A feature enhanced EEG compression model using asymmetric encoding-decoding network.

Objective.Recently, the demand for wearable devices using electroencephalography (EEG) has increased rapidly in many fields. Due to its volume and computation constraints, wearable devices usually compress and transmit EEG to external devices for analysis. However, current EEG compression algorithms are not tailor-made for wearable devices with limited computing and storage. Firstly, the huge amount of parameters makes it difficult to apply in wearable devices; secondly, it is tricky to learn EEG signals' distribution law due to the low signal-to-noise ratio, which leads to excessive reconstruction error and suboptimal compression performance.Approach.Here, a feature enhanced asymmetric encoding-decoding network is proposed. EEG is encoded with a lightweight model, and subsequently decoded with a multi-level feature fusion network by extracting the encoded features deeply and reconstructing the signal through a two-branch structure.Main results.On public EEG datasets, motor imagery and event-related potentials, experimental results show that the proposed method has achieved the state of the art compression performance. In addition, the neural representation analysis and the classification performance of the reconstructed EEG signals also show that our method tends to retain more task-related information as the compression ratio increases and retains reliable discriminative information after EEG compression.Significance.This paper tailors an asymmetric EEG compression method for wearable devices that achieves state-of-the-art compression performance in a lightweight manner, paving the way for the application of EEG-based wearable devices.

s-TBN: A New Neural Decoding Model to Identify Stimulus Categories From Brain Activity Patterns.

Neural decoding is still a challenging and a hot topic in neurocomputing science. Recently, many studies have shown that brain network patterns containing rich spatiotemporal structural information represent the brain's activation information under external stimuli. In the traditional method, brain network features are directly obtained using the standard machine learning method and provide to a classifier, subsequently decoding external stimuli. However, this method cannot effectively extract the multidimensional structural information hidden in the brain network. Furthermore, studies on tensors have show that the tensor decomposition model can fully mine unique spatiotemporal structural characteristics of a spatiotemporal structure in data with a multidimensional structure. This research proposed a stimulus-constrained Tensor Brain Network (s-TBN) model that involves the tensor decomposition and stimulus category-constraint information. The model was verified on real neuroimaging data obtained via magnetoencephalograph and functional mangetic resonance imaging). Experimental results show that the s-TBN model achieve accuracy matrices of greater than 11.06% and 18.46% on the accuracy matrix compared with other methods on two modal datasets. These results prove the superiority of extracting discriminative characteristics using the STN model, especially for decoding object stimuli with semantic information.

Decoding Human Interaction Type from Inter-brain Synchronization by Using EEG Brain Network.

Cooperation and competition are two common forms of interpersonal interactions and exploring inter-brain synchronization in these two forms can help to further deliberate the underlying neural mechanisms of interpersonal interactions. Recently, studies revealed that electrode-paired inter-brain synchronization plays an important role in human interactions. This study investigated the neural correlates of interpersonal synchronization at the brain network scale and interaction type. Firstly, the network-wise inter-brain synchronization (NIBS) index reflecting cross-brain network synchronization from the global brain perspective was advanced. Secondly, statistical analysis demonstrated that there are differences in NIBS activities between cooperative and competitive interactions. And a row-filtered depthwise separable convolution network was proposed to classify the NIBS features. Results of EEG hyper-scanning data showed significant differences in NIBS between cooperative and competitive tasks, and a comparative study manifested that the cross-brain synchronization in cooperative tasks is more consistent than that of competitive tasks. The neural decoder using a modified convolution network achieved a peak accuracy of 96.05% under the binary classification(cooperation vs competition).

Advanced Reinforcement Learning and Its Connections with Brain Neuroscience.

In recent years, brain science and neuroscience have greatly propelled the innovation of computer science. In particular, knowledge from the neurobiology and neuropsychology of the brain revolutionized the development of reinforcement learning (RL) by providing novel interpretable mechanisms of how the brain achieves intelligent and efficient decision making. Triggered by this, there has been a boom in research about advanced RL algorithms that are built upon the inspirations of brain neuroscience. In this work, to further strengthen the bidirectional link between the 2 communities and especially promote the research on modern RL technology, we provide a comprehensive survey of recent advances in the area of brain-inspired/related RL algorithms. We start with basis theories of RL, and present a concise introduction to brain neuroscience related to RL. Then, we classify these advanced RL methodologies into 3 categories according to different connections of the brain, i.e., micro-neural activity, macro-brain structure, and cognitive function. Each category is further surveyed by presenting several modern RL algorithms along with their mathematical models, correlations with the brain, and open issues. Finally, we introduce several important applications of RL algorithms, followed by the discussions of challenges and opportunities for future research.

