Biology subjects

He, B. J.

Publications and source records attributed to He, B. J..

13 recordsLinked to original sources

bioRxiv2026

Probing the neural geometry of conscious object perception in time

Conscious object recognition is a critical brain function, key to making sense of the plethora of visual inputs and guiding adaptive behavior. Yet, the sequence of neural processing underlying conscious object recognition remains unclear. Existing proposals alternatively suggest that conscious object recognition proceeds in a "coarse-to-fine" sequence or a "fine-to-coarse" sequence. Here, we recorded high-density EEG while participants of both sexes performed a threshold-level object recognition task under different viewing conditions. Both superordinate and basic-level category information emerged early in neural activity, followed by a late superordinate category representation in an abstract format that is disentangled from low-level object features. Strikingly, in the early period, while basic-level category information positively correlated with perceptual awareness, superordinate category information negatively predicted perceptual awareness. By contrast, both superordinate and basic-level category information positively correlated with objective categorization accuracy in the early period. In the late period, both levels of category information positively correlated with awareness. These results suggest that neural processing underlying conscious object perception proceeds from the basic to the superordinate category level.

neuroscience↗

bioRxiv2026

Systematic image perturbations reveal persistent gaps between human and machine vision

Deep neural networks (DNNs) are promising computational models for understanding visual object recognition. Yet, whether DNNs use similar visual cues for object recognition as humans do remains unknown. We created an image set that systematically untangles global shape, internal parts, and texture information, and compared human recognition behavior against >200 DNNs spanning diverse architectures, training diets, and training objectives. No DNNs replicated humans cue-reliance profile, including those with recurrence or specialized training. Fine-tuned text-image contrastive-trained models, regardless of architecture, were most human-like overall, but lost their human-alignment when the global shape was disrupted. Strikingly, all DNNs substantially underperformed humans when the global shape cue alone was critical to object recognition. Furthermore, alignment with ventral stream neural recordings in an existing database did not predict alignment to human behavior, and model performance does not always predict its human-alignment. Together, these findings reveal systematic and persistent differences between human and machine vision.

neuroscience↗

bioRxiv2025

Genetic elements promote retention of extrachromosomal DNA in cancer cells

Extrachromosomal DNA (ecDNA) is a prevalent and devastating form of oncogene amplification in cancer1,2. Circular megabase-sized ecDNAs lack centromeres and segregate stochastically during cell division3-6 yet persist over many generations. EcDNAs were first observed to hitchhike on mitotic chromosomes into daughter cell nuclei over 40 years ago with unknown mechanism3,7. Here we identify a family of human genomic elements, termed retention elements, that tether episomes to mitotic chromosomes to increase ecDNA transmission to daughter cells. We develop Retain-seq, a genome-scale assay that reveals thousands of human retention elements conferring generational persistence to heterologous episomes. Retention elements comprise a select set of CpG-rich gene promoters and act additively. Live-cell imaging and chromatin conformation capture show that retention elements physically interact with mitotic chromosomes at regions which are mitotically bookmarked by transcription factors and chromatin proteins, intermolecularly recapitulating promoter-enhancer interactions. Multiple retention elements are co-amplified with oncogenes on individual ecDNAs in human cancers and shape their sizes and structures. CpG-rich retention elements are focally hypomethylated; targeted cytosine methylation abrogates retention activity and leads to ecDNA loss, suggesting that methylation-sensitive interactions modulate episomal DNA retention. These results highlight the DNA elements and regulatory logic of mitotic ecDNA retention. Amplifications of retention elements promote the maintenance of oncogenic ecDNA across generations of cancer cells, revealing the principles of episome immortality intrinsic to the human genome.

cancer biology↗

bioRxiv2025

Attention robustly dissociates objective performance and subjective visibility reports

