工程技术最新文献

One of the areas where artificial intelligence (AI) technologies are used is the detection and diagnosis of mental disorders. AI approaches, including machine learning and deep learning models, can identify early signs of bipolar disorder, schizophrenia, autism spectrum disorder, depression, suicidality, and dementia by analyzing speech patterns, behaviors, and physiological data. These approaches increase diagnostic accuracy and enable timely intervention, which is crucial for effective treatment. This paper presents a comprehensive literature review of AI approaches applied to mental disorder detection using various data sources, such as survey, Electroencephalography (EEG) signal, text and image. Applications include predicting anxiety and depression levels in online games, detecting schizophrenia from EEG signals, detecting autism spectrum disorder, analyzing text-based indicators of suicidality and depression, and diagnosing dementia from magnetic resonance imaging images. eXtreme Gradient Boosting (XGBoost), light gradient-boosting machine (LightGBM), random forest (RF), support vector machine (SVM), K-nearest neighbor were designed as machine learning models, and convolutional neural networks (CNN), long short-term memory (LSTM) and gated recurrent unit (GRU) models suitable for the dataset were designed as deep learning models. Data preprocessing techniques such as wavelet transforms, normalization, clustering were used to optimize model performances, and hyperparameter optimization and feature extraction were performed. While the LightGBM technique had the highest performance with 96% accuracy for anxiety and depression prediction, the optimized SVM stood out with 97% accuracy. Autism spectrum disorder classification reached 98% accuracy with XGBoost, RF and LightGBM. The LSTM model achieved a high accuracy of 83% in schizophrenia diagnosis. The GRU model showed the best performance with 93% accuracy in text-based suicide and depression detection. In the detection of dementia, LSTM and GRU models have demonstrated their effectiveness in data analysis by reaching 99% accuracy. The findings of the study highlight the effectiveness of LSTM and GRU for sequential data analysis and their applicability in medical imaging or natural language processing. XGBoost and LightGBM are noted to be highly accurate ML tools for clinical diagnoses. In addition, hyperparameter optimization and advanced data pre-processing approaches are confirmed to significantly improve model performance. The results obtained with this study have revealed the potential to improve clinical decision support systems for mental disorders with AI, facilitating early diagnosis and personalized treatment strategies.

Genetically modified crop adoption in Canada has been the key driver in removing tillage as the lead form of weed control, due to increased weed control efficiency. Land use has transitioned from the use of summerfallow to continuous cropping, predominantly involving zero or minimum tillage practices. Prairie crop rotations have diversified away from mainly cereals to include three-year rotations of cereals, pulses, and oilseeds. Total herbicide volume applied has increased as crop production acres increased, but the rate of herbicide active ingredient applied per hectare has declined. Diverse crop rotations allow for weed control using herbicides with different modes of action, reducing selection pressure for resistant weed development. Herbicide-resistant weeds are an important concern for farmers, as the loss of key herbicides would make weed control exceedingly more difficult. The objective of this case study is to examine herbicide resistance weed development in the Canadian Prairies and to identify changes in resistance development following GM crop adoption.

The main aim of this study is to propose a novel convolutional neural network, named BrainNeXt, for the automated brain disorders detection using magnetic resonance images (MRI) images. Furthermore, we aim to investigate the performance of our proposed network on various medical applications. To achieve high/robust image classification performance, we gathered a new MRI dataset belonging to four classes: (1) Alzheimer's disease, (2) chronic ischemia, (3) multiple sclerosis, and (4) control. Inspired by ConvNeXt, we designed BrainNeXt as a lightweight classification model by incorporating the structural elements of the Swin Transformers Tiny model. By training our model on the collected dataset, a pretrained BrainNeXt model was obtained. Additionally, we have suggested a feature engineering (FE) approach based on the pretrained BrainNeXt, which extracted features from fixed-sized patches. To select the most discriminative/informative features, we employed the neighborhood component analysis selector in the feature selection phase. As the classifier for our patch-based FE approach, we utilized the support vector machine classifier. Our recommended BrainNeXt approach achieved an accuracy of 100% and 91.35% for training and validation. The recommended model obtained the test classification accuracy of 94.21%. To further improve the classification performance, we suggested a patch-based DFE approach, which achieved a test accuracy of 99.73%. The obtained results, surpassing 90% accuracy on the test dataset, demonstrate the effectiveness and high classification performance of the proposed models.

