Biology subjects

Ding, M.

Publications and source records attributed to Ding, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Representational Competition of Spatially and Temporally Overlapped Target and Distractor

Representational competition occurs when a task-relevant target stimulus and a distractor overlap in space and time. Given limited neural resources, it is expected that stronger representations of the distractor will result in weaker representations of the target, leading to poorer behavioral performance. We tested this hypothesis by recording fMRI while participants (n = 27) viewed a compound stimulus consisting of randomly moving dots (the target) superimposed on IAPS affective pictures (the distractor). Each trial lasted [~]12 seconds, during which the moving dots and the IAPS pictures were flickered at 4.29 Hz and 6 Hz, respectively. The task was to detect and report brief episodes of coherent motion in the moving dots. Depending on the emotion category of the IAPS pictures, the trials were classified as pleasant, neutral and unpleasant. Focusing on three ROIs: middle temporal cortex (MT), ventral visual cortex (VVC), as well as the primary visual cortex (V1), we performed MVPA analysis to decode the distractor categories in each ROI, and correlated the decoding accuracy, taken to index the strength of distractor representation, with the accuracy in detecting the episodes of coherent motion. The following results were found: (1) the decoding accuracy was above chance level in all ROIs, and (2) in MT and VVC but not in V1, the higher the decoding accuracy, the worse the behavioral performance. These results suggested that distractor information was represented in V1 as well as in the two motion-processing areas, and in the motion-processing areas, stronger representations of the distractor led to poorer ability to process attended information, leading to worse behavioral performance. The hypothesis was thus supported and trade-offs in the fidelity of stimulus representations prompted by neural competition demonstrated.

neuroscience↗

bioRxiv2025

Global Mapping of Combinatorial Chromatin Regulatory Events Using Hi-Plex CUT&Tag

Epigenetic modulators, transcription factors, and chromatin-associated proteins collaboratively regulate essential genomic functions, such as transcription, repression, and DNA damage repair. However, the intricate cross-talk or the interaction between these chromatin regulatory factors (CRFs) remains poorly understood, largely due to limitations in experimental methods, which traditionally interrogate the genomic locations of only one CRF. Although the latest methods can study multiple CRFs simultaneously, they cannot examine the interaction between these factors. Inferring biological interactions often requires integrating data from multiple experiments, which can be labor-intensive and imprecise since the measurement is not from the same DNA molecule. To address these challenges, we developed Hi-Plex CUT&Tag, an advanced technology that enables the robust detection of co-localization events across over 600 pairs of CRFs using 36 barcoded monoclonal antibodies (mAbs), while minimizing background signals and cross-contamination. Hi-Plex CUT&Tag facilitates comprehensive pairwise analysis of epigenetic modifiers, including histone post-translational modifications (PTMs), writers, and transcription factors (TFs). Each Tn5 tagmentation fragment provides detailed insights, capturing the identities of two co-localized events, the underlying genomic sequences, and the molecular distance between them. For the first time, numerous novel bivalent events, epigenetic-context-dependent transcriptional regulation, and specific chromatin mark combinations with significant impacts on gene regulation can be detected in a single experiment. Furthermore, single-cell Hi-Plex CUT&Tag enables the analysis of synergistic interactions between CRF pairs at the single-cell level, providing unprecedented resolution for studying chromatin dynamics.

genetics↗

bioRxiv2025

MBOAT7 and CDS2 coordinate lipid droplet dynamics and cholesterol metabolism in liver

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a highly prevalent disorder worldwide characterized by the accumulation of hepatic lipid droplets (LDs). However, the mechanisms by which hepatocytes adapt to the dynamic changes in LD morphology to maintain metabolic homeostasis remains incompletely understood. Here, we show that MBOAT7, a known MASLD-associated protein, cooperates with CDS2 to regulate lipid droplet dynamics and lipid metabolism. Under physiological conditions, their interaction in the endoplasmic reticulum (ER) maintains metabolic homeostasis. Disruption of this interaction, such as through CDS2 knockdown or loss of function, triggers an adaptive response wherein MBOAT7 translocates to ER-LD contact sites in a RAB1-dependent manner. This redistribution inhibits DGAT2-mediated LD growth and promotes lipolysis. Our findings highlight the redistribution of MBOAT7 to ER-LD contacts serves as a critical mechanism for adaptive control LD size and lipid metabolic homeostasis.

