Biology subjects

Mei, N.

Publications and source records attributed to Mei, N..

14 recordsLinked to original sources

bioRxiv2026

Neural Representations of Ensemble Mean and Variance Across Visual Features

Humans can rapidly extract summary statistics, such as the mean and variance of visual features, to efficiently represent complex visual environments despite limits in attention and working memory. For example, when viewing a field of flowers, we can perceive the average colour and overall variability of the display without individuating each flower. Although ensemble perception is central to visual cognition, two fundamental questions remain unresolved. First, it is unclear whether ensemble statistics for features represented at different levels of the visual hierarchy rely on a common neural system or on separate feature-specific systems. Second, it remains unknown whether different summary statistics, such as mean and variance, rely on shared or dissociable neural mechanisms. Here, we used fMRI and multivariate pattern analysis to examine the neural representation of ensemble mean and variance across three visual features spanning the processing hierarchy: orientation (low-level), shape (mid-level), and animacy (high-level) (N = 24; two fMRI sessions). By combining whole-brain searchlight and ROI-based approaches, we found a graded division of labour between ventral and dorsal visual pathways. Although mean and variance ensemble statistics were distributed across the visual cortex, mean decoding was stronger in ventral regions, whereas variance decoding was stronger in dorsal regions. Ensemble mean representations followed a posterior-anterior gradient within the ventral visual pathway, consistent with increasing abstraction from orientation to shape and animacy, and showed little anatomical overlap suggesting largely feature-specific. By contrast, ensemble variance was weighted toward dorsal parietal and frontoparietal regions, especially superior parietal cortex and intraparietal sulcus, decoding clusters largely overlap across features and generalized robustly across orientation, shape, and animacy. Together, these findings provide a more nuanced account of ensemble perception, showing that feature-specific and feature-independent neural codes can coexist across visual cortex and help reconcile previously conflicting evidence. Significance StatementThis study shows that information about ensemble mean and variance is distributed throughout the visual cortex but weighted differently across visual pathways. Mean-related information was stronger in the ventral visual pathway and showed a largely feature-independent anatomical organization, whereas variance-related information was stronger in dorsal parietal regions and generalized across orientation, shape and animacy. Together, these findings provide clarifying neural evidence for longstanding debates about whether ensemble statistics rely on shared or dissociable mechanisms and whether ensemble representations are feature-specific or generalize across visual features. More broadly, the study offers a nuanced account of ensemble perception in which feature-specific and feature-general neural codes coexist across visual cortex, potentially reconciling previously conflicting evidence.

neuroscience↗

bioRxiv2025

Orthogonal representational geometry in dACC underpins human hierarchical reasoning

Flexible adaptation requires inferring the causes of feedback and adjusting accordingly. When tasks are hierarchically structured with high-level rules governing low-level perceptual judgements, good performance requires hierarchical reasoning. Yet the computational and neural mechanisms underlying this process remain unclear. Here, building on Bayesian modelling and recurrent neural network simulation of hierarchical reasoning, we verified that such algorithm and representations are implemented in human dorsal anterior cingulate cortex (dACC). dACC encodes accumulated errors and perceptual difficulty to estimate high-level rule switch confidence. These two variables were represented along two separable dimensions, and could be approximated by Gaussian basis functions. Orthogonality between these two dimensions yielded an optimal two-dimensional representation for hierarchical reasoning. A closer-to-orthogonal representational geometry also predicted better performance. Together, these findings provide an integrated computational and neural representational basis for hierarchical reasoning.

neuroscience↗

bioRxiv2025

Membrane binding promotes oligomerization and functional activation of viral RNA-dependent RNA polymerase

