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Tingting Wu

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11 papers

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YNIMG Journal 2026 Journal Article

When More Control Means Better Choices: Cognitive Control Networks Drive Expected-Value Maximization Under Uncertainty

Xia Wu
Yuning Geng
Yan Chen
Shuoxian Zhang
Tianhao Liu
Shuaipeng You
Fang Liu
Yunpeng Jiang

Human decision-making under outcome uncertainty often deviates from rational expected-value maximization, frequently falling back on the suboptimal probability matching heuristic. The neurocomputational mechanisms determining individual differences in overcoming this heuristic remain elusive. Here, we investigated how cognitive control capacity (CCC) modulates decision-making under varying levels of outcome uncertainty. Participants with high and low CCC performed a predictive inference task during functional magnetic resonance imaging. Behaviorally, high CCC individuals consistently exhibited a significantly higher proportion of maximizing responses (PMR) across all uncertainty levels. Using hierarchical drift-diffusion modeling, we demonstrated that this optimal performance was driven by more cautious decision thresholds, indicating greater deliberation to resist intuitive shortcuts. At the neural level, while localized activations in the cingulo-opercular network (CON) and frontoparietal network (FPN) reflected the general cognitive burden of escalating uncertainty, functional connectivity analyses revealed a specific neural pathway supporting optimal choices. Crucially, the connectivity within the CON (anterior insula to middle frontal gyrus) acted as a specific neural amplifier, which was absolutely necessary for translating high cognitive capacity into optimal expected-value maximization. The FPN, while tracking uncertainty, did not modulate this capacity-performance link. Together, these findings provide an integrated neurocomputational framework demonstrating how specific cognitive control networks mobilize resources to overcome heuristic tendencies and achieve optimal decisions under uncertainty.

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AAAI Conference 2025 Conference Paper

KOALA: Kernel Coupling and Element Imputation Induced Multi-View Clustering

Tingting Wu
Zhendong Li
Zhibin Gu
Jiazheng Yuan
Songhe Feng

Incomplete Multi-View Clustering (IMVC) has made significant progress by optimally merging multiple pre-specified incomplete views. Most existing IMVC algorithms operate under the assumption that view alignment is known, but in practice, the coupling information between views may be absent, thereby limiting the practical applicability of these methods. Being aware of this, we propose a novel IMVC method named Kernel cOupling And eLement imputAtion induced Multi-View Clustering (KOALA), which sufficiently explores the nonlinear relationship among features and optimally processes a group of kernels with missing and unaligned elements to simultaneously resolve multi-view clustering problem under both uncoupled and incomplete scenarios. Specifically, we first introduce a cross-kernel alignment learning strategy to reconstruct the coupling relationships among multiple kernels, which effectively captures high-order nonlinear relationships among samples and enhances alignment accuracy. Additionally, a low-rank tensor constraint is imposed on the optimizable alignment kernel tensor, facilitating the effective imputation of missing kernel elements by leveraging consistency information across views. Subsequently, we develop an alternative optimization approach with promising convergence to solve the resultant optimization problem. Extensive experimental results on various multi-view datasets demonstrate that the KOALA method achieves remarkable clustering performance.

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JBHI Journal 2025 Journal Article

PPA Net: The Pixel Prediction Assisted Net for 3D TOF-MRA Cerebrovascular Segmentation

Zhiqi Lee
Tao Liu
Haonan Zhang
Xiang Zhang
Xuan Li
Yizhen Pan
Tingting Wu
Jierui Ding

Cerebrovascular segmentation is essential for diagnosing and treating cerebrovascular diseases. However, accurately segmenting cerebral vessels in TOF-MRA remains challenging due to significant interindividual variations in cerebrovascular morphology, low image con-trast, and class imbalance. The present study proposes an advanced deep learning model called PPA Net, consisting of VesselMRA Net and VesselConvLSTM components. Firstly, VesselMRA Net utilizes rectangular convolutional blocks to fuse multi-scale features, enhancing feature extraction per-formance. VesselMRA Net employs the attention mechanism to boost certain valuable semantic weighting, addressing segmentation challenges arising from class imbalance and low contrast. Secondly, VesselConvLSTM, a pixel-level prediction model, employs a gating mechanism to learn cerebral vessel morphology across individuals. It reduces individual differences in segmentation and restores inter-voxel correlations disrupted by data slicing, aiding VesselMRA Net in accurately segmenting cerebrovascular pixels. Lastly, integrating VesselMRA Net and VesselConv-LSTM results in a modular cerebral vessel segmentation framework, PPA Net, facilitating separate optimization of the backbone network and predicted model components. The performance of this model has been extensively validated through experimental evaluations on three publicly available datasets, obtaining significant competitiveness when compared to the state-of-the-art of the current cerebral vessel segmentation models.

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YNIMG Journal 2024 Journal Article

Brain age prediction using interpretable multi-feature-based convolutional neural network in mild traumatic brain injury

Xiang Zhang
Yizhen Pan
Tingting Wu
Wenpu Zhao
Haonan Zhang
Jierui Ding
Qiuyu Ji
Xiaoyan Jia

BACKGROUND: Convolutional neural network (CNN) can capture the structural features changes of brain aging based on MRI, thus predict brain age in healthy individuals accurately. However, most studies use single feature to predict brain age in healthy individuals, ignoring adding information from multiple sources and the changes in brain aging patterns after mild traumatic brain injury (mTBI) were still unclear. METHODS: Here, we leveraged the structural data from a large, heterogeneous dataset (N = 1464) to implement an interpretable 3D combined CNN model for brain-age prediction. In addition, we also built an atlas-based occlusion analysis scheme with a fine-grained human Brainnetome Atlas to reveal the age-sstratified contributed brain regions for brain-age prediction in healthy controls (HCs) and mTBI patients. The correlations between brain predicted age gaps (brain-PAG) following mTBI and individual's cognitive impairment, as well as the level of plasma neurofilament light were also examined. RESULTS: Our model utilized multiple 3D features derived from T1w data as inputs, and reduced the mean absolute error (MAE) of age prediction to 3.08 years and improved Pearson's r to 0.97 on 154 HCs. The strong generalizability of our model was also validated across different centers. Regions contributing the most significantly to brain age prediction were the caudate and thalamus for HCs and patients with mTBI, and the contributive regions were mostly located in the subcortical areas throughout the adult lifespan. The left hemisphere was confirmed to contribute more in brain age prediction throughout the adult lifespan. Our research showed that brain-PAG in mTBI patients was significantly higher than that in HCs in both acute and chronic phases. The increased brain-PAG in mTBI patients was also highly correlated with cognitive impairment and a higher level of plasma neurofilament light, a marker of neurodegeneration. The higher brain-PAG and its correlation with severe cognitive impairment showed a longitudinal and persistent nature in patients with follow-up examinations. CONCLUSION: We proposed an interpretable deep learning framework on a relatively large dataset to accurately predict brain age in both healthy individuals and mTBI patients. The interpretable analysis revealed that the caudate and thalamus became the most contributive role across the adult lifespan in both HCs and patients with mTBI. The left hemisphere contributed significantly to brain age prediction may enlighten us to be concerned about the lateralization of brain abnormality in neurological diseases in the future. The proposed interpretable deep learning framework might also provide hope for testing the performance of related drugs and treatments in the future.

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EAAI Journal 2024 Journal Article

High-throughput soybean pods high-quality segmentation and seed-per-pod estimation for soybean plant breeding

Si Yang
Lihua Zheng
Tingting Wu
Shi Sun
Man Zhang
Minzan Li
Minjuan Wang

Accurately identifying soybean pods is a crucial prerequisite for retrieving multi-phenotypic traits (such as number of pods per plant, number of seeds per pod, pod size, pod color, and pod shape). However, the traditional manual measurement approach for pod phenotype investigation is time-consuming and labor-intensive, particularly when counting the number of seeds per pod. Furthermore, existing instance segmentation methods designed for coarse-grained classification are inadequate for precise seed-per-pod estimation. To address these challenges, we modified an instance segmentation network for high-throughput soybean pods high-quality segmentation and accurate seed-per-pod estimation. We modified the classification branch of the instance segmentation network Mask Transfiner (Ke et al. , 2022) by increasing the resolution of feature map of each Region of Interest (RoI) region, incorporating a dual attention mechanism, and leveraging a center loss function, named RefinePod. To overcome the limitation of scarce labeled data, we modified our synthesizing image method to automatedly generate fine-labeled multi-class soybean pods images. We then train RefinePod purely with these synthetic images. Subsequently, we evaluate the trained model on both synthetic and real test images. Experimental results demonstrate a significant improvement in the accuracy of seed-per-pod estimation achieved by RefinePod. Additionally, we conduct ablation experiments to analyze the individual contributions of each strategy employed in RefinePod. In summary, RefinePod achieves remarkable results in seed-per-pod estimation accuracy by integrating advanced techniques and leveraging synthetic images for training. Our findings highlight the potential of RefinePod for accelerating soybean phenotype investigation, enabling more efficient agricultural research and crop improvement initiatives.

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AAAI Conference 2024 Conference Paper

Low-Rank Kernel Tensor Learning for Incomplete Multi-View Clustering

Tingting Wu
Songhe Feng
Jiazheng Yuan

Incomplete Multiple Kernel Clustering algorithms, which aim to learn a common latent representation from pre-constructed incomplete multiple kernels from the original data, followed by k-means for clustering. They have attracted intensive attention due to their high computational efficiency. However, our observation reveals that the imputation of these approaches for each kernel ignores the influence of other incomplete kernels. In light of this, we present a novel method called Low-Rank Kernel Tensor Learning for Incomplete Multiple Views Clustering (LRKT-IMVC) to address the above issue. Specifically, LRKT-IMVC first introduces the concept of kernel tensor to explore the inter-view correlations, and then the low-rank kernel tensor constraint is used to further capture the consistency information to impute missing kernel elements, thereby improving the quality of clustering. Moreover, we carefully design an alternative optimization method with promising convergence to solve the resulting optimization problem. The proposed method is compared with recent advances in experiments with different missing ratios on seven well-known datasets, demonstrating its effectiveness and the advantages of the proposed interpolation method.

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YNIMG Journal 2023 Journal Article

Resource sharing in cognitive control: Behavioral evidence and neural substrates

Tingting Wu
Alfredo Spagna
Melissa-Ann Mackie
Jin Fan

Cognitive control is a capacity-limited function responsible for the resolution of conflict among competing cognitive processes. However, whether cognitive control handles multiple concurrent requests through a single bottleneck or a resource sharing mechanism remains elusive. In this functional magnetic resonance imaging study, we examined the effect of dual flanker conflict processing on behavioral performance and on activation in regions of the cognitive control network (CCN). In each trial, participants completed two flanker conflict tasks (T1 and T2) sequentially, with the stimulus onset asynchrony (SOA) varied as short (100 ms) and long (1000 ms). We found a significant conflict effect (indexed by the difference between incongruent and congruent flanker conditions) in reaction time (RT) for both T1 and T2, together with a significant interaction between SOA and T1-conflict on RT for T2 with an additive effect. Importantly, there was a small but significant SOA effect on T1 with a prolonged RT under the short SOA compared to the long SOA. Increased activation in the CCN was associated with conflict processing and the main effect of SOA. The anterior cingulate cortex and anterior insular cortex showed a significant interaction effect between SOA and T1-conflict in activation parallel with the behavioral results. The behavioral and brain activation patterns support a central resource sharing model, in which the core resources for cognitive control are shared when multiple simultaneous conflicting processes are required.

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YNIMG Journal 2022 Journal Article

Representational coding of overt and covert orienting of visuospatial attention in the frontoparietal network

Tingting Wu
Melissa-Ann Mackie
Chao Chen
Jin Fan

Orienting of visuospatial attention refers to reallocation of attentional focus from one target or location to another and can occur either with (overt) or without (covert) eye movement. Although it has been demonstrated that both types of orienting commonly involve frontal and parietal brain regions as the frontoparietal network (FPN), the underlying representational coding of these two types of orienting remains unclear. In this functional magnetic resonance imaging study, participants performed a task that elicited overt and covert orienting to endogenously or exogenously cued targets with eye-tracking to monitor eye movement. Although the FPN was commonly activated for both overt and covert orienting, multivariate patterns of the activation of voxels in the FPN accurately predicted whether eye movements were involved or not during orienting. These overt- and covert-preferred voxels were topologically distributed as distinct and interlaced clusters in a millimeter scale. Inclusion of the two types of clusters predicted orienting type more accurately than one type of clusters alone. These findings suggest that overt and covert orienting are represented by interdependent functional clusters of neuronal populations in regions of the FPN, which might reflect a generalizable principle in the nervous system for functional organization of closely associated processes.