cell biology↗

bioRxiv2025

Neural representation of emotional valence in human amygdala

The amygdala is a core structure for encoding the affective value of external stimuli. Animal studies suggest that positive and negative emotions are separately encoded by distinct neuronal populations within the amygdala; however, this hypothesis has rarely been tested in humans. The current study examined this hypothesis by comparing the distributed emotion encoding model, as proposed in animal studies, with the univariate emotion encoding model using functional magnetic resonance (fMRI) imaging. More specifically, we applied univariate regression, using average amygdala activation to represent global activation level, and multivariate regression, using distributed voxel-level pattern within the amygdala, to predict normative valence of affective images from the IAPS library. In the core amygdala, the multivariate models prediction performance was not better than that of the univariate model, with weight map analysis revealing an overwhelming predominance of voxels selectively responsive to negative stimuli. When the region of interest was expanded to include voxels with lower anatomical probability of belonging to the amygdala as well as voxels from adjacent areas, the multivariate model significantly outperformed the univariate model, with the voxels selectively responsive to positive valence primarily located in regions surrounding the core amygdala. These findings suggest that in the human amygdala, the core region encodes emotional valence primarily through a global activation signal, rather than distributed patterns consisting of separate clusters of positive and negative voxels, and a more distributed valence representation emerges when voxels surrounding the amygdala are taken into consideration.

neuroscience↗

bioRxiv2025

Concept2Brain: An AI model for predicting subject-level neurophysiological responses to text and pictures

The current growth of artificial intelligence (AI) tools provides an unprecedented opportunity to extract deeper insights from neurophysiological data while also enabling the reproduction and prediction of brain responses to a wide range of events and situations. Here, we introduce the Concept2Brain model, a deep network architecture designed to generate synthetic electrophysiological responses to semantic/emotional information conveyed through pictures or text. Leveraging AI solutions like CLIP from OpenAI, the model generates a representation of pictorial or language input and maps it into an electrophysiological latent space. We demonstrate that this openly available resource generates synthetic neural responses that closely resemble those observed in studies of naturalistic scene perception. The Concept2Brain model is provided as a web service tool for creating open and reproducible EEG datasets, allowing users to predict brain responses to any semantic concept or picture. Beyond its applied functionality, it also paves the way for AI-driven modeling of brain activity, offering new possibilities for studying how the brain represents the world.

neuroscience↗

bioRxiv2025

The rupture and independent extension of sister membranes in C. elegans embryos

Cell division, the physical separation of a cell into two daughter cells, is classically thought to require continuous plasma membrane invagination driven by equatorial contractile forces. Here, we challenge this paradigm by uncovering an unexpected mechanism in C. elegans embryos: the plasma membrane ruptures as early as metaphase. Instead of invaginating as a continuous sheet, the separated membranes extend independently to partition the cytoplasm. The membrane rupture and independent extension of sister membranes create membrane discontinuities--widely observed in electron microscopy (EM) sections. Without plasma membrane coverage, the exposed cytoplasmic regions are shielded either by plasma membrane from neighboring, likely kinship, cells or extracellular matrix components, preventing massive leakage. Our findings overturn the long-standing assumption that cytokinesis requires persistent membrane integrity, and offer fresh insights into cell division, particularly in fast dividing embryonic cells.

cell biology↗

bioRxiv2025

BifurcatoR: A Framework for Revealing Clinically Actionable Signal in Variance Masquerading as Noise