Positive-sense, single-stranded RNA (ssRNA+) viruses initiate infection by utilizing host ribosomes to directly translate proteins from their genomes. One of these proteins is the RNA-dependent RNA polymerase (RdRp), which is responsible for replicating the viral genome. Hepatitis C virus (HCV), a well-studied ssRNA+ virus, encodes a membrane-bound RdRp, here termed NS5B, that can form oligomers. NS5B exists in two conformations; only one is believed to be capable of binding RNA. The relationship between membrane localization, oligomerization, and conformation remains unclear. We investigated whether membrane localization mediates NS5Bs oligomerization, conformation and function. In solution, NS5B exhibited temperature-dependent oligomerization that correlated with cooperative RNA binding and a conformational shift at micromolar concentrations. In contrast, total internal reflection fluorescence microscopy and atomic force microscopy revealed that membrane-bound NS5B forms stable, non-terminating oligomers at nanomolar concentrations, often localized near lipid raft domains. The presence of oligomers was correlated with membrane recruitment of RNA and nucleotides, suggesting functional activation. The lower concentration threshold for RNA binding on membranes may be explained by membrane-induced oligomerization that promotes the RNA-binding conformation of NS5B. Our findings are consistent with a model in which membrane association regulates NS5Bs function through oligomerization-driven conformational control.

microbiology↗

bioRxiv2024

Brain representation in conscious and unconscious vision

The development of robust frameworks to understand how the human brain represents conscious and unconscious perceptual contents is paramount to make progress in the neuroscience of consciousness. Recent functional MRI studies using multi-voxel pattern classification analyses showed that unconscious contents could be decoded from brain activity patterns. However, decoding does not imply a full understanding of neural representations. Here we re-analysed data from a high-precision fMRI study coupled with representational similarity analysis based on convolutional neural network models to provide a detailed information-based approach to neural representations of both unconscious and conscious perceptual content. The results showed that computer vision model representations strongly predicted brain responses in ventral visual cortex and in fronto-parietal regions to both conscious and unconscious contents. Moreover, this pattern of results generalised when the models were trained and tested with different participants. Remarkably, these observations results held even when the analysis was restricted to observers that showed null perceptual sensitivity. In light of the highly distributed brain representation of unconscious information, we suggest that the functional role of fronto-parietal cortex in conscious perception is unlikely to be related to the broadcasting of information, as proposed by the global neuronal workspace theory, and may instead relate to the generation of meta-representations as proposed by higher-order theories.

neuroscience↗

bioRxiv2023

PyDecNef: An open-source framework for fMRI-based decoded neurofeedback

Real time fMRI research has suffered from inaccessible analysis pipelines, hindering collaboration and reproducibility. Here we present PyDecNef, a Python-based platform designed to advance real-time fMRI analysis and fuel exploration of close-loop neuroimaging for cognitive neuroscience studies. Creating a real-time fMRI analysis pipeline from scratch poses formidable technical challenges, involving data transfer, experimental software, and machine learning classifier preparation. Existing tools like FRIEND, Brain-Voyant, and OpenNFT demand expensive licenses or rely on proprietary software, impeding accessibility and customizability. PyDecNef offers a solution: a transparent, versatile, and open workflow for real-time fMRI decoding protocols. This open-source platform simplifies decoder construction, real-time preprocessing, decoding, and feedback signal generation. It also supports co-adaptive decoders that update in real time based on decoded neurofeedback performance, enabling researchers to conduct more precise and efficient DecNef experiments. Moreover, its openness promotes collaboration, enhancing research quality, replicability, and impact. With PyDecNef, the path to advancing DecNef studies becomes more accessible and collaborative. PyDecNef resources for real-time fMRI analysis can be found at https://github.com/pydecnef/PydecNef.

neuroscience↗

bioRxiv2023

Unconscious manipulation of conceptual representations with decoded neurofeedback impacts search behaviour

