Biology subjects

Soto, D.

Publications and source records attributed to Soto, D..

At least 19 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

Inflammatory Oxidative Stress Compounds Inhibit Insulin Secretion through Rapid Protein Carbonylation

Pancreatic {beta}-cells in pre-type 1 diabetes (T1D) experience stress due to islet inflammation, which accompanies early defects in insulin secretion that precede autoimmune destruction. One product of inflammatory stress is protein carbonylation (PC), brought on by reactive oxygen species (ROS) combining with lipids to produce reactive aldehydes such as 4-hydroxynonenal (4-HNE) that irreversibly modify Cys, His, and Lys sidechains. In this study, we used proteomics to measure patterns of PC in pancreatic islets from 10-week-old pre-diabetic NOD mice and in cultured insulin-secreting cells treated with either 4-HNE or pro-inflammatory cytokines. All three stress conditions increased carbonylation of proteins central to {beta}-cell function including Rab GTPases and other proteins that are essential for vesicle trafficking. Gene ontology analysis indicates that the affected proteins and pathways in pre-diabetic NOD islets reflect a combination of those impacted by 4-HNE and cytokine treatment. Furthermore, both 4-HNE and cytokines significantly inhibited insulin secretion by [~]50% in cultured MIN6 and INS-1-GRINCH cells. In particular, exposure to 4-HNE for as little as 5 minutes suppressed insulin secretion and increased the carbonylation of over 1000 proteins. Overall, the observed PC pattern in pre-T1D islets is consistent with a model in which {beta}-cells experience multiple sources of oxidative stress, including ROS generation within {beta}-cells themselves and reactive compounds released by infiltrating immune cells. The latter exogenous source may represent a novel rapid mechanism for inhibiting insulin secretion.

biochemistry↗

bioRxiv2025

Co-adaptive training improves performance during fMRI decoded neurofeedback

A significant challenge for neurofeedback training research and related clinical applications, is participants difficulty in learning to induce specific brain patterns during training. Here, we address this issue in the context of fMRI-based decoded neurofeedback (DecNef). Arguably, discrepancies between the data used to construct the decoder and the data used for neurofeedback training, such as differences in data distributions and experimental contexts, neural and non-neural noise, are likely the cause of the difficulties of the aforementioned participants. Here, we developed a co-adaptation procedure using standard machine learning algorithms. The procedure involves an adaptive decoder algorithm that is updated in real time based on its predictions across neurofeedback trials. First, we tested the procedure via simulations using a previous DecNef dataset and showed that decoder co-adaptation can improve performance during neurofeedback training. Importantly, a drift analysis demonstrated the stability of the co-adapted decoder throughout the neurofeedback training sessions. We then collected real time fMRI data in a DecNef training procedure to provide proof of concept evidence that co-adaptation enhances participants ability to induce the target state during training. Thus, personalized decoders through co-adaptation can improve the precision and reliability of DecNef training protocols to target specific brain representations, with ramifications in translational research. The tools are made openly available to the scientific community.

neuroscience↗

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

Deep generative models predict SARS-CoV-2 Spike infectivity and foreshadow neutralizing antibody escape

Recurrent waves of SARS-CoV-2 infection, driven by the periodic emergence of new viral variants, highlight the need for vaccines and therapeutics that remain effective against future strains. Yet, our ability to proactively evaluate such therapeutics is limited to assessing their effectiveness against previous or circulating variants, which may differ significantly in their antibody escape from future viral evolution. To address this challenge, we develop a deep learning method to predict the effect of mutations on fitness and escape from neutralizing antibodies. We use this model to engineer 83 unique SARS-CoV-2 Spike proteins incorporating novel combinations of up to 46 amino acid changes relative to the ancestral B.1 variant. The designed constructs were infectious and evaded neutralization by nine well-characterized panels of human polyclonal anti-SARS-CoV-2 immune sera (from vaccinated, boosted, bivalent boosted, and breakthrough infection individuals). Designed constructs on contemporary SARS-CoV-2 strains displayed similar levels of antibody neutralization escape and similar antigenic profiles as variants seen subsequently (up to 12 months later) during the COVID-19 pandemic despite differences in exact mutations. Our approach provides targeted datasets of antigenically diverse escape variants for an early evaluation of the protective ability of vaccines and therapeutics to inhibit not only currently circulating but also future variants. This approach is generalizable to other viral pathogens.

immunology↗

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

Generalized and specialized brain representations of semantic knowledge in healthy aging