BackgroundDisease heterogeneity is a persistent challenge in medicine, complicating both research and treatment. Standard analytical pipelines often assume patient populations are homogeneous, overlooking variance patterns that may signal biologically distinct subgroups. Variance heterogeneity (VH)--including skewness, outliers, and multimodal distributions--offers a powerful but underused lens for detecting latent etiological structures relevant to prognosis and therapeutic response. MethodsA major barrier to VH analysis is the fragmented landscape of available methods, many of which rely on normality assumptions that biological data frequently violate. In addition, existing tools often require programming expertise, and clear guidance on study design considerations--such as sample size and method selection--is lacking. To address these issues, we developed BifurcatoR, an open-source software platform that simplifies the detection, modeling, and interpretation of VH. BifurcatoR integrates simulation-based method evaluation, study design recommendations, and a user-friendly web interface to support VH analysis across a range of data distributions. We benchmarked VH methods through simulation and applied BifurcatoR to two clinical datasets: acute myeloid leukemia (AML) and obesity. ResultsSimulation studies revealed that VH method performance is highly context-specific, varying with distribution shape, mean-variance coupling, and underlying subgroup structure. In AML, BifurcatoR identified two molecularly distinct subgroups with different treatment responses, including an EVI1-high group with significantly poorer prognosis (p < 0.005) among KMT2A-rearranged cases. In a separate study, VH analysis uncovered immunophenotypic subgroups in obesity based on gene-level discordance across monozygotic twin pairs, highlighting latent variation in adipose immune cell composition. ConclusionsVH is not "noise", biological variation without clinical relevance. Instead, VH is a structured signal that can reveal latent and clinically meaningful subtypes. BifurcatoR offers a practical, accessible framework for incorporating VH into biomedical research, with implications for biomarker discovery, patient stratification, and precision medicine.

bioinformatics↗

bioRxiv2025

Coherent thalamic inputs organize head direction signal in the medial entorhinal cortex

Successful navigation relies on signals that remain stable despite environmental changes. This stability can arise by constraining neuronal activity to low-dimensional subspaces. During sleep, when external input is reduced, pairwise coordination persists in the spatial navigation system, as observed for head-direction (HD) cells of the anterodorsal nucleus (ADn) and grid cells of the medial entorhinal cortex (MEC). As ADn is crucial for spatial representation in the MEC, we hypothesized that coherent HD input underlies MEC organization. To test this, we performed simultaneous recordings in the ADn and MEC during wakefulness and sleep. We found that HD cell pairs maintained stable coordination across both states, and that MEC cell coordination was partly driven by common inputs from ADn HD cells. These results suggest that MEC activity is shaped, in part, by coherent thalamic HD signals, supporting stable network organization.

neuroscience↗

bioRxiv2025

The Ventral Attention Network Mediates Attentional Reorienting to Cross-Modal Expectancy Violations: Evidence from EEG and fMRI

Our daily interactions with the world are shaped by sensory expectations informed by context and prior experiences, which in turn influence how we allocate our attention. Prominent predictive coding models suggest that sensory expectancy and attention interact but disagree on the precise mechanisms. One possibility is that the Ventral Attention Network (VAN), may play a role by facilitating attentional reorienting when expectancy is violated. To test this, we employed an auditory-visual trial-by-trial cueing paradigm in three experiments integrating EEG and fMRI to investigate the VANs role in violations of cross-modal expectancy. Behavioral results showed faster responses to expected targets, confirming the efficacy of cue-induced expectations in orienting attention to the expected target modality. EEG analyses revealed differences in early ([~]100 ms latency) event-related potentials (ERPs) to both auditory and visual stimuli when expectations were violated. Unexpected stimuli elicited significantly larger early-latency negative ERPs, across both modalities. Source localization of these ERPs and subsequent fMRI evidence revealed activation in the right VAN. Functional connectivity analyses further showed greater coupling between VAN regions and sensory cortices, with modality-specific pathways involving superior temporal gyrus (STG) for auditory and fusiform gyrus (FG) for visual targets. These findings demonstrate that expectancy violations recruit the VAN to reorient attention and resolve sensory conflict. By coordinating top-down control and bottom-up sensory input, the VAN supports adaptive responses to unexpected stimuli. This work advances our understanding of predictive processing in multisensory perception and highlights the VANs central role in flexible cognitive control.

neuroscience↗

bioRxiv2025

Robust single-trial estimates of electrocortical generalized aversive conditioning: Validation of a Bayesian multilevel learning model