The necessity of conscious awareness in human learning has been a long-standing topic in psychology and neuroscience. Previous research on non-conscious associative learning is limited by the low signal-to-noise ratio of the subliminal stimulus, and the evidence remains controversial, including failures to replicate. Using functional MRI decoded neurofeedback (fMRI-DecNef) we guided participants from both sexes to generate neural patterns akin to those observed when visually perceiving real-world entities (e.g., dogs). Importantly, participants remained unaware of the actual content represented by these patterns. We utilized an associative DecNef approach to imbue perceptual meaning (e.g., dogs) into Japanese hiragana characters that held no inherent meaning for our participants, bypassing a conscious link between the characters and the dogs concept. Despite their lack of awareness regarding the neurofeedback objective, participants successfully learned to activate the target perceptual representations in the bilateral fusiform. The behavioural significance of our training was evaluated in a visual search task. DecNef and control participants searched for dogs or scissors targets that were pre-cued by the hiragana used during DecNef training or by a control hiragana. The DecNef hiragana did not prime search for its associated target but, strikingly, participants were impaired at searching for the targeted perceptual category. Hence, conscious awareness may function to support higher-order associative learning. Meanwhile, lower-level forms of re-learning, modification, or plasticity in existing neural representations can occur unconsciously, with behavioural consequences outside the original training context. The work also provides an account of DecNef effects in terms of neural representational drift. Significance StatementThis study examined the role of conscious awareness in human learning by using fMRI-DecNef. These techniques enabled participants to self-regulate their brain activity to align with the perceptual representations generated by a real-world entity (i.e., dogs), without awareness of the content they represented. We demonstrated that established brain conceptual representations can be unconsciously modified, influencing visual search behaviour for the targeted perceptual content through the neural representational drift mechanism. Nonetheless, our research suggests that conscious awareness plays a role in more advanced forms of associative learning. Further, this study offers methodological insights for improving DecNef protocols and suggests potential for personalized interventions, including guidance to correct maladaptive conceptual representations.

neuroscience↗

bioRxiv2023

Stimulus novelty uncovers coding diversity in visual cortical circuits

Detecting novel stimuli in the environment is critical for learning and survival, yet the neural basis of novelty processing is not understood. To characterize cell type-specific novelty processing, we surveyed the activity of [~]15,000 excitatory and inhibitory neurons in mice performing a visual task with novel and familiar stimuli. Clustering revealed a dozen functional neuron types defined by experience-dependent encoding. Vasoactive-intestinal-peptide (Vip) expressing inhibitory neurons were diverse, encoding novel stimuli, omissions of familiar stimuli, or behavioral features. Distinct Somatostatin (Sst) expressing inhibitory neurons encoded either familiar or novel stimuli. Subsets of excitatory neurons co-clustered with specific Vip or Sst subpopulations, while Sst and Vip inhibitory clusters were non-overlapping. This study establishes that novelty processing is mediated by diverse functional neuron types in the visual cortex.

neuroscience↗

bioRxiv2022

Assessing the brain representation of conscious and unconscious visual contents using encoding based representational similarity analysis

The development of novel frameworks to understand the properties of unconscious representations and how they differ from the conscious counterparts may be critical to make progress in the neuroscience of vision consciousness. Here we re-analysed data from a within-subject, high-precision, highly-sampled fMRI study (N=7) coupled with model-based representational similarity analysis (RSA) in order to provide an information-based approach to study the representation of conscious and unconscious visual contents The standard whole-brain searchlight RSA revealed that the hidden representations of convolutional neural network models explained brain activity patterns in response to unconscious contents in the ventral visual pathway in the majority of the observers, particularly for models that ranked high in explaining the variance of the visual cortex (i.e., VGGNet and ResNet50). Also five of seven subjects showed brain activity patterns that correlated with the model in frontoparietal areas in the unconscious trials. However, the results of an encoding-based RSA analyses in the unconscious condition were mixed and somehow difficult to interpret, including negative correlations between the representations of the computer vision models and the brain activity in frontal areas in a substantial amount of the observers.

neuroscience↗

bioRxiv2021

Informative neural representations of unseen objects during higher-order processing in human brains and deep artificial networks

A framework to pinpoint the scope of unconscious processing is critical to improve our models of visual consciousness. Previous research observed brain signatures of unconscious processing in visual cortex but these were not reliably identified. Further, whether unconscious content is represented in high-level stages of the ventral visual stream and linked parieto-frontal areas remains unknown. Using a within-subject, high-precision fMRI approach, we show that unconscious contents can be decoded from multivoxel patterns that are highly distributed alongside the ventral visual pathway and also involving parieto-frontal substrates. Classifiers trained with multivoxel patterns of conscious items generalised to predict the unconscious counterparts, indicating that their neural representations overlap. These findings suggest revisions to models of consciousness such as the neuronal global workspace. We then provide a computational simulation of visual processing/representation without perceptual sensitivity by using deep neural networks performing a similar visual task. The work provides a framework for pinpointing the representation of unconscious knowledge across different task domains.