Aging is often associated with a decrease in cognitive capacities. However, semantic memory appears relatively well preserved in healthy aging. Both behavioral and neuroimaging studies support the view that changes in brain networks contribute to this preservation of semantic cognition. However, little is known about the role of healthy aging in the brain representation of semantic categories. Here we used pattern classification analyses and computational models to examine the neural representations of living and non-living word concepts. The results demonstrate that brain representations of animacy in healthy aging exhibit increased similarity across categories, even across different task contexts. This pattern of results aligns with the neural dedifferentiation hypothesis that proposes that aging is associated with decreased specificity in brain activity patterns and less efficient neural resource allocation. However, the loss in neural specificity for different categories was accompanied by increased dissimilarity of item-based conceptual representations within each category. Taken together, the age-related patterns of increased generalization and specialization in the brain representations of semantic knowledge may reflect a compensatory mechanism that enables a more efficient coding scheme characterized by both compression and sparsity, thereby helping to optimize the limited neural resources and maintain semantic processing in the healthy aging brain.

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↗

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↗

bioRxiv2022

LRRC37B is a species-specific regulator of voltage-gated channels and excitability in human cortical neurons

The enhanced cognitive abilities characterizing the human species result from specialized features of neurons and circuits, but the underlying molecular mechanisms remain largely unknown. Here we report that the hominid-specific gene LRRC37B encodes a novel receptor expressed in a subset of human cortical pyramidal neurons (CPNs). LRRC37B protein localizes at the axon initial segment (AIS), the specialized domain triggering action potentials. LRRC37B ectopic expression in mouse CPNs in vivo leads to reduced intrinsic excitability, a distinctive feature of some classes of human CPNs. At the molecular level, LRRC37B acts as a receptor for the secreted ligand FGF13A and interacts with the voltage gated sodium channel (VGSC) beta subunit SCN1B, thereby inhibiting the channel function of VGSC, specifically at the AIS. Electrophysiological recordings in adult human cortical slices reveals that endogenous expression of LRRC37B in human CPNs reduces neuronal excitability. LRRC37B thus acts as a species-specific modifier of human cortical neuron function, with important implications for human brain evolution and diseases.

neuroscience↗

bioRxiv2022

The cell cycle regulator cyclin D1 modulates the activity of α4-containing GABAA receptors

Cyclin D1 (Ccnd1){middle dot}Cdk4 complexes drive cell cycle progression through phosphorylation of pRb. Interestingly, Ccnd1 moves to the cytoplasm at the onset of differentiation in neuronal precursors. However, the cytoplasmic functions and targets of Ccnd1 in post-mitotic neurons are unknown. Here we identify the 4 subunit of gamma-aminobutyric acid (GABA) type A receptors (GABAARs) as an interactor and target of Ccnd1{middle dot}Cdk4. Ccnd1 binds to an intracellular loop in 4 and, together with Cdk4, phosphorylates the 4 subunit at threonine 423 and serine 431. These modifications increase the activity of 4-containing GABAARs, measured in whole-cell patch-clamp recordings, and upregulate its surface levels. In agreement with this role of Ccnd1{middle dot}Cdk4 in neuronal signaling, inhibition of Cdk4 decreases synaptic and extrasynaptic currents in the hippocampus of newborn rats. Moreover, CCND1 knockout mice display an altered pattern of dendritic spines, according to 4 functions in synaptic pruning. Overall, our findings molecularly link Ccnd1{middle dot}Cdk4 to GABAARs activity in the central nervous system and highlight a novel role for this G1 cyclin in neuronal signaling.

molecular biology↗

bioRxiv2022

Active and passive mechanics for rough terrain traversal in centipedes

Centipedes coordinate body and limb flexion to generate propulsion. On flat solid surfaces, the limb-stepping patterns can be characterized according to the direction in which limbaggregates propagate, opposite to (retrograde) or with the direction of motion (direct). It is unknown how limb and body dynamics are modified in terrain with terradynamic complexity more representative of their natural heterogeneous environments. Here, we investigated how centipedes that use retrograde and direct limp-stepping patterns, S. polymorpha and S. sexspinosus, respectively, coordinate their body and limbs to navigate laboratory environments which present footstep challenges and terrain rugosity. We recorded the kinematics and measured the locomotive performance of these animals traversing two rough terrains with randomly distributed step heights and compared the kinematics to those on a flat frictional surface. S. polymorpha exhibited similar body and limb dynamics across all terrains and a decrease in speed with increased terrain roughness. Unexpectedly, when placed in a rough terrain, S. sexspinosus changed the limb-stepping pattern from direct to retrograde. Further, for both species, traversal of rough terrains was facilitated by hypothesized passive mechanics: upon horizontal collision of a limb with a block, the limb passively bent and later continued the stepping pattern. While centipedes have many degrees of freedom. our results suggest these animals negotiate limb-substrate interactions and navigate complex terrains, by offloading complex control and leveraging the innate flexibility of their limbs.

animal behavior and cognition↗

bioRxiv2021

Expression of Chlamydomonas reinhardtii chloroplast diacylglycerol acyltransferase-3 is activated by light in concert with triacylglycerol accumulation.