Visual experience can sometimes depart from visual performance, providing a powerful lens into the mechanisms generating conscious perception. In one proposed dissociation--subjective inflation--unattended locations in the periphery appear stronger than attended ones despite equated performance. Subjective inflation has played a central role in motivating theories of consciousness that reject the sufficiency of sensory signals for conscious perception. Yet the empirical basis for subjective inflation is limited. Here, in a large-scale adversarial collaboration, we conducted four simultaneously-replicated experiments testing the strength, character, and extent of subjective inflation under inattention. We used a new analytic approach to quantify inattentional inflation over full psychometric functions, beyond single matched-performance levels. We found robust inattentional inflation for contrast-dependent and texture-based perception, at and above the visual threshold. However at suprathreshold, we found inattentional inflation for the overall stimulus but not the specific feature relevant for performance. Finally, we establish the unifying principle that inattentional inflation occurs if and only if attention reduces performance thresholds more than visibility thresholds. Thus what we think we see may regularly exceed what we can visually discriminate, placing constraints on theories of conscious perception.

neuroscience↗

bioRxiv2025

Neural and Computational Mechanisms Underlying One-shot Perceptual Learning in Humans

The ability to quickly learn and generalize is one of the brains most impressive feats and recreating it remains a major challenge for modern artificial intelligence research. One of the most mysterious one-shot learning abilities displayed by humans is one-shot perceptual learning, whereby a single viewing experience drastically alters visual perception in a long-lasting manner. Where in the brain one-shot perceptual learning occurs and what mechanisms support it remain enigmatic. Combining psychophysics, 7T fMRI, and intracranial recordings, we identify high-level visual cortex as the most likely neural substrate wherein neural plasticity supports one-shot perceptual learning. We further develop a novel deep neural network model incorporating top-down feedback into a vision transformer, which recapitulates and predicts human behavior. The prior knowledge learnt by this model is highly similar to the neural code in the human high-level visual cortex. These results reveal the neurocomputational mechanisms underlying one-shot perceptual learning in humans.

neuroscience↗

bioRxiv2025

TEAD1 condensates are transcriptionally inactive storage sites on the pericentromeric heterochromatin

TEA domain transcription factor 1 (TEAD1), a Hippo pathway transcription factor important in cellular homeostasis and development, is increasingly implicated in cancer biology. Here, we reveal a novel role for TEAD1 in organizing nuclear condensates, independent of active transcription. Using high-resolution imaging, ChIP-seq, RNA-seq and proximity-based proteomics, we demonstrate that in patient-derived renal cell carcinoma cells, TEAD1 forms micron-sized condensates by binding to the heterochromatic pericentromeric regions using its DNA-binding domain. TEAD-specific MCAT motifs selectively enrich and cluster in the pericentromeric region, specifically seeding TEAD1 condensates. TEAD1 condensates do not activate transcription but instead serve as depots for excess TEAD1, and disrupting TEAD1 condensates leads to increases in YAP/TEAD target gene expression. This organization of TEAD1 contrasts with that observed in other genomic regions of both RCC and normal kidney cells, in which TEAD1 associates with markers of active transcription. Our findings provide a mechanistic framework for TEAD1s dual regulatory roles, offering new insights into its contribution to transcriptional dysregulation and tumor progression.

cell biology↗

bioRxiv2024

Enhancer activation from transposable elements in extrachromosomal DNA

Extrachromosomal DNA (ecDNA) drives oncogene amplification and intratumoral heterogeneity in aggressive cancers. While transposable element (TE) reactivation is common in cancer, its role on ecDNA remains unexplored. Here, we map the 3D architecture of MYC-amplified ecDNA in colorectal cancer cells and identify 68 ecDNA-interacting elements (EIEs)--genomic loci enriched for TEs that are frequently integrated onto ecDNA. We focus on an L1M4a1#LINE/L1 fragment co-amplified with MYC, which functions only in the ecDNA amplified context. Using CRISPR-CATCH, CRISPR interference, and reporter assays, we confirm its presence on ecDNA, enhancer activity, and essentiality for cancer cell fitness. These findings reveal that repetitive elements can be reactivated and co-opted as functional rather than inactive sequences on ecDNA, potentially driving oncogene expression and tumor evolution. Our study uncovers a mechanism by which ecDNA harnesses repetitive elements to shape cancer phenotypes, with implications for diagnosis and therapy.