Aversive conditioning changes visuocortical responses to conditioned cues, and the generalization of these changes to perceptually similar cues may provide mechanistic insights into anxiety and fear disorders. Yet, neuroimaging conditioning paradigms are challenged by poor single-trial signal-to-noise ratios (SNR), missing trials, and inter-individual differences in learning. Here, we address these issues with the validation of a steady-state visual evoked potential (ssVEP) generalization paradigm in conjunction with a Rescorla-Wagner inspired Bayesian multilevel learning model. A preliminary group of observers (N=24) viewed circular gratings varying in grating orientation, with only one orientation paired with an aversive outcome (noxious electric pulse). Gratings were flickered at 15 Hz to evoke ssVEPs recorded with 31 channels of EEG in an MRI scanner. The multilevel structure of the Bayesian model learning model informs and constrains estimates per participant providing an interpretable generative model. It led to superior cross-validation accuracy and insights into individual participant dynamics than simpler models. It also isolates the generalized effects of conditioning, providing improved statistical certainty. Lastly, the present report demonstrates that missing trials are interpolated and weighted appropriately using the full models structure. This is a critical aspect for single-trial analyses of simultaneously recorded physiological measures, because each added measure will typically increase the number of trials missing a complete set of observations. The present technical report validates a limited version of a learning model to illustrate the utility of this analytical framework. It shows how models may be iteratively built and compared in a modern Bayesian workflow. Future models may use different conceptualizations of learning, allow integration of clinically relevant factors, and enable the fusion of different simultaneous physiological recordings.

neuroscience↗

bioRxiv2025

Alpha-frequency stimulation strengthens coupling between temporal fluctuations in alpha oscillation power and default mode network connectivity

Alpha (8-12 Hz) oscillations and default mode network (DMN) activity dominate the brains intrinsic activity in the temporal and spatial domains, respectively. They are thought to play crucial roles in the spatiotemporal organization of the complex brain system. Relatedly, both have been implicated, often concurrently, in diverse neuropsychiatric disorders, with accruing electroencephalogram/magnetoencephalogram (EEG/MEG) and functional magnetic resonance imaging (fMRI) data linking these two neural activities both at rest and during key cognitive operations. Prominent theories and extant findings thus converge to suggest a mechanistic relationship between alpha oscillations and the DMN. Here, we leveraged simultaneous EEG-fMRI data acquired before and after alpha-frequency transcranial alternating current stimulation (-tACS) and observed that -tACS tightened the dynamic coupling between spontaneous fluctuations in alpha power and DMN connectivity (especially, in the posterior DMN, between the posterior cingulate cortex and the bilateral angular gyrus). In comparison, no significant changes were observed for temporal correlations between power in other oscillatory frequencies and connectivity in other major networks. These results thus suggest an inherent coupling between alpha and DMN activity in humans. Importantly, these findings highlight the efficacy of -tACS in regulating the DMN, a clinically significant network that is challenging to target directly with non-invasive methods. Significance StatementAlpha (8-12 Hz) oscillations and the default mode network (DMN) represent two major intrinsic activities of the brain. Prominent theories and extant findings converge to suggest a mechanistic relationship between alpha oscillations and the DMN. Combining simultaneous electroencephalogram-functional-magnetic-resonance imaging (EEG-fMRI) with alpha-frequency transcranial alternating current stimulation (-tACS), we demonstrated tightened coupling between alpha oscillations and DMN connectivity. These results lend credence to an inherent alpha-DMN link. Given DMN dysfunctions in multiple major neuropsychiatric conditions, the findings also highlight potential utility of -tACS in clinical interventions by regulating the DMN.

neuroscience↗

bioRxiv2024

Pulmonary granuloma formation during latent Cryptococcus neoformans infection in C3HeB/FeJ mice involves progression through three immunological phases