Applying Interleaving Strategy of Learning Materials and Perceptual Modality to Address Secondary Students' Need to Restore Cognitive Capacity.

Online courses are prevalent around the world, especially during the COVID-19 pandemic. Long hours of highly demanding online learning can lead to mental fatigue and cognitive depletion. According to Attention Restoration Theory, 'being away' or a mental shift could be an important strategy to allow a person to recover from the cognitive overload. The present study aimed to test the interleaving strategy as a mental shift method to help sustain students' online learning attention and to improve learning outcomes. A total of 81 seventh-grade Chinese students were randomly assigned to four learning conditions: blocked (by subject matter) micro-lectures with auditory textual information (B-A condition), blocked (by subject matter) micro-lectures with visual textual information (B-V condition), interleaved (by subject matter) micro-lectures with auditory textual information (I-A condition), and interleaved micro-lectures by both perceptual modality and subject matter (I-all condition). We collected self-reported data on subjective cognitive load (SCL) and attention level, EEG data during the 40 min of online learning, and test results to assess learning outcomes. The results showed that the I-all condition showed the best overall outcomes (best performance, low SCL, and high attention). This study suggests that interleaving by both subject matter and perceptual modality should be preferred in scheduling and planning online classes.

Categorizing objects from MEG signals using EEGNet.

Magnetoencephalography (MEG) signals have demonstrated their practical application to reading human minds. Current neural decoding studies have made great progress to build subject-wise decoding models to extract and discriminate the temporal/spatial features in neural signals. In this paper, we used a compact convolutional neural network-EEGNet-to build a common decoder across subjects, which deciphered the categories of objects (faces, tools, animals, and scenes) from MEG data. This study investigated the influence of the spatiotemporal structure of MEG on EEGNet's classification performance. Furthermore, the EEGNet replaced its convolution layers with two sets of parallel convolution structures to extract the spatial and temporal features simultaneously. Our results showed that the organization of MEG data fed into the EEGNet has an effect on EEGNet classification accuracy, and the parallel convolution structures in EEGNet are beneficial to extracting and fusing spatial and temporal MEG features. The classification accuracy demonstrated that the EEGNet succeeds in building the common decoder model across subjects, and outperforms several state-of-the-art feature fusing methods.

Domain adaptation based on rough adjoint inconsistency and optimal transport for identifying autistic patients.

BACKGROUND AND OBJECTIVE: Computer aided diagnosis technology has been widely used to diagnose autism spectrum disorder (ASD) from neural images. The performance of the model usually depends largely on a sufficient number of training samples that reflect the real sample distribution. Due to the lack of labelled neural images data, multisite data are often pooled together to expand the sample size. However, the heterogeneity among sites will inevitably lead to a decline in the generalization of models. To solve this problem, we propose a multisource unsupervised domain adaptation method using rough adjoint inconsistency and optimal transport. METHODS: First, we define the concept of rough adjoint inconsistency and propose a double quantization method based on rough adjoint inconsistency and Dempster-Shafer (D-S) evidence theory to estimate the weight coefficient of each source domain to accurately describe the importance of each source domain to the target domain. Second, using optimal transport theory, we weaken the data distribution differences between domains and solve the problem of class imbalance by adjusting the sampling weights among classes. RESULTS: The ASD recognition accuracy of the proposed method is improved on all eight tasks, which are 70.67%, 64.86%, 62.50%, 70.80%, 73.08%, 71.19%, 75.41% and 75.76%, respectively. Our proposed model achieves superior performance compared to traditional machine learning methods and other recently proposed deep learning model. CONCLUSIONS: Our method demonstrates that the fusion of rough adjoint inconsistency and optimal transport can be a powerful tool for identifying ASD and quantifying the correlations between domains.

Self-Supervised Node Classification with Strategy and Actively Selected Labeled Set.

To alleviate the impact of insufficient labels in less-labeled classification problems, self-supervised learning improves the performance of graph neural networks (GNNs) by focusing on the information of unlabeled nodes. However, none of the existing self-supervised pretext tasks perform optimally on different datasets, and the choice of hyperparameters is also included when combining self-supervised and supervised tasks. To select the best-performing self-supervised pretext task for each dataset and optimize the hyperparameters with no expert experience needed, we propose a novel auto graph self-supervised learning framework and enhance this framework with a one-shot active learning method. Experimental results on three real world citation datasets show that training GNNs with automatically optimized pretext tasks can achieve or even surpass the classification accuracy obtained with manually designed pretext tasks. On this basis, compared with using randomly selected labeled nodes, using actively selected labeled nodes can further improve the classification performance of GNNs. Both the active selection and the automatic optimization contribute to semi-supervised node classification.

