Objective Evaluation of Pulse Width Using an Array Pulse Diagram.

BACKGROUND: Pulse width, which can reflect qi, blood excess, and deficiency, has been used for diagnosing diseases and determining the prognosis in traditional Chinese medicine (TCM). This study aimed to devise an objective method to measure the pulse width based on an array pulse diagram for objective diagnosis. METHODS: The channel 6, the region wherein the pulse wave signal is the strongest, is located in the middle of the pulse sensor array and at the guan position of cunkou during data collection. Therefore, the main wave (h1) time of the pulse wave was collected from the channel 6 through calculation. The left h1 time was collected from the remaining 11 channels. The amplitudes at these time points were extracted as the h1 amplitudes for each channel. However, the pulse width could not be calculated accurately at 12 points. Consequently, a bioharmonic spline interpolation algorithm was used to interpolate the h1 amplitude data obtained from the horizontal and vertical points, yielding 651 (31 × 21) h1 amplitude data. The 651 data points were converted into a heat map to intuitively calculate the pulse width. The pulse width was calculated by multiplying the number of grids on the vertical axis with the unit length of the grid. The pulse width was determined by TCM doctors to verify the pulse width measurement accuracy. Meanwhile, a color Doppler ultrasound examination of the volunteers' radial arteries was performed and the intravascular meridian widths of the radial artery compared with the calculated pulse widths to determine the reliability. RESULTS: The pulse width determined using the maximal h1 amplitude method was comparable with the radial artery intravascular meridian widths measured using color Doppler ultrasound. The h1 amplitude was higher in the high blood pressure group and the pulse width was greater. CONCLUSIONS: The pulse width determined using the maximal h1 amplitude was objective and accurate. Comparison between the pulse widths of the normal and high blood pressure groups verified the reliability of the method.

Machine learning prediction models for different stages of non-small cell lung cancer based on tongue and tumor marker: a pilot study.

OBJECTIVE: To analyze the tongue feature of NSCLC at different stages, as well as the correlation between tongue feature and tumor marker, and investigate the feasibility of establishing prediction models for NSCLC at different stages based on tongue feature and tumor marker. METHODS: Tongue images were collected from non-advanced NSCLC patients (n = 109) and advanced NSCLC patients (n = 110), analyzed the tongue images to obtain tongue feature, and analyzed the correlation between tongue feature and tumor marker in different stages of NSCLC. On this basis, six classifiers, decision tree, logistic regression, SVM, random forest, naive bayes, and neural network, were used to establish prediction models for different stages of NSCLC based on tongue feature and tumor marker. RESULTS: There were statistically significant differences in tongue feature between the non-advanced and advanced NSCLC groups. In the advanced NSCLC group, the number of indexes with statistically significant correlations between tongue feature and tumor marker was significantly higher than in the non-advanced NSCLC group, and the correlations were stronger. Support Vector Machine (SVM), decision tree, and logistic regression among the machine learning methods performed poorly in models with different stages of NSCLC. Neural network, random forest and naive bayes had better classification efficiency for the data set of tongue feature and tumor marker and baseline. The models' classification accuracies were 0.767 ± 0.081, 0.718 ± 0.062, and 0.688 ± 0.070, respectively, and the AUCs were 0.793 ± 0.086, 0.779 ± 0.075, and 0.771 ± 0.072, respectively. CONCLUