Biology subjects

Dou, W.

Publications and source records attributed to Dou, W..

9 recordsLinked to original sources

bioRxiv2026

MIRATS framework: Normative multiscale characterization of brain regulatory systems across sex and age using multimodal MRI

Deep brain systems involved in arousal, autonomic regulation, sensory integration, and homeostatic control remain underrepresented in conventional whole-brain neuroimaging frameworks. In particular, diencephalic and brainstem nuclei are often insufficiently represented in cortex-centered analyses, limiting the normative references needed to interpret systems-level variation in health and disease. To address this gap, we developed a unified multiscale framework with explicit representation of deep nuclei. By integrating cerebral, cerebellar, diencephalic, and brainstem atlases in standard space, we constructed a 220-region whole-brain parcellation and extracted complementary features at three analytical scales: nodal properties, edge-wise connectivity, and persistent-homology-based topological descriptors. We applied this framework to healthy adults from the Human Connectome Project-Aging cohort to characterize normative multiscale organization and test sex- and age-related variation. Applied to this cohort, our framework revealed pronounced heterogeneity across anatomical systems. Brainstem and diencephalic nuclei showed multiscale feature profiles distinct from those of cerebral and cerebellar regions across nodal, edge-wise, and higher-order topological scales. Sex comparisons identified selective differences across different scales, whereas age modeling revealed widespread but feature- and system-dependent variation across adulthood. Together, these findings show that normative whole-brain organization in this deep-system-aware space is structured by system-specific rather than globally uniform patterns. These findings establish a normative multiscale framework for characterizing brainstem-diencephalic-cerebellar-cerebral organization in healthy adults and provide a quantitative reference for future translational studies of disease-related abnormalities in deep regulatory systems.

bioinformatics↗

bioRxiv2026

DINMC: A Deep Learning Framework for Interpretable Normative Model Construction and Pathological Brain Alteration Detection

Background and ObjectiveNormative modeling is a key tool for understanding brain alterations in neurodegenerative diseases, such as cerebellar-type multiple system atrophy. However, existing methods lack interpretability and fail to capture clinically meaningful pathological changes. This study presents DINMC, a Deep Interpretable Normative Model Construction framework, which combines autoencoder-based learning with statistical hypothesis testing to better capture and interpret disease-specific neu-roanatomical changes. MethodsThe DINMC framework constructs normative models using neuroimaging data from multi-site large healthy cohorts. It utilizes a U-shaped convolutional autoencoder to train these models, which are then applied to reconstruct brain features from both patients and healthy controls within the same study cohort. Pathological confidence values are derived by fusing original and deviation feature spaces, offering a measure of disease-related pathology reflected in each dimension of the features. The framework was validated through statistical analysis and prognostic classification and regression tasks. ResultsThe pathological confidence provides valuable insights into the neuroanatomical regions most affected by the disease, as well as the correlation between changes in these regions and clinical assessment scales. Our optimal model outperform traditional methods in prognostic prediction tasks, with an AUC of 0.972 for classification tasks and an R2 of 0.432 for regression tasks. ConclusionDINMC provides a novel and interpretable framework for neuroimaging analysis. By combining deep learning and statistical hypothesis testing, this framework offers a unique solution to improving both the interpretability and performance of normative models in neuroimaging. The approach is scalable to other neuroimaging datasets, offering a versatile tool for broader biomedical applications.

bioinformatics↗

bioRxiv2026

A unified population-genetic framework for inferring double reduction and higher-order two-locus linkage disequilibrium in autopolyploids

Polyploid genomes exhibit complex allelic interactions driven by polysomic inheritance that cannot be adequately captured by conventional pairwise linkage disequilibrium (LD) models, leaving higher-order dependency structures largely unresolved. Here, we develop a unified population-genetic framework that jointly infers double reduction and higher-order allelic associations in autopolyploids. By modeling dosage-dependent gametic structure, our approach reformulates LD as a hierarchical system, enabling decomposition of allelic dependencies beyond pairwise interactions. Through simulations, we show that conventional estimators systematically confound higher-order allelic structure with pairwise LD, leading to biased inference and loss of identifiability under dosage uncertainty. In contrast, our joint likelihood framework achieves asymptotically unbiased estimation and reveals that effective double reduction emerges as a composite parameter linking meiotic configuration to population-level allelic reshaping. Applying this model to Arabidopsis arenosa and cultivated potato, we uncover fundamental contrasts in genomic architecture. In natural populations, higher-order LD self-organizes into structured, long-range dependency networks, whereas predominantly clonal populations exhibit collapse of higher-order dependency structure. We further identify a spatial antagonism between double reduction and higher-order LD, demonstrating that elevated double reduction compresses the physical scale of allelic interactions. Our results demonstrate that autopolyploid genomes are organized as higher-order dependency systems rather than collections of pairwise associations, establishing higher-order allelic structure as a fundamental extension of classical LD theory.