genetics↗

bioRxiv2023

Coordinated inheritance of extrachromosomal DNA species in human cancer cells

The chromosomal theory of inheritance has dominated human genetics, including cancer genetics. Genes on the same chromosome segregate together while genes on different chromosomes assort independently, providing a fundamental tenet of Mendelian inheritance. Extrachromosomal DNA (ecDNA) is a frequent event in cancer that drives oncogene amplification, dysregulated gene expression and intratumoral heterogeneity, including through random segregation during cell division. Distinct ecDNA sequences, herein termed ecDNA species, can co-exist to facilitate intermolecular cooperation in cancer cells. However, how multiple ecDNA species within a tumor cell are assorted and maintained across somatic cell generations to drive cancer cell evolution is not known. Here we show that cooperative ecDNA species can be coordinately inherited through mitotic co-segregation. Imaging and single-cell analyses show that multiple ecDNAs encoding distinct oncogenes co-occur and are correlated in copy number in human cancer cells. EcDNA species are coordinately segregated asymmetrically during mitosis, resulting in daughter cells with simultaneous copy number gains in multiple ecDNA species prior to any selection. Computational modeling reveals the quantitative principles of ecDNA co-segregation and co-selection, predicting their observed distributions in cancer cells. Finally, we show that coordinated inheritance of ecDNAs enables co-amplification of specialized ecDNAs containing only enhancer elements and guides therapeutic strategies to jointly deplete cooperating ecDNA oncogenes. Coordinated inheritance of ecDNAs confers stability to oncogene cooperation and novel gene regulatory circuits, allowing winning combinations of epigenetic states to be transmitted across cell generations.

cancer biology↗

bioRxiv2022

Spatiotemporal neural dynamics of object recognition under uncertainty in humans

While there is a wealth of knowledge about core object recognition--our ability to recognize clear, high-contrast object images, how the brain accomplishes object recognition tasks under increased uncertainty remains poorly understood. We investigated the spatiotemporal neural dynamics underlying object recognition under increased uncertainty by combining MEG and 7 Tesla fMRI in humans during a threshold-level object recognition task. We observed an early, parallel rise of recognition-related signals across ventral visual and frontoparietal regions that preceded the emergence of category-related information. Recognition-related signals in ventral visual regions were best explained by a two-state representational format whereby brain activity bifurcated for recognized and unrecognized images. By contrast, recognition-related signals in frontoparietal regions exhibited a reduced representational space for recognized images, yet with sharper category information. These results provide a spatiotemporally resolved view of neural activity supporting object recognition under uncertainty, revealing a pattern distinct from that underlying core object recognition.

neuroscience↗

bioRxiv2022

Frequency-specific neural signatures of perceptual content and perceptual stability

In the natural environment, we often form stable perceptual experiences from ambiguous and fleeting sensory inputs. Which neural activity underlies the content of perception and which neural activity supports perceptual stability remains an open question. We used a bistable perception paradigm involving ambiguous images to behaviorally dissociate perceptual content from perceptual stability, and magnetoencephalography (MEG) to measure whole-brain neural dynamics in humans. Combining multivariate decoding and neural state-space analyses, we found frequency band-specific neural signatures that underlie the content of perception and promote perceptual stability, respectively. Across different types of images, non-oscillatory neural activity in the slow cortical potential (SCP, <5 Hz) range supported the content of perception. By contrast, perceptual stability is influenced by the amplitude of alpha and beta oscillations. In addition, neural activity underlying perceptual memory, which supports perceptual stability when sensory input is temporally removed from view, also encodes elapsed time. Together, these results reveal distinct neural mechanisms that support the content vs. stability of visual perception.

neuroscience↗

bioRxiv2019

Task-evoked activity quenches neural correlations and variability in large-scale brain systems

Many large-scale functional connectivity studies have emphasized the importance of communication through increased inter-region correlations during task states. In contrast, local circuit studies have demonstrated that task states primarily reduce correlations among pairs of neurons, likely enhancing their information coding by suppressing shared spontaneous activity. Here we sought to adjudicate between these conflicting perspectives, assessing whether co-active brain regions during task states tend to increase or decrease their correlations. We found that variability and correlations primarily decrease across a variety of cortical regions in two highly distinct data sets: non-human primate spiking data and human functional magnetic resonance imaging data. Moreover, this observed variability and correlation reduction was accompanied by an overall increase in dimensionality (reflecting less information redundancy) during task states, suggesting that decreased correlations increased information coding capacity. We further found in both spiking and neural mass computational models that task-evoked activity increased the stability around a stable attractor, globally quenching neural variability and correlations. Together, our results provide an integrative mechanistic account that encompasses measures of large-scale neural activity, variability, and correlations during resting and task states.