Domain Adaptation Using a Three-Way Decision Improves the Identification of Autism Patients from Multisite fMRI Data.

Machine learning methods are widely used in autism spectrum disorder (ASD) diagnosis. Due to the lack of labelled ASD data, multisite data are often pooled together to expand the sample size. However, the heterogeneity that exists among different sites leads to the degeneration of machine learning models. Herein, the three-way decision theory was introduced into unsupervised domain adaptation in the first time, and applied to optimize the pseudolabel of the target domain/site from functional magnetic resonance imaging (fMRI) features related to ASD patients. The experimental results using multisite fMRI data show that our method not only narrows the gap of the sample distribution among domains but is also superior to the state-of-the-art domain adaptation methods in ASD recognition. Specifically, the ASD recognition accuracy of the proposed method is improved on all the six tasks, by 70.80%, 75.41%, 69.91%, 72.13%, 71.01% and 68.85%, respectively, compared with the existing methods.

Mitigation Effects of Bentonite and Yeast Cell Wall Binders on AFB1, DON, and OTA Induced Changes in Laying Hen Performance, Egg Quality, and Health.

The objective of this study was to evaluate the efficacy of mycotoxin binders in reducing the adverse effects of co-occurring dietary aflatoxin B1 (AFB1), deoxynivalenol (DON) and ochratoxin A (OTA) on laying hens. Three hundred and sixty 26-week-old Roman laying hens were randomly allocated into four experimental groups with 10 replicates of nine birds each. The four groups received either a basal diet (BD; Control), a BD supplemented with 0.15 mg/kg AFB1 + 1.5 mg/kg DON + 0.12 mg/kg OTA (Toxins), a BD + Toxins with Toxo-HP binder (Toxins + HP), or a BD + Toxins with TOXO XL binder (Toxins + XL) for 12 weeks. Compared to the control, dietary supplementation of mycotoxins decreased (P < 0.10) total feed intake, total egg weight, and egg-laying rate, but increased feed/egg ratio by 2.5-6.1% and mortality during various experimental periods. These alterations induced by mycotoxins were alleviated by supplementation with both TOXO HP and XL binders (P < 0.10). Furthermore, dietary mycotoxins reduced (P < 0.05) eggshell strength by 12.3% and caused an accumulation of 249 µg/kg of DON in eggs at week 12, while dietary supplementation with TOXO HP or XL mitigated DON-induced changes on eggshell strength and prevented accumulation of DON in eggs (P < 0.05). Moreover, dietary mycotoxins increased relative liver weight, but decreased spleen and proventriculus relative weights by 11.6-22.4% (P < 0.05). Mycotoxin exposure also increased alanine aminotransferase activity and reduced immunoglobulin (Ig) A, IgM, and IgG concentrations in serum by 9.2-26.1% (P < 0.05). Additionally, mycotoxin exposure induced histopathological damage and reduced villus height, villus height/crypt depth, and crypt depth in duodenum, jejunum and (or) ileum (P < 0.05). Notably, most of these histological changes were mitigated by supplementation with both TOXO HP and XL (P < 0.05). In conclusion, the present study demonstrated that the mycotoxin binders TOXO HP and XL can help to mitigate the combined effects of AFB1, DON, and OTA on laying hen performance, egg quality, and health.

Transcriptomics and flow cytometry reveals the cytotoxicity of aflatoxin B1 and aflatoxin M1 in bovine mammary epithelial cells.

Aflatoxin is a known mycotoxin that pollutes various grains widely in the environment. Aflatoxin B1 (AFB1) and Aflatoxin M1 (AFM1) have been shown to induce cytotoxicity in many cells, yet their effects on mammary epithelial cells remain unclear. In this study, we examined the toxicity and the effects of AFB1 and AFM1 on bovine mammary epithelial cells (BME cells). The cells were treated with AFB1 or AFM1 at a concentration of 0-10 mg/L for 24 or 48 h, followed by cytotoxicity assays, flow cytometry, and transcriptomics. Our results demonstrated that AFB1 and AFM1 induced cell proliferation inhibition, apoptosis and cell cycle arrest. However, the level of intracellular reactive oxygen species has no significant difference. The RNA-Seq results also showed that AFB1 and AFM1 changed many related gene expressions like apoptosis and oxidative stress, cycle, junction, and signaling pathway. Taken together, AFB1 and AFM1 were found to affect cytotoxicity and related gene changes in BME cells. Notably, this study reported that 2 mg/L of AFB1 and AFM1 affected the expression of methylation-related genes, and ultimately altered the rate of m6A methylation in RNA. It may provide a potential direction for toxins to indirectly regulate gene expression by affecting RNA methylation modification. Our research provides some novel insights and data about AFB1 and AFM1 toxicity in BME cells.