genetics↗

bioRxiv2025

Non-conscious Multisensory Integration in the Ventriloquist Effect

The degree to which information from distinct sensory modalities can interact in the absence of conscious awareness remains controversial. According to the Global Neuronal Workspace Theory (GNWT), unconscious sensory information remains relatively confined to sensory cortex and should not be capable of interacting with other modalities until it is broadcast into the (conscious) global workspace comprising late (>300ms) frontal-parietal activation. The ventriloquist effect is a classical multisensory integration phenomenon that refers to the misperception of sound location towards concurrent visual stimulation, such as perceiving the voice of a ventriloquist actor as coming from the moving dummy. Here we used meta-contrast masking to render a brief flash stimulus non-conscious while 30 participants (male, female, and non-binary) performed a sound localization task. We found that, despite being subjectively invisible and at chance at discriminating the flash location, participants were nevertheless biased to localize sounds towards the unconscious flash locations. The effect was present in virtually all participants, was nearly as large as the effect on conscious trials, and was robust to controls for individual differences in task performance. Decoding and ERP analyses of concurrently recorded EEG signals showed that the non-conscious flash location information was present up until around 220ms but not after; suggesting that the visual influence on sound perception may have occurred before conscious broadcast. In support of this, early decoding accuracy on non-conscious trials predicted individual variation in the size of the unconscious ventriloquist effect. Our findings suggest that subjective perception is not required for the integration of signals originating in distinct sensory modalities prompting new questions about the role of subjective perception in multisensory integration.

neuroscience↗

bioRxiv2024

Metacognitive Introspection Alters the Dynamics of Pre-Decisional Neural Evidence Accumulation

Metacognitive introspection refers to the capacity to monitor and evaluate ones own performance, such as when expressing confidence in ones choice. In the context of perceptual decision making, current computational models of confidence typically assume that confidence is computed after the evidence collection process underlying the initial choice has unfolded. This account predicts that the act of introspection required in order to provide a confidence judgment about ones decision should have little impact on the dynamics of evidence accumulation leading up to that decision. We tested this by examining whether a neural proxy for perceptual evidence accumulation (the central-parietal positivity; CPP) exhibits different dynamics when participants do or do not rate confidence in their perceptual decisions. Behavioral results showed that confidence introspection increased decision accuracy and response time. Importantly, confidence introspection also resulted in a steeper CPP slope beginning shortly after ([~]250 ms) decision evidence was available. We further observed an effector-specific neural signal of confidence evidence which both built up prior to the perceptual choice and which was predictive of the amount of confidence subsequently expressed by the participants. Our results indicate that introspection can alter the dynamics of perceptual evidence accumulation, possibly by acting on the gain of evidence accumulation, and that motor-related confidence signals emerge in tandem with pre-decisional evidence accumulation signals rather than post-choice.

