moderate acne and 10 healthy controls, integrating 16S rRNA gene sequencing, shotgun metagenomics, and untargeted metabolomics across lesional and non-lesional sites. Compositional tensor factorization revealed that non-lesional acne skin occupies a distinct intermediate state between healthy and lesional skin in both the microbiome and the metabolome. Machine learning models distinguished healthy from non-lesional acne skin with 70% accuracy, demonstrating that molecular dysbiosis occurs in areas of the skin without visible lesions. Non-lesional sites exhibited reduced microbial diversity, strain-level shifts in Corynebacterium and Lawsonella correlating with disease severity, and metabolic alterations, including elevated lipids and perturbed amino acid and dipeptide profiles. Microbe-metabolite co-occurrence network analyses revealed that healthy skin is enriched for protective metabolites such as urocanic acid, while acne-associated skin shows distinct co-occurrence patterns. These findings establish that acne represents a field effect disorder, with molecular alterations extending beyond visible lesions to encompass the entire facial skin ecosystem. This molecular signature of pre-lesional skin provides potential biomarkers for early intervention and suggests that effective acne treatment may require holistic approaches targeting the broader skin environment rather than individual lesions alone.

microbiology↗

bioRxiv2026

Integrated multi-omics analysis identifies microbial and metabolic signatures and drivers of CNS autoimmunity

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) driven by genetic and environmental determinants. The gut microbiome of people with MS (pwMS) is distinct and influences disease through immunomodulatory metabolite production. Circulating metabolites are altered in pwMS, but identifying microbial-metabolic drivers remains challenging. We previously showed that colonization by the gut commensal Limosilactobacillus reuteri (L. reuteri) exacerbates disease in the experimental autoimmune encephalomyelitis (EAE) model of MS, in a tryptophan-dependent manner. Here, we integrated microbiomic and metabolomic datasets from a longitudinal EAE study utilizing high and low tryptophan diets in mice colonized or not with L. reuteri. Gut microbiome dynamics under short- and long-term alterations in tryptophan bioavailability, were affected by diet, microbiome context, or disease. During short-term dietary intervention, L. reuteri colonization exerted a greater impact on microbiome composition than tryptophan bioavailability. With longer dietary exposure and EAE progression, high dietary tryptophan and L. reuteri colonization synergized to elicit profound microbiota changes, including alterations in Lachnospiraceae, Blautia, and Akkermansia. Integration of metabolomic and microbiomic datasets using joint Robust Aitchison PCA revealed clusters of associated metabolites and microbiota enriched for functional pathways, including bile acid and tryptophan metabolism. Metabolites outperformed microbiota in predicting EAE severity, identifying p-cresols and indoles as top disease-associated metabolites. Treatment with p-cresol or 3-indoleglyoxylic acid exacerbated EAE, enhanced proinflammatory T cell responses, and increased cerebellar pathology. These data demonstrate that dietary responses are shaped by gut microbiome composition and that integrated microbiomic-metabolomic analyses can identify drivers of disease worsening in MS. IMPORTANCEMS is a multifactorial disease influenced not only by genetics but also by environmental factors, potentially including diet and the composition of the gut microbiome. We show that interactions between diet and commensal gut microbiota profoundly impact levels of immunomodulatory systemic metabolites, including several that are associated with disease in pwMS. Importantly, we demonstrate that individual gut microbiota produced metabolites are sufficient to worsen disease in a mouse model of MS. Integration of gut microbiome and blood metabolite datasets combined with subsequent predictive modeling, may bolster biomarker identification and the capacity to predict disease severity in pwMS, as compared to performance of individual datasets alone. These findings highlight metabolites as key mediators linking diet and the gut microbiota to neuroinflammation. Importantly, this work suggests that targeting microbial metabolites or modifying diet-microbiome interactions may represent new strategies to reduce disease activity in MS and related autoimmune disorders.

bioinformatics↗

bioRxiv2026

When exploration replaces storage: how eye movements shape visual working memory

Visual working memory (VWM) is traditionally studied while constraining eye movements and limiting access to visual input, yet in natural vision humans constantly explore and resample their environment. Only a few studies have examined VWM utilization when participants were allowed to interact with the environment and found that participants often preferred to resample their environment rather than rely on VWM storage. However, since eye movements were not controlled in these studies, the link between VWM utilization and free visual exploration remained unknown. In two experiments (N = 40), we investigated how visual exploration shapes reliance on VWM versus perceptual input. Participants searched for a common target across two item sets and could either store multiple items for comparison or repeatedly resample the sets by switching between them. Results revealed that when switching was achieved through eye movements, participants consistently relied more on visual resampling and less on VWM; in contrast, when switching required a manual response, they shifted toward greater VWM use. This pattern persisted even when peripheral input was equated, suggesting that natural exploration through eye movements reduces the cognitive cost of acquiring visual information, leading to a strategic reduction in VWM use. Our findings challenge fixation-based approaches to VWM research and highlight the importance of studying cognition under ecological viewing conditions.

neuroscience↗

bioRxiv2025

Personalised whole-body modelling links gut microbiota to metabolic perturbations in Alzheimer's disease