Considerable progress has been made towards the understanding of triacylglycerol (TAG) accumulation in algae. One key aspect is finding conditions that trigger TAG production without reducing cell division. Previously, we identified a soluble diacylglycerol acyltransferase (DGAT), related to plant DGAT3, with heterologous DGAT activity. In this work, we demonstrate that Chlamydomonas reinhardtii DGAT3 localizes to the chloroplast and its expression is activated by light, in correspondence with TAG accumulation. Dgat3 mRNAs and TAGs increased in both wild type and starch-deficient cells grown with acetate upon transferring them from dark or low light to higher light. Light-activated DGAT3 expression and TAG accumulation depended on the preexisting levels of TAGs, suggesting the existence of a regulatory loop. These results indicate that DGAT3 could be responsible, at least in part, for light-dependent TAG accumulation. Moreover, our results present DGAT3 as a promising target of future studies oriented to the industrial applications of TAGs.

plant biology↗

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

RTP801 Is A Key Modulator For Motor Learning

RTP801/REDD1 is a stress-regulated protein whose upregulation is necessary and sufficient to trigger neuronal death in in vitro and in vivo models of Parkinsons and Huntingtons diseases and is up regulated in compromised neurons in human postmortem brains of both neurodegenerative disorders. Indeed, in both Parkinsons and Huntingtons disease mouse models, RTP801 knockdown alleviates motor-learning deficits. Here, we investigated the physiological role of RTP801 in neuronal plasticity. RTP801 is found in rat, mouse and human synapses. The absence of RTP801 enhanced excitatory synaptic transmission in both neuronal cultures and brain slices from RTP801 knock-out (KO) mice. Indeed, RTP801 KO mice showed improved motor learning, which correlated with lower spine density but increased basal filopodia and mushroom spines in the motor cortex layer V. This paralleled with higher levels of synaptosomal GluA1 and TrkB receptors in homogenates derived from KO mice motor cortex, proteins that are associated with synaptic strengthening. Altogether, these results indicate that RTP801 has an important role modulating neuronal plasticity in motor learning.

neuroscience↗

bioRxiv2020

Decoding social knowledge in the human brain

The present functional MRI study addressed how the brain maps different aspects of social information. We focused on two key dimensions of social knowledge: affect and likableness. Thirty participants were presented with audio definitions, half referring to affective (e.g. empathetic) and half to non-affective concepts (e.g. intelligent). Orthogonally, half of the concepts were highly likable (e.g. sincere) and half were socially undesirable (e.g. liar). We used a support vector machine to delineate how both concept dimensions are represented in a set of 9 a priori brain regions defined from previous meta-analyses on semantic and social cognition. We show that average decoding in semantic regions (e.g. lateral temporal lobe, inferior frontal gyrus, and precuneus) outperformed social ROIs (e.g. insula and anterior cingulate), with the lateral temporal lobe containing the highest amount of information about the affect and likableness of social concepts. We also found that the insula had a bias towards affect while the likableness dimension was better represented in anterior cingulate cortex. Our results do not support a modular view of social knowledge representation. They rather indicate that the brain representation of social concepts implicates a distributed network of regions that involves domain-specific social cognitive systems, but with a greater dependence on language-semantic processing.

neuroscience↗

bioRxiv2019

AMPAR/TARP stoichiometry differentially modulates channel properties

AMPARs control fast synaptic communication between neurons and their function relies on auxiliary subunits, which importantly modulate channel properties. Although it has been suggested that AMPARs can bind to TARPs with variable stoichiometry, little is known about the effect that this stoichiometry exerts on certain AMPAR properties. Here we have found that AMPARs show a clear stoichiometry dependent modulation although AMPARs still need to be fully saturated with TARPs to show some typical TARP-induced characteristics (i.e. an increase in channel conductance). We also have uncovered important differences in the stoichiometric modulation between calcium-permeable and calcium-impermeable AMPARs. Moreover, in heteromeric AMPARs, TARP positioning in the complex is important to exert certain TARP-dependent features. Finally, by comparing data from recombinant receptors with endogenous AMPAR currents from cerebellar granule cells, we have determined a likely functional stoichiometry of 2 TARPs associated with GluA2 subunits in the somatic AMPARs found in this cell type.

neuroscience↗

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↗