Recent studies combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have shown promising results linking neural and vascular responses. This study analyzes the topographical effect of auditory stimulus intensity on cortical activation and explores neurovascular coupling between fNIRS hemodynamic signals and auditory-evoked potentials (AEPs), extracted from EEG. Forty healthy volunteers (13 males, 27 females; mean age = 22.27 ± 3.96 years) listened to complex tones of varying intensities (50-, 70-, and 90-dB SPL) across seven frequencies (range of 400-2750 Hz) in blocks of five, while EEG and fNIRS were recorded. PERMANOVA analysis revealed that increasing intensity modulated hemodynamic activity, leading to amplitude changes and enhanced recruitment of auditory and prefrontal cortices. To isolate stimulus-specific activity, Spearman correlations were computed on residuals-components of AEPs and fNIRS responses with individual trends removed. The N1 amplitude increase was correlated with higher superior temporal gyrus (STG) and superior frontal gyrus (SFG) activity, and reduced activity in inferior frontal gyrus (IFG) for the oxygenated hemoglobin (HbO), while the deoxygenated hemoglobin (HbR) was associated with increased activity in one channel near the Supramarginal Gyrus (SMG). P2 amplitude increase was associated with higher activation in SFG and IFG for HbO, while for HbR with the activity in SMG, angular gyrus (AnG), SFG, and IFG. Additionally, internal correlations between fNIRS channels revealed strong associations within auditory and frontal regions. These findings provide insights into existing models of neurovascular coupling by showing how stimulus properties, such as intensity, modulate the relationship between neural activity and vascular responses.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10281-7.

Neural stem cell (NSC) possess the essential properties of pluripotency and self-renewal, making them promising candidates for the treatment of neurological disorders such as Alzheimer's disease (AD), Parkinson's disease (PD), and spinal cord injuries. While previous studies have identified the long non-coding RNAs (lncRNAs) Pnky as a regulator of NSC differentiation into neurons via RNA splicing, its role in NSC differentiation and proliferation through the Wnt/β-catenin pathway remains unclear. In this study, we investigated the mechanism by which Pnky influences the Wnt/β-catenin pathway to promote NSC differentiation into neurons. Using cck8 assays, western blot analysis, and quantitative polymerase chain reaction (qPCR), we found that Pnky knockdown significantly enhanced NSC proliferation and promoted their differentiation into neurons. Additionally, Pnky knockdown resulted in the downregulation of the neural stem cell marker Nestin and upregulation of the neuronal marker β3-Tubulin, through activation of the β-catenin signaling pathway. Conversely, inhibiting the β-catenin pathway hindered both NSC differentiation and proliferation. These findings suggest that targeting the Pnky-mediated Wnt/β-catenin pathway may offer novel strategies for the treatment, diagnosis, and drug development of central nervous system diseases.

Background: Epilepsy is a common disease of the nervous system. Recent advances in epigenetics have revealed DNA methylation as a key mechanism in epilepsy pathogenesis, particularly through dysregulation of GABAergic signaling. Baicalein has been shown to have anticonvulsant and neuroprotective effects. However, its epigenetic regulatory effects on GABA receptor function remain unexplored.

Methods: The status epilepticus (SE) model was induced by lithium chloride-pilocarpine (LiCl-PILO) in Sprague-Dawley (SD) rats. The rats were divided into control group, epileptic SE group and baicalein intervention group. Morris water maze (MWM) test, Nissl staining, immunofluorescence and enzyme-linked immunosorbent assay (ELISA) were used to detect cognitive functions and neuronal damage. Online sites, chromatin immunoprecipitation (ChIP) and western blotting were used to identify DNA methyltransferase 1 (DNMT1)-mediated methylation of gamma-aminobutyric acid type A receptor subunit delta (GABRD) promoter region.

Results: Baicalein treatment significantly prolonged the latency of SE onset and seizure onset, and improved the development of epilepsy. Meanwhile, baicalein improved the cognitive impairment in rats induced by LiCl-PILO. After treatment with baicalein, a sustained elevation in the number of neurons and NeuN levels was observed, along with a decrease in the contents of tumor necrosis factor -alpha (TNF-α), interleukin-1β (IL-1β), and ionized calcium-binding adapter molecule 1 (Iba-1) in the hippocampus. Mechanistically, baicalein interacted with DNMT1 to suppress GABRD promoter region methylation, thus increasing GABRD protein level in the hippocampus of rats induced by LiCl-PILO.

Conclusion: This study identifies DNMT1/GABRD axis as a novel epigenetic target for epilepsy intervention. Baicalein's ability to enhance tonic inhibition through demethylation of GABRD provides a groundbreaking strategy for drug-resistant epilepsy.

The metacognition of one's planning strategy constitutes a "second-level" of metacognition that goes beyond the knowledge and monitoring of one's cognition and refers to the ability to use awareness mechanisms to regulate execution of present or future actions effectively. This study investigated the relation between metacognition of one's planning strategy and the behavioral and electrophysiological (EEG) correlates that support strategic planning abilities during performance in a complex decision-making task. Moreover, a possible link between task execution, metacognition, and individual differences (i.e., personality profiles and decision-making styles) was explored. A modified version of the Tower of Hanoi task was proposed to a sample of healthy participants, while their behavioral and EEG neurofunctional correlates of strategic planning were collected throughout the task with decisional valence. After the task, a metacognitive scale, the 10-item Big Five Inventory, the General Decision-Making Style inventory, and the Maximization Scale were administered. Results showed that the metacognitive scale enables to differentiate between the specific dimensions and levels of metacognition that are related to strategic planning behavioral performance and decision. Higher EEG delta power over left frontal cortex (AF7) during task execution positively correlates with the metacognition of one's planning strategy for the whole sample. While increased beta activity over the left frontal cortex (AF7) during task execution, higher metacognitive beliefs of efficacy and less willingness to change their strategy a posteriori were correlated with specific personality profiles and decision-making styles. These findings allow researchers to delve deeper into the multiple facets of metacognition of one's planning strategy in decision-making.