neuroscience↗

bioRxiv2024

Threshold-Free Network-Oriented Statistics in Neuroscience

Network neuroscience has emerged as an indispensable tool for studying brain structure and function. Currently, the network-based statistic (NBS) procedure is widely used for dealing with massive multiple testing/comparison problems in brain networks. However, the NBS requires choosing a hard cluster-forming threshold, lacking objective rules. A powerful and flexible statistical framework is urgently needed with growing interest in finer-grained network explorations across modalities and scales. Here, we introduce a permutation-based framework--"Threshold-Free Network-Oriented Statistics" (TFNOS). It integrates two "threshold-free" pathways: traversing all cluster-forming thresholds (TT) and using predefined clusters (PC). The TT procedure, building upon the threshold-free network-based statistics, requires setting additional parameters. The PC procedures comprise six variants given the degree of freedom in pooling data, null distribution construction, and controlled error rate. Using numerical simulations, we evaluated the performance of the TT procedure under 600 parameter combinations, then benchmarked TFNOS procedures and baselines across different topologies of effects, sample sizes, and effect sizes, and finally provided illustrative examples with real data. We offer recommended parameter values that allow the TT procedure to stably maintain leading power, while empirically controlling the false discovery rate (FDR) beyond only weakly controlling the familywise error rate (FWER). Notably, the relevant parameters commonly employed in the field appear overly liberal. Furthermore, for the PC procedures, FDR-controlling variants showed improved power compared to FWER-controlling variants, and some of them are simple but do not compromise power. The nonparametric PC procedures allow the selection of any test statistics considered appropriate. Overall, the TFNOS is a generalized framework for inference on edges/nodes of undirected/directed brain networks. We provide empirical and principled criteria for selecting appropriate procedures and may enhance the reproducibility and sensitivity of future brain research.

neuroscience↗

bioRxiv2023

Neural Signatures of Evidence Accumulation Encode Subjective Perceptual Confidence

Confidence is an adaptive computation when environmental feedback is absent, yet there is little consensus regarding how perceptual confidence is computed in the brain. Difficulty arises because confidence correlates with other factors such as accuracy, response time (RT), or evidence quality. We investigated whether neural signatures of evidence accumulation during a perceptual choice predict subjective confidence independently of these factors. Using motion stimuli, a central-parietal EEG component (CPP) behaves as an accumulating decision variable that predicts evidence quality, RT, accuracy, and confidence (Experiment 1). Psychophysically varying confidence while holding accuracy constant (Experiment 2), the CPP still predicts confidence. Statistically controlling for RT, accuracy, and evidence quality (Experiment 3), the CPP still explains unique variance in confidence. The results indicate that evidence accumulation, indexed by the CPP, is tightly linked to the subjective perceptual experience of sensory information. Independent of other factors, pre-decision neural signatures of evidence accumulation encode subjective confidence.

neuroscience↗

bioRxiv2021

Pre-Stimulus Alpha-Band Phase Gates Afferent Visual Cortex Responses

The neurogenesis of alpha-band (8-13 Hz) activity has been characterized across many different animal experiments. However, the functional role that alpha oscillations play in perception and behavior has largely been attributed to two contrasting hypotheses, with human evidence in favor of either (or both or neither) remaining sparse. On the one hand, alpha generators have been observed in relay sectors of the visual thalamus and are postulated to phasically inhibit afferent visual input in a feedforward manner 1-4. On the other hand, evidence also suggests that the direction of influence of alpha activity propagates backwards along the visual hierarchy, reflecting a feedback influence upon the visual cortex 5-9. The primary source of human evidence regarding the role of alpha phase in visual processing has been on perceptual reports 10-16, which could be modulated either by feedforward or feedback alpha activity. Thus, although these two hypotheses are not mutually exclusive, human evidence clearly supporting either one is lacking. Here, we present human subjects with large, high-contrast visual stimuli that elicit robust C1 event-related potentials (ERP), which peak between 70-80 milliseconds post-stimulus and are thought to reflect afferent primary visual cortex (V1) input17-20. We find that the phase of ongoing alpha oscillations modulates the global field power (GFP) of the EEG during this first volley of stimulus processing (the C1 time-window). On the standard assumption21-23 that this early activity reflects postsynaptic potentials being relayed to visual cortex from the thalamus, our results suggest that alpha phase gates visual responses during the first feed-forward sweep of processing.

neuroscience↗

bioRxiv2018

DNA segment of African Swine Fever Virus first detected in hard ticks from sheep and bovine

In this study, we aimed to detect viruses in hard ticks using the small RNA sequencing based method. A 235-bp DNA segment was detected in Dermacentor nuttalli (hard ticks) and D. silvarum (hard ticks) from sheep and bovine, respectively. The detected 235-bp segment had an identity of 99% to a 235-bp DNA segment of African Swine Fever Virus (ASFV) and contained three single nucleotide mutations (C38T, C76T and A108C). C38T, resulting in an single amino acid mutation G66D, suggests the existence of a new ASFV strain, which is different from all reported ASFV strains in NCBI GenBank database. These results also suggest that ASFV could have a wide range of hosts or vectors, beyond the well known Suidae family and soft ticks. Our findings pave the way toward further studies of ASFV transmission and development of prevention and control measures.

zoology↗