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YNIMG Journal 2021 Journal Article

Activation of the cognitive control network associated with information uncertainty

Tingting Wu
Kurt P. Schulz
Jin Fan

The cognitive control network (CCN) that comprises regions of the frontoparietal network, the cingulo-opercular network, and other sub-cortical regions as core structures is commonly activated by events with an increase in information uncertainty. However, it is not clear whether this CCN activation is associated with both information entropy that represents the information conveyed by the context formed by a sequence of events and the surprise that quantifies the information conveyed by a specific type of event in the context. We manipulated entropy and surprise in this functional magnetic resonance imaging study by varying the probability of occurrence of two types of events in both the visual and auditory modalities and measured brain response as a function of entropy and surprise. We found that activation in regions of the CCN increased as a function of entropy and surprise in both the visual and auditory tasks. The frontoparietal network and additional structures in the CCN mediated the relationship between these information measures and behavioral response. These results suggest that the CCN is a high-level modality-general neural entity for the control of the processing of information conveyed by both context and event.

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YNIMG Journal 2019 Journal Article

Anterior insular cortex is a bottleneck of cognitive control

Tingting Wu
Xingchao Wang
Qiong Wu
Alfredo Spagna
Jiaqi Yang
Changhe Yuan
Yanhong Wu
Zhixian Gao

Cognitive control, with a limited capacity, is a core process in human cognition for the coordination of thoughts and actions. Although the regions involved in cognitive control have been identified as the cognitive control network (CCN), it is still unclear whether a specific region of the CCN serves as a bottleneck limiting the capacity of cognitive control (CCC). Here, we used a perceptual decision-making task with conditions of high cognitive load to challenge the CCN and to assess the CCC in a functional magnetic resonance imaging study. We found that the activation of the right anterior insular cortex (AIC) of the CCN increased monotonically as a function of cognitive load, reached its plateau early, and showed a significant correlation to the CCC. In a subsequent study of patients with unilateral lesions of the AIC, we found that lesions of the AIC were associated with a significant impairment of the CCC. Simulated lesions of the AIC resulted in a reduction of the global efficiency of the CCN in a network analysis. These findings suggest that the AIC, as a critical hub in the CCN, is a bottleneck of cognitive control.

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YNIMG Journal 2016 Journal Article

The activation of interactive attentional networks

Bin Xuan
Melissa-Ann Mackie
Alfredo Spagna
Tingting Wu
Yanghua Tian
Patrick R. Hof
Jin Fan

Attention can be conceptualized as comprising the functions of alerting, orienting, and executive control. Although the independence of these functions has been demonstrated, the neural mechanisms underlying their interactions remain unclear. Using the revised attention network test and functional magnetic resonance imaging, we examined cortical and subcortical activity related to these attentional functions and their interactions. Results showed that areas in the extended frontoparietal network (FPN), including dorsolateral prefrontal cortex, frontal eye fields (FEF), areas near and along the intraparietal sulcus, anterior cingulate and anterior insular cortices, basal ganglia, and thalamus were activated across multiple attentional functions. Specifically, the alerting function was associated with activation in the locus coeruleus (LC) in addition to regions in the FPN. The orienting functions were associated with activation in the superior colliculus (SC) and the FEF. The executive control function was mainly associated with activation of the FPN and cerebellum. The interaction effect of alerting by executive control was also associated with activation of the FPN, while the interaction effect of orienting validity by executive control was mainly associated with the activation in the pulvinar. The current findings demonstrate that cortical and specific subcortical areas play a pivotal role in the implementation of attentional functions and underlie their dynamic interactions.

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