The human gut microbiome has been linked to metabolic disturbances in Alzheimers disease (AD). However, the mechanisms by which gut microbes might influence metabolic dysfunction in AD remain poorly understood. Previously, we used constraint-based metabolic modelling to associate an increased risk of AD with altered production of microbiome-derived metabolites. In this study, we investigated whether these previous results can also be identified in AD patients. Therefore, we created personalised whole-body metabolic models from gut metagenomics samples from 34 AD dementia patients, 51 individuals with mild cognitive impairments, and 298 healthy controls. These in silico models were profiled to predict the metabolic influences of gut microbiomes on blood metabolites with previously reported alterations in AD. We found an increased capacity of AD host-microbiome co-metabolism to produce S-adenosyl-L-methionine, L-arginine, creatine, taurine, and formate in the blood of AD dementia patients and patients with mild cognitive impairments. The metabolic predictions were then mechanistically linked to gut microbial changes in AD. This analysis identified that increased relative abundances of Bacteroides uniformis and Bacteroides thetaiotamicron were key factors driving the predicted metabolic changes. Furthermore, the predicted altered microbial influences on blood metabolites were also associated with allelic variations in the APOE risk gene in healthy individuals, which confirmed our previous findings. In conclusion, we identified blood metabolites whose perturbations in AD may be influenced by gut microbiota and predicted the key microbial drivers for these metabolic influences. These findings may facilitate the development of microbiome-informed treatments of AD.

systems biology↗

bioRxiv2025

Brain Dynamics of Mental Manipulation

Humans effortlessly juggle their internal thoughts, but the neuronal dynamics that support mental manipulation are largely unknown. Leveraging the high spatiotemporal fidelity of intracranial recordings in humans (N = 30), we provide evidence that mental sound manipulation involves the inhibition of sensory cortex and the coordinated engagement of memory and control networks. This modulation manifests in two ways. First, there is a shift in the balance between faster (> 30 Hz) and slower (< 30 Hz) dynamics in primary and secondary auditory areas, suggesting a decrease in local excitability. Second, there is a distributed increase in oscillatory synchrony (6-10 Hz), which predicts imagery vividness and task performance. This evidence points to a key role of local excitability and inter-areal synchrony in the manipulation of thought.

neuroscience↗

bioRxiv2025

NLRP3 is a thermosensor that is negatively regulated by high temperature

Inflammation is an essential response to infection and injury, but unregulated inflammation is damaging and must be limited by negative feedback signalling. Inflammasome signalling drives local inflammation and systemic responses like fever. However, our understanding of how inflammasome signalling is negatively regulated is limited. NLRP3 is activated by a vast number of stimuli and senses perturbations of cytoplasmic homeostasis. As temperature is a fundamental environmental stressor, we hypothesised that NLRP3 inflammasome signalling would be sensitive to increased temperatures and so we investigated the effects of high temperatures on NLRP3 in macrophages. Short-term incubation at high fever range temperatures significantly inhibits NLRP3 activation, while secretion of the inflammasome-independent cytokines TNF and IL-6 are much less affected. High temperature blocks NLRP3 inflammasome formation in a transcription-independent manner, and NLRP3 is highly sensitive to temperature-mediated inhibition relative to the NLRC4, AIM2, and NLRP1 inflammasomes. Using cellular assays and molecular simulations we show that the effect of high temperature on NLRP3 is protein intrinsic. NLRP3 activation is associated with a decrease in the thermal stability of the protein and multiscale molecular dynamics simulations identified a peptide in the C-terminal of the FISNA domain (COFI) that is highly flexible and undergoes a significant conformational shift at high temperature. Cellular assays demonstrate that the COFI regulates NLRP3 stability and is required for activation. Furthermore, mice exposed to high temperature display attenuated inflammatory cytokine production upon in vivo LPS challenge. Our studies thus reveal that high temperatures associated with fever limit NLRP3 activity and identify a novel role for NLRP3 as a protein thermosensor.

immunology↗

bioRxiv2025

Predictive encoding of auditory sequences in the human prefrontal cortex

Humans extract regularities from the environment to form expectations that guide perception and optimize behavior. Although the prefrontal cortex (PFC) is central to this process, the relative contributions of orbitofrontal (OFC) and lateral PFC (LPFC) remain unclear. Here, we show that the brain tracks sound regularities in an auditory deviance detection task to predict when a target deviant will occur. Intracranial EEG in epilepsy patients reveals prefrontal engagement, with earlier expectancy-related modulation in OFC and later modulation in LPFC. Connectivity analyses indicate bidirectional and asymmetrical expectancy-related information exchange between the two areas, with a first lead by OFC, consistent with its role in initiating predictive encoding. Converging causal evidence shows that OFC lesions abolish sensitivity to expectancy, whereas LPFC lesions yield only modest effects not significantly different from controls. Together, these results provide electrophysiological and causal evidence for distinct, temporally organized contributions of prefrontal subregions to predictive processing. Significance StatementThe human brain builds probabilistic associations capturing the dynamic environmental structure in a predictive manner. The ability to encode and deploy predictive information to anticipate future events relies on the prefrontal cortex (PFC). However, the specific roles of PFC subregions in this process remain unclear. Here, we provide converging electrophysiological and causal lesion evidence on the distinct involvement of the orbital and lateral PFC in generating anticipatory neural signals based on probabilistic auditory structure.

neuroscience↗