neuroscience↗

bioRxiv2018

State-aware detection of sensory stimuli in the cortex of the awake mouse

Cortical responses to sensory inputs vary across repeated presentations of identical stimuli, but how this trial-to-trial variability impacts detection of sensory inputs is not fully understood. Using multi-channel local field potential (LFP) recordings in primary somatosensory cortex (S1) of the awake mouse, we optimized a data-driven cortical state classifier to predict single-trial sensory-evoked responses, based on features of the spontaneous, ongoing LFP recorded across cortical layers. Our findings show that, by utilizing an ongoing prediction of the sensory response generated by this state classifier, an ideal observer improves overall detection accuracy and generates robust detection of sensory inputs across various states of ongoing cortical activity in the awake brain, which could have implications for variability in the performance of detection tasks across brain states.\n\nAuthor summaryEstablishing the link between neural activity and behavior is a central goal of neuroscience. One context in which to examine this link is in a sensory detection task, in which an animal is trained to report the presence of a barely perceptible sensory stimulus. In such tasks, both sensory responses in the brain and behavioral responses are highly variable. A simple hypothesis, originating in signal detection theory, is that perceived inputs generate neural activity that cross some threshold for detection. According to this hypothesis, sensory response variability would predict behavioral variability, but previous studies have not born out this prediction. Further complicating the picture, sensory response variability is partially dependent on the ongoing state of cortical activity, and we wondered whether this could resolve the mismatch between response variability and behavioral variability. Here, we use a computational approach to study an adaptive observer that utilizes an ongoing prediction of sensory responsiveness to detect sensory inputs. This observer has higher overall accuracy than the standard ideal observer. Moreover, because of the adaptation, the observer breaks the direct link between neural and behavioral variability, which could resolve discrepancies arising in past studies. We suggest new experiments to test our theory.

neuroscience↗

bioRxiv2017

Initial-State-Dependent, Robust, Transient Neural Dynamics Encode Conscious Visual Perception

Recent research has identified late-latency, long-lasting neural activity as a robust correlate of conscious perception. Yet, the dynamical nature of this activity is poorly understood, and the mechanisms governing its presence or absence and the associated conscious perception remain elusive. We applied dynamic-pattern analysis to whole-brain slow cortical dynamics recorded by magnetoencephalography (MEG) in human subjects performing a threshold-level visual perception task. Up to 1 second before stimulus onset, brain activity pattern across widespread cortices significantly predicted whether a threshold-level visual stimulus was later consciously perceived. This initial state of brain activity interacts nonlinearly with stimulus input to shape the evolving cortical activity trajectory, with seen and unseen trials following well separated trajectories. Furthermore, cortical activity trajectories during conscious perception are fast evolving and robust to small variations in the initial state. These results suggest that brain dynamics underlying conscious visual perception belongs to the class of initial-state-dependent, robust, transient neural dynamics.\n\nAUTHOR SUMMARYWhat brain mechanisms underlie conscious perception? A commonly adopted paradigm for studying this question is to present human subjects with threshold-level stimuli. When shown repeatedly, the same stimulus is sometimes consciously perceived, sometimes not. Using magnetoencephalography, we shed light on the neural mechanisms governing whether the stimulus is consciously perceived in a given trial. We observed that depending on the initial brain state defined by widespread activity pattern in the slow cortical potential (<5 Hz) range, a physically identical, brief (30 - 60 ms) stimulus input triggers distinct sequences of activity pattern evolution over time that correspond to either consciously perceiving the stimulus or not. Such activity pattern evolution forms a \"trajectory\" in the state space and affords significant single-trial decoding of perceptual outcome from 1 sec before to 3 sec after stimulus onset. While previous theories on conscious perception have emphasized sustained, high-level activity, we found that brain dynamics underlying conscious perception exhibit fast-changing activity patterns. These results significantly further our understanding on the neural mechanisms governing conscious access of a stimulus and the dynamical nature of neural activity underlying conscious perception.

neuroscience↗