Rapidly Decoding Image Categories From MEG Data Using a Multivariate Short-Time FC Pattern Analysis Approach.

Recent advances in the development of multivariate analysis methods have led to the application of multivariate pattern analysis (MVPA) to investigate the interactions between brain regions using graph theory (functional connectivity, FC) and decode visual categories from functional magnetic resonance imaging (fMRI) data from a continuous multicategory paradigm. To estimate stable FC patterns from fMRI data, previous studies required long periods in the order of several minutes, in comparison to the human brain that categories visual stimuli within hundreds of milliseconds. Constructing short-time dynamic FC patterns in the order of milliseconds and decoding visual categories is a relatively novel concept. In this study, we developed a multivariate decoding algorithm based on FC patterns and applied it to magnetoencephalography (MEG) data. MEG data were recorded from participants presented with image stimuli in four categories (faces, scenes, animals and tools). MEG data from 17 participants demonstrate that short-time dynamic FC patterns yield brain activity patterns that can be used to decode visual categories with high accuracy. Our results show that FC patterns change over the time window, and FC patterns extracted in the time window of 0∼200 ms after the stimulus onset were most stable. Further, the categorizing accuracy peaked (the mean binary accuracy is above 78.6% at individual level) in the FC patterns estimated within the 0∼200 ms interval. These findings elucidate the underlying connectivity information during visual category processing on a relatively smaller time scale and demonstrate that the contribution of FC patterns to categorization fluctuates over time.

Effects of Thymol Supplementation on Goat Rumen Fermentation and Rumen Microbiota In Vitro.

This study was performed to explore the predominant responses of rumen microbiota with thymol supplementation as well as effective dose of thymol on rumen fermentation. Thymol at different concentrations, i.e., 0, 100 mg/L, 200 mg/L, and 400 mg/L (four groups × five replications) was applied for 24 h of fermentation in a rumen fluid incubation system. Illumina MiSeq sequencing was applied to investigate the ruminal microbes in addition to the examination of rumen fermentation. Thymol doses reached 200 mg/L and significantly decreased (p < 0.05) total gas production (TGP) and methane production; the production of total volatile fatty acids (VFA), propionate, and ammonia nitrogen, and the digestibility of dry matter and organic matter were apparently decreased (p < 0.05) when the thymol dose reached 400 mg/L. A thymol dose of 200 mg/L significantly affected (p < 0.05) the relative abundance of 14 genera of bacteria, three species of archaea, and two genera of protozoa. Network analysis showed that bacteria, archaea, and protozoa significantly correlated with methane production and VFA production. This study indicates an optimal dose of thymol at 200 mg/L to facilitate rumen fermentation, the critical roles of bacteria in rumen fermentation, and their interactions with the archaea and protozoa.

Individual-specific and shared representations during episodic memory encoding and retrieval.

Although human memories seem unique to each individual, they are shared to a great extent across individuals. Previous studies have examined, separately, subject-specific and cross-subject shared representations during memory encoding and retrieval, but how shared memories are formed from individually encoded representations is not clearly understood. Using a unique fMRI design involving memory encoding and retrieval, and representational similarity analysis to link representations from different individuals, brain regions, and processing stages, the current study revealed that distributed brain regions showed both subject-specific and shared neural representations during both memory encoding and retrieval. Furthermore, different brain regions showed stage-specific representational strength, with the visual cortex showing greater unique and shared representations during encoding, whereas the left angular gyrus showing greater unique and shared representations during retrieval. The neural representations during encoding were transformed during retrieval, as shown by smaller cross-subject encoding-retrieval similarity (ERS) than cross-subject similarity either during encoding or during retrieval. This cross-subject and cross-stage similarity was found both within and across regions, with strong pattern similarity between the encoded representation in VVC and the retrieved representation in the angular gyrus. Simulation analysis further suggested that these patterns could be achieved by incorporating stage-specific representational strength, and cross-region reinstatement from encoding to retrieval, but not by a common transformation from encoding to retrieval across subjects. Together, our results shed light on how memory representations are encoded and transformed to maintain individual characteristics and at the same time to create shared representations to facilitate interpersonal communication.