neuroscience↗

bioRxiv2020

Melatonin alters fluid phase co-existence in POPC/DPPC/cholesterol membranes

The structure and biophysical properties of lipid biomembranes are important for normal function of plasma and organelle membranes, which is essential for proper functioning of living cells. In Alzheimers disease (AD) the structure of neuronal membranes becomes compromised by the toxic effect of amyloid-{beta} (A{beta}) protein which accumulates at neuron synapses, resulting in membrane perforation and dysfunction, oxidative stress and cell death. Melatonin is an important pineal gland hormone that has been shown to be protective against A{beta} toxicity in cellular and animal studies, but the molecular mechanism of this protection is not well understood. It has been shown that melatonin can interact with model lipid membranes and alter the membrane biophysical properties, such as membrane molecular order and dynamics. This effect of melatonin has been previously studied in simple model bilayers with one or two lipid components, we consider a more complex ternary lipid mixture as our membrane model. In this study, we used 2H-NMR to investigate the effect of melatonin on lipid phase behaviour of a three-component model lipid membranes composed of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC), 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) and cholesterol. We used deuterium labelled palmitoyl-d31 in POPC-d31 and DPPC-d62 separately, to probe the changes in hydrocarbon chain order as a function of temperature and varying concentrations of melatonin. We found that melatonin concentration influences phase separation in these ternary mixtures somewhat differently depending on whether POPC-d31 or DPPC-d62was used. At 5 mol% melatonin we observed phase separation in samples with POPC-d31, but not with DPPC-d62. However, at 10 mol% melatonin phase separation was observed in both samples with either POPC-d31 or DPPC-d62. These results indicate that melatonin can have a strong effect on membrane structure and physical properties, which may provide some clues to understanding how melatonin protects against A{beta}. SIGNIFICANCEMelatonin has been shown to be protective against A{beta} pathology in animal and cellular studies. Although the mechanism of this protection is not well-understood, melatonins membrane-active properties may be important in this regard. In this work solid-state deuterium nuclear magnetic resonance was used to study the effect of melatonin on the POPC/DPPC/cholesterol model membranes. Specifically, we showed that melatonin modifies lipid hydrocarbon chain order to promote phase separation. This knowledge helps to explain the role of melatonin in lipid domain formation and may provide a deeper understanding of the mechanism of melatonin neuroprotection in AD.

biophysics↗

bioRxiv2020

Behavioral features of motivated response to alcohol in Drosophila

Animals avoid predators and find the best food and mates by learning from the consequences of their behavior. However, reinforcers are not always uniquely appetitive or aversive but can have complex properties. Most intoxicating substances fall within this category; provoking aversive sensory and physiological reactions while simultaneously inducing overwhelming appetitive properties. Here we describe the subtle behavioral features associated with continued seeking for alcohol despite aversive consequences. We developed an automated runway apparatus to measure how Drosophila respond to consecutive exposures of a volatilized substance. Behavior within this Behavioral Expression of Ethanol Reinforcement Runway (BEER Run) demonstrated a defined shift from aversive to appetitive responses to volatilized ethanol. Behavioral metrics attained by combining computer vision and machine learning methods, reveal that a subset of 9 classified behaviors and component behavioral features associate with this shift. We propose this combination of 9 behaviors can be used to navigate the complexities of operant learning to reveal motivated goal-seeking behavior.

animal behavior and cognition↗

bioRxiv2019

Decoding and encoding models reveal how the depth of processing shapes the brain representation of meaning