Cryptococcus neoformans is a fungal pathogen that can cause lethal disease in immunocompromised patients. Immunocompetent host immune responses, such as formation of pulmonary granulomas, control the infection and prevent disseminated disease. Little is known about the immunological conditions establishing the latent infection granuloma in the lungs. To investigate this, we performed an analysis of pulmonary immune cell populations, cytokine changes, and granuloma formation during infection with a latent disease-causing clinical isolate in C3HeB/FeJ mice over 360 days. We found that latently infected mice progress through three phases of granuloma formation where different immune profiles dominate: an early phase characterized by eosinophilia, high IL-4/IL-13, and C. neoformans proliferation in the lungs; an intermediate phase characterized by multinucleated giant cell formation, high IL-1/IFN{gamma}, granuloma expansion, and increased blood antigen levels; and a late phase characterized by a significant expansion of T cells, granuloma condensation, and decreases in lung fungal burden and blood antigen levels. These findings highlight a complex series of immune changes that occur during the establishment of granulomas that control C. neoformans in the lungs and lay the foundation for studies to identify critical beneficial immune responses to Cryptococcus infections. IMPORTANCECryptococcus neoformans is a fungal pathogen that disseminates from the lungs to the brain to cause fatal disease. Latent C. neoformans infection in the lungs is controlled by organized collections of immune cells called granulomas. The formation and structure of Cryptococcus granulomas are poorly understood due to inconsistent human pathology results and disagreement between necrotic granuloma-forming rat models and non-necrotic granuloma-forming mouse models. To overcome this, we investigated granuloma formation during latent C. neoformans infection in the C3HeB/FeJ mouse strain which forms necrotic lung granulomas in response to other pathogens. We found that latent C. neoformans granuloma formation progresses through phases that we described as early, intermediate, and late with different immune response profiles and granulomatous characteristics. Ultimately, we show that C3HeB/FeJ mice latently infected with C. neoformans form non-necrotic granulomas and could provide a novel mouse model to investigate host immune response profiles.

microbiology↗

bioRxiv2024

Brain Aging Among Individuals with Classical Trigeminal Neuralgia

Trigeminal neuralgia (TN) is a complex orofacial neuropathic pain condition with limited understanding of underlying mechanisms and therapeutic options. Emerging evidence suggests the involvement of the brain in persons with TN including widespread brain changes when employing a widely used brain aging biomarker that estimates a predicted brain age difference or brain age gap. The aim of the present cross-sectional study was to assess the predicted brain age difference (brain-PAD) or brain age gap across two discrete TN subtypes (classical TN, and secondary/idiopathic TN) in comparison with age-and sex-matched pain-free controls and its association with several clinical and psychological characteristics. Thirty-four individuals diagnosed with Classical TN, 17 diagnosed with secondary/idiopathic TN were age- and sex-matched to pain-free controls (n=54). All participants underwent a T1 brain MRI and completed clinical and psychological measures. There were significant differences in brain-PAD among TN subtypes (ANCOVA p = 0.0078, effect size f2 = 0.282), with individuals diagnosed with Classical TN having a brain-PAD significantly greater than the controls by 3.87 years (p = 0.01, Bonferroni-corrected). There were no significant brain-PAD differences between secondary/idiopathic TN and pain-free controls. Brain-PAD had a significant positive association with both pain catastrophizing (p = 0.032) and pain-related anxiety (p = 0.041), but no significant association with disease duration (p = 0.519) or usual pain intensity (p = 0.443). We report here accelerated brain aging processes in patients with classical TN, but not in persons diagnosed with secondary/idiopathic TN. Our study replicates previous findings and adds to the literature that accelerated brain aging may not occur across all TN subtypes. Given the increased use of MRI for TN diagnostics, combined with our own recent work deriving our brain aging biomarker from clinical-grade scans, future studies within clinical settings are feasible and needed to understand this debilitating condition.

neuroscience↗

bioRxiv2024

Omega-3 fatty acid normalizes postsynaptic density proteins via miRNAs regulation in hippocampus and prevents DEHP-induced impairment of learning and memory in mice