Yeast culture promotes the production of aged laying hens by improving intestinal digestive enzyme activities and the intestinal health status.

Yeast culture (YC) positively affects the performance of laying hens. The purpose of the present study was to explore the underlying mechanism for the YC-mediated performance improvement. Sixty 67-week-old Hy-Line Brown laying hens were randomly allocated into 2 experimental groups with 5 replicates of 6 birds each. One group was fed a control diet, whereas the other received the control diet supplemented with YC at 3.0 g/kg; treatment lasted for 8 wk. The results showed that dietary YC supplementation increased (P < 0.05) the total egg weight (11.2-13.6%) and egg-laying rate (13.0-13.5%) but decreased (P < 0.05) the feed/egg ratio by 9.3 to 11.0% during weeks 5 to 6 and 7 to 8 compared with the control. However, egg quality, including eggshell strength, eggshell thickness, egg weight, albumen height, egg yolk color, and Haugh unit, was not affected (P > 0.05) by YC supplementation. Furthermore, dietary YC supplementation increased (P < 0.05) chymotrypsin and ɑ-amylase activities by 54.8 to 62.5% in the duodenal chyme and reduced (P < 0.05) plasma endotoxin by 44.1%. YC dietary supplementation also upregulated (P < 0.05) the mRNA levels of intestinal barrier-related genes (occludin and claudin 1) and antimicrobial peptides genes (ß-defensin 1 and 7 and cathelicidin 1 and 3) in the duodenum or jejunum compared with the control. In conclusion, dietary YC supplementation improved the performance of aged laying hens, potentially through the upregulation of intestinal digestive enzyme activities and intestinal health-related gene expression.

Decoding disparity categories in 3-dimensional images from fMRI data using functional connectivity patterns.

Humans use binocular disparity to extract depth information from two-dimensional retinal images in a process called stereopsis. Previous studies usually introduce the standard univariate analysis to describe the correlation between disparity level and brain activity within a given brain region based on functional magnetic resonance imaging (fMRI) data. Recently, multivariate pattern analysis has been developed to extract activity patterns across multiple voxels for deciphering categories of binocular disparity. However, the functional connectivity (FC) of patterns based on regions of interest or voxels and their mapping onto disparity category perception remain unknown. The present study extracted functional connectivity patterns for three disparity conditions (crossed disparity, uncrossed disparity, and zero disparity) at distinct spatial scales to decode the binocular disparity. Results of 27 subjects' fMRI data demonstrate that FC features are more discriminatory than traditional voxel activity features in binocular disparity classification. The average binary classification of the whole brain and visual areas are respectively 87% and 79% at single subject level, and thus above the chance level (50%). Our research highlights the importance of exploring functional connectivity patterns to achieve a novel understanding of 3D image processing.

Sodium Butyrate Protects the Intestinal Barrier by Modulating Intestinal Host Defense Peptide Expression and Gut Microbiota after a Challenge with Deoxynivalenol in Weaned Piglets.

This study aims to determine whether sodium butyrate (SB) could antagonize deoxynivalenol (DON)-induced intestinal epithelial dysfunction. In a four-week feeding trial, twenty-eight barrows were randomly divided into four treatments: (1) uncontaminated basal diet (control); (2) 4 mg/kg DON-contaminated diet (DON); (3) basal diet supplemented with 0.2% SB (SB); and (4) 4 mg/kg DON + 0.2% SB (DON + SB). A decrease in performance was observed in DON-exposed animals, which was prevented by the dietary SB supplementation. DON exposure also depressed the expression of host defense peptides (HDPs) in the intestine, impaired the intestinal barrier integrity, and disturbed the gut microbiota homeostasis. These alterations induced by DON were attenuated by SB supplementation. The supplementation of 0.2% SB ameliorated the adverse effects of DON on the liver in terms of hepatic lesions as well as serum concentrations of alkaline phosphatase and aspartate aminotransferase. In IPEC-J2 cells, pretreatment with SB alleviated the DON-induced decreased cell viability. Additionally, the NOD2/caspase-12 pathway participated in the alleviation of SB on DON-induced diminished HDP expression. Taken together, these data demonstrated that SB protected piglets from DON-induced intestinal barrier dysfunction potentially through stimulation of intestinal HDP assembly and regulation in gut microbiota.