Insomnia, as a common sleep disorder, is the most common complaints in medical practice affecting a large proportion of the population on a situational, recurrent or chronic basis. It has been demonstrated that, during wakefulness, patients with insomnia exhibit increased EEG power in theta, beta, and gamma band. However, the relevant mechanisms underlying such power changes are still lack of understanding. In this paper, by combining the neural computational model with the real EEG data, we focus on exploring what's behind the EEG power changes for insomniac. We first develop a modified Liley model, named FSR-Liley, by respectively considering the fast and slow synaptic responses in inhibitory neurons along with the one-way projection between them. Then we introduce a parameter selection and evaluation method based on Markov chain Monte Carlo algorithm and Wasserstein distance, by which the sensitive parameters are selected automatically, and meanwhile, the optimal values of selected parameters are evaluated. Finally, through combining with EEG data, we determine the sensitive parameters in FSR-Liley and accordingly provide the mechanistic hypotheses: (1) decrease in $P_{e{i}_f}$ , corresponding to the input from the thalamus to cortical inhibitory population with fast synaptic response, leads to the increased theta and beta power; (2) decrease in $N_{e{i}_f}$ , corresponding to the projection from cortical excitatory population to inhibitory population with fast synaptic response, causes the increased gamma power. The results in this paper provide insights into the mechanisms of EEG power changes in insomnia and establish a theoretical foundation to support further experimental research.

Granule cells (GCs) are mainly responsible for receiving and integrating information from the entorhinal cortex and transferring it to the hippocampus to accomplish memory-related functions such as pattern separation. Owing to the heterogeneity of GCs, there are also two other subtypes, namely semilunar granule cells (SGCs) and hilar ectopic granule cells (HEGCs). In order to investigate their differences, here we examine the disparities in dendritic integration among the different subtypes of GCs. By utilizing biological experimental data, we developed detailed multi-compartment models for each type of GC. Our findings reveal that under the excitatory synaptic inputs (mediated by AMPA receptors), the dendritic integration of GCs, SGCs and HEGCs are linear, sublinear, and supralinear respectively. Furthermore, we propose that the sublinear integration observed in SGCs may be attributed to a high density of V-type potassium channels (K $_{\text{V}}^{}$ ) distributed in dendrites with smaller volume and higher input resistance; while the supralinear integration seen in HEGCs may be due to a high density of T-type calcium channels (Ca $_{\text{T}}^{}$ ) distributed in dendrites with larger volume and lower input resistance. Additionally, sodium channels, six types of potassium channels (K $_{\text{A}}^{}$ , K $_{\text{M}}^{}$ , sK $_{\text{DR}}^{}$ , fK $_{\text{DR}}^{}$ , BK, SK), and two types of calcium channels (Ca $_{\text{N}}^{}$ , Ca $_{\text{L}}^{}$ ) have minimal influence on their respective integration modes. We also found different integration modes exhibit varied somatic firing rates when subjected to different spatial synaptic activation sets, the HEGCs with the supralinear integration demonstrate higher somatic firing rates than the SGCs with the sublinear integration. These results provide theoretical insights into understanding the distinct roles played by these three subtypes of granule cells in memory-related functions within the dentate gyrus.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10226-0.

Parkinson's disease is the neurodegenerative disorder which involves both neurons and non-neurons, and whose symptoms are usually represented by the error index and synchronization index in the computational study. This paper combines with the classical basal ganglia-thalamic network model and tripartite synapse model to explore the internal effects of astrocytes on the Parkinson's disease. The model simulates the firing patterns of the Parkinsonian state and healthy state, verifies the feasibility of the neural-glial model. The results show that the rate of production for IP $_3^{}$ modulate the frequency and amplitude of slow inward current for subthalamic nucleus, globus pallidus externa and interna in two modes. Increasing the rate of production for IP $_3^{}$ of subthalamic nucleus and globus pallidus externa can decrease the error index and presumably alleviate the Parkinson's disease. Increasing the rate of production for IP $_3^{}$ of globus pallidus externa and adjusting the rate of production for IP $_3^{}$ of subthalamic nucleus can result in the desynchronization of network in a regular way. These obtained results emphasize the effect of neurons (especially subthalamic nucleus and globus pallidus externa), astrocytes and their interaction on the Parkinson's disease. It enriches the evidence of involvement of astrocyte in Parkinson's disease, and proposes some cognitive points to the alleviation of Parkinson's disease.