AbstractsDEHP is the most widely used plasticizer in many products. However, growing evidence has indicated that DEHP may induce neurotoxicity. DEHP exposure affects mircoRNAs (miRNAs) expression in brain. A growing body of evidence suggests that nutrients and other bioactive food components prevent neurotoxicity through regulation of miRNA expression. Due to the increasing concern about the risks of DEHP to human health, we explored the neuroprotective effect of Omega-3 fatty acid (Omega-3FA) on subchronic DEHP-induced neurotoxicity in mice, and the potential involved miRNAs and their targets in the protective action of Omega-3FA against DEHP-induced neurotoxicity. Omega-3FA protected against the DEHP-induced impairment of learning and memory and alleviated the thinning of postsynaptic density (PSD) thickness in hippocampal synapses. We observed that there are fourteen up or down regulated miRNAs associated PSD in DEHP exposure which were normalized by Omega-3FA treatment. Protein targets in PSD of these differentially expressed miRNAs were predicted. Furthermore, the expression levels of protein mGluR5, Homer1, and NMDAR2B were carried out via Western blot, for further verifying PSD associated miRNAs targets are involved in neuroprotection of Omega-3FA against DEHP. These findings suggested that Omega-3FA protected DEHP-induced impairment of learning and memory as well as synaptic structure alteration in the hippocampus by regulating the expression of PSD associated miRNAs and their targets. Thus, Omega-3FA should be included in diet to prevent or suppress neurotoxicity caused by continuous exposure to DEHP.

neuroscience↗

bioRxiv2024

Perceptual Expertise and Attention: An Exploration using Deep Neural Networks

Perceptual expertise and attention are two important factors that enable superior object recognition and task performance. While expertise enhances knowledge and provides a holistic understanding of the environment, attention allows us to selectively focus on task-related information and suppress distraction. It has been suggested that attention operates differently in experts and in novices, but much remains unknown. This study investigates the relationship between perceptual expertise and attention using convolutional neural networks (CNNs), which are shown to be good models of primate visual pathways. Two CNN models were trained to become experts in either face or scene recognition, and the effect of attention on performance was evaluated in tasks involving complex stimuli, such as superimposed images containing superimposed faces and scenes. The goal was to explore how feature-based attention (FBA) influences recognition within and outside the domain of expertise of the models. We found that each model performed better in its area of expertise--and that FBA further enhanced task performance, but only within the domain of expertise, increasing performance by up to 35% in scene recognition, and 15% in face recognition. However, attention had reduced or negative effects when applied outside the models expertise domain. Neural unit-level analysis revealed that expertise led to stronger tuning towards category-specific features and sharper tuning curves, as reflected in greater representational dissimilarity between targets and distractors, which, in line with the biased competition model of attention, leads to enhanced performance by reducing competition. These findings highlight the critical role of neural tuning at single as well as network level neural in distinguishing the effects of attention in experts and in novices and demonstrate that CNNs can be used fruitfully as computational models for addressing neuroscience questions not practical with the empirical methods.

neuroscience↗

bioRxiv2024

Hierarchical Organization of Visual Feature Attention Control

Attention can be deployed in anticipation of visual stimuli based on features such as their color or direction of motion. This anticipatory feature-based attention involves top-down neural control signals from the frontoparietal network that bias visual cortex to enhance the processing of attended information and suppress distraction. So, for example, anticipatory attention control can enable effective selection based on stimulus color while ignoring distracting information about stimulus motion. But as well, anticipatory attention can be focused more narrowly, for example, to select specific colors or motion directions that define task-relevant events and objects. One important question that remains open is whether anticipatory attention control first biases broad feature dimensions such as color versus motion before biasing the specific feature attributes (e.g., blue vs. green). To investigate this, we recorded EEG activity during a task where participants were cued to either attend to a color (blue or green) or a motion direction (up or down) on a trial-by-trial basis. Applying multivariate decoding approaches to the EEG alpha band (8-12 Hz) activity during the attention control period (cue-target interval), we observed significant decoding for both the attended dimensions (color vs. motion) and specific feature attributes (blue vs. green; up vs. down). Importantly, the temporal onset of the dimension-level biasing (color vs. motion) preceded that of the attribute-level biasing (e.g., blue vs. green). These findings demonstrate that the top-down control of feature-based attention proceeds in a hierarchical fashion, first biasing the broad feature dimension, and then narrowing to the specific feature attribute. Significance StatementDuring voluntary feature-based attention, electrophysiological and neuroimaging studies have highlighted the role of anticipatory (top-down) biasing of the sensory cortex in enhancing the selection of attended stimulus attributes, but little is known about how this is achieved. In particular, it is not clear whether attending to an attribute such as a color (blue vs. green) or motion direction (up vs. down) first biases all neural structures coding that dimension (color/motion) before biasing the specific attribute, or if the top-down signals directly bias only the attended attribute. Using EEG and multivariate decoding, we report that top-down attention control follows a hierarchical organization: first, the broader attended feature dimension is biased, which is followed by the biasing of the specific feature attribute.