Decoding Brain States From fMRI Signals by Using Unsupervised Domain Adaptation.

With the development of deep learning in medical image analysis, decoding brain states from functional magnetic resonance imaging (fMRI) signals has made significant progress. Previous studies often utilized deep neural networks to automatically classify brain activity patterns related to diverse cognitive states. However, due to the individual differences between subjects and the variation in acquisition parameters across devices, the inconsistency in data distributions degrades the performance of cross-subject decoding. Besides, most current networks were trained in a supervised way, which is not suitable for the actual scenarios in which massive amounts of data are unlabeled. To address these problems, we proposed the deep cross-subject adaptation decoding (DCAD) framework to decipher the brain states. The proposed volume-based 3D feature extraction architecture can automatically learn the common spatiotemporal features of labeled source data to generate a distinct descriptor. Then, the distance between the source and target distributions is minimized via an unsupervised domain adaptation (UDA) method, which can help to accurately decode the cognitive states across subjects. The performance of the DCAD was evaluated on task-fMRI (tfMRI) dataset from the Human Connectome Project (HCP). Experimental results showed that the proposed method achieved the state-of-the-art decoding performance with mean 81.9% and 84.9% accuracies under two conditions (4 brain states and 9 brain states respectively) of working memory task. Our findings also demonstrated that UDA can mitigate the impact of the data distribution shift, thereby providing a superior choice for increasing the performance of cross-subject decoding without depending on annotations.

The paradoxical effects of progesterone on the eggshell quality of laying hens.

This study demonstrates the effects of progesterone on eggshell quality and ultrastructure by injecting progesterone into laying hens 2 and 5 h post-oviposition, respectively. Progesterone injected 2 h post-oviposition (P4-2 h) improved eggshell quality with a significant decrease (P < 0.01) in the thickness of the mammillary layer and a significant increase (P < 0.01) in the thickness of the effective layer in the eggshell ultrastructure compared to the control. Progesterone injected 5 h post-oviposition (P4-5 h) damaged the eggshell quality by significantly reducing (P < 0.01) the effective layer thickness. Progesterone injected delayed obviously (P < 0.01) the following oviposition. Moreover, the concentrations of Thr, Cys, Leu, Lys, and His in the eggshell membranes were significantly higher (P < 0.05) in the P4-2 h treated hens whereas Val and Lys were significantly lower (P < 0.05) in P4-5 h treated hens compared to the control. Therefore, progesterone shows paradoxical effects on eggshell quality depending on the injection time-points post-oviposition, which could explain the contradictions in previous related reports. P4 injected affected the content of amino acids in eggshell membranes, especially lysine which contributed to eggshell quality. In addition, P4 injected 2 h after oviposition improved eggshell quality by promoting the premature fusion of mammillary knobs. This work contributed to a novel insight to understanding the mechanism of improving eggshell quality.

Deoxynivalenol Inhibits Porcine Intestinal Trefoil Factors Expression in Weanling Piglets and IPEC-J2 Cells.

Trefoil factors (TFFs) are regulatory peptides playing critical roles in mucosal repair and protection against a variety of insults within the gastrointestinal tract. This work aimed to explore the effects of deoxynivalenol (DON) on intestinal TFFs expression using in vivo and in vitro models. In an animal trial, twenty-four 28-d-old barrows (Duroc × Landrace × Large White; initial body weight = 7.6 ± 0.7 kg) were randomly divided into three treatments for 28 days, including a control diet (0.61 mg DON/kg feed), and two levels of DON-contaminated diets containing 1.28 and 2.89 mg DON/kg feed, respectively. Piglets exposed to DON had lower mRNA expression of TFF1, TFF2, TFF3, as well as Claudin-4 in the intestine (P < 0.05). Dietary DON exposure decreased the protein levels of TFF2 and TFF3 in the jejunum as demonstrated by western blot and immunohistochemistry. In intestinal porcine epithelial cells (IPEC-J2), DON depressed the mRNA expression of TFF2, TFF3, and Claudin-4. Overexpression of sterile alpha motif (SAM) pointed domain E26 transformation-specific (ETS) factor (SPDEF) was found to attenuate DON-induced suppression of TFFs in IPEC-J2 cells. Altogether, our work shows, for the first time, that dietary DON exposure depresses the expression of intestinal TFFs in piglets. Given the fundamental role of TFFs in intestinal mucosal homeostasis, our observations indicate that the DON content in animal feed should be strictly controlled based on the existing regulation for DON.