How the brain representation of conceptual knowledge vary as a function of processing goals, strategies and task-factors remains a key unresolved question in cognitive neuroscience. Here we asked how the brain representation of semantic categories is shaped by the depth of processing during mental simulation. Participants were presented with visual words during functional magnetic resonance imaging (fMRI). During shallow processing, participants had to read the items. During deep processing, they had to mentally simulate the features associated with the words. Multivariate classification, informational connectivity and encoding models were used to reveal how the depth of processing determines the brain representation of word meaning. Decoding accuracy in putative substrates of the semantic network was enhanced when the depth processing was high, and the brain representations were more generalizable in semantic space relative to shallow processing contexts. This pattern was observed even in association areas in inferior frontal and parietal cortex. Deep information processing during mental simulation also increased the informational connectivity within key substrates of the semantic network. To further examine the properties of the words encoded in brain activity, we compared computer vision models - associated with the image referents of the words - and word embedding. Computer vision models explained more variance of the brain responses across multiple areas of the semantic network. These results indicate that the brain representation of word meaning is highly malleable by the depth of processing imposed by the task, relies on access to visual representations and is highly distributed, including prefrontal areas previously implicated in semantic control.

neuroscience↗

bioRxiv2019

Machine learning predicts human prospective decision making

Metacognition can be deployed retrospectively -to reflect on the correctness of our behavior- or prospectively -to make predictions of success in ones future behavior or make decisions about strategies to solve future problems. We investigated the factors that determine prospective decision making. Human participants performed a visual discrimination task followed by ratings of visibility and response confidence. Prior to each trial, participants made prospective judgments. In Experiment 1, they rated their belief of future success. In Experiment 2, they rated their decision to adopt a focused attention state. Prospective beliefs of success were associated with no performance changes while prospective decisions to engage attention were followed by better self-evaluation of the correctness of behavioral responses. Using standard machine learning classifiers we found that the current prospective decision could be predicted from information concerning task-correctness, stimulus visibility and response confidence from previous trials. In both Experiments, awareness and confidence were more diagnostic of the prospective decision than task correctness. Notably, classifiers trained with prospective beliefs of success in Experiment 1 predicted decisions to engage in Experiment 2 and vice-versa. These results indicate that the formation of these seemingly different prospective decisions share a common, dynamic representational structure.

neuroscience↗

bioRxiv2017

Visual Continuous Recognition Reveals Widespread Cortical Contributions to Scene Memory

Humans have a remarkably high capacity and long duration memory for complex scenes. Previous research documents the neural substrates that allow for efficient categorization of scenes from other complex stimuli like objects and faces, but the spatiotemporal neural dynamics underlying scene memory are less well understood. In the present study, we used high density EEG during a visual continuous recognition task in which new, old, and scrambled scenes consisting of color outdoor photographs were presented at an average rate 0.26 Hz. Old scenes were single repeated presentations occurring within either a short-term ([≤] 20 seconds) or longer-term intervals of between 30 sec and 3 minutes or 4 and 10 minutes. Overall recognition was far above chance, with better performance at short- than longer-term intervals. A group ANOVA found parietal and frontal ERPs discriminated the three scene types as early as 59 ms after stimulus onset. Parietal ERPs were greater for old compared to new scenes by 189 ms, while fronto-temporal ERPs were greater for new compared to old scenes by 194 ms. For old scenes presented within longer-term intervals, parieto-temporal and centro-frontal ERPs were greater by 228 and 355 ms respectively compared to old scenes presented within a short-term interval. Supervised machine learning exhibited above-chance decoding of scene type by 275 ms. Single-subject BOLD-fMRI showed greater activity for old scenes across frontal, parietal, and temporal cortex. These converging findings show that a widespread network including parietal, frontal, and temporal regions supports short- and long-term scene memory.\n\nSignificance StatementThe ability to recognize a scene as novel or familiar is critical for basic cognition. Scene recognition plays an important role in episodic memory because it helps us quickly establish place, a first step in recalling where previous events occurred. Short-term recognition supports our ability to detect changes in the immediate environment, an ability critical to survival. Scene recognition after a longer-term interval is often the essential cue for retrieving autobiographical memories. Previous behavioral studies demonstrate high capacity and long duration scene memory. Neural studies have identified the brain regions that support scene-specific processing. The present study extends this research by filling a gap in understanding how distributed spatiotemporal patterns of neural activity support short- and long-term scene memory.

neuroscience↗