neuroscience↗

bioRxiv2024

Rhythmic Sampling and Competition of Target and Distractor in a Motion Detection Task

It has been suggested that the visual system samples attended information rhythmically. Does rhythmic sampling also apply to distracting information? How do attended information and distracting information compete temporally for neural representations? We recorded electroencephalography (EEG) from participants who detected instances of coherent motion in a random dot kinematogram (RDK; the target), overlayed on different categories (pleasant, neutral, and unpleasant) of affective images from the International Affective System (IAPS) (the distractor). The moving dots were flickered at 4.29 Hz whereas the IAPS pictures were flickered at 6 Hz. The time course of EEG spectral power at 4.29 Hz was taken to index the temporal dynamics of target processing. The spatial pattern of the EEG spectral power at 6 Hz was similarly extracted and subjected to a moving-window MVPA decoding analysis to index the temporal dynamics of processing pleasant, neutral, or unpleasant distractor pictures. We found that (1) both target processing and distractor processing exhibited rhythmicity at [~]1 Hz and (2) the phase difference between the two rhythmic time courses were related to task performance, i.e., relative phase closer to {pi} predicted a higher rate of coherent motion detection whereas relative phase closer to 0 predicted a lower rate of coherent motion detection. These results suggest that (1) in a target-distractor scenario, both attended and distracting information were sampled rhythmically and (2) the more target sampling and distractor sampling were separated in time within a sampling cycle, the less distraction effects were observed, both at the neural and the behavioral level.

neuroscience↗

bioRxiv2024

Identification of Intrinsic Features for Cortical Separability of Human and Mouse Neurons

This work introduces a novel framework for holistic comparative analysis of cortical regions in mouse and human brains at single-neuron resolution, with a primary focus on the morphological and molecular characteristics of neurons. To do so, we generated one of the largest dendritic reconstruction datasets of cortical neurons to date, comprising 2,363 human neurons and 16,011 mouse neurons from the frontal, parietal, and temporal lobes, followed by establishing a rigorous procedure to identify anatomically and functionally corresponding brain regions with minimal variability in brain mapping. Additionally, we leveraged single nucleus/cell transcriptomic data from independent groups to validate the molecular correspondence of the brain regions identified in this study. The significance of these anatomically, functionally, and molecularly corresponding mouse-human region pairs is highlighted by examining the intrinsic features of their respective cortical regions. Our findings reveal that human neuron branching patterns differ dramatically from those in mouse brains, particularly in terms of dendritic branching frequency and normalized dendritic branching intervals. This difference is pronounced in the frontal and temporal lobes, underscoring the distinct neuronal architectures between the two species. At the single-neuron level, we found that neurons from the human frontal and parietal lobes are six times more separable than those from the same regions in mouse brains. This heightened separability is also observed between the frontal and temporal lobes, as well as between the parietal and temporal lobes in humans. We thoroughly explored the entire morphological feature space, along with its characteristic subspaces, and consistently found this distinct separability. Remarkably, this neuronal separability can be partially recapitulated when examining the global functional states of these brain lobes--using newly acquired Electroencephalography (EEG) and Magnetoencephalography (MEG) signals as physiological measures--as well as their global metabolic states, molecular profiles, and cortical geometry. These findings suggest that our comparative analysis of single-neuron intrinsic features could serve as a valuable foundation for future comprehensive studies of cross-species brain structures and functions.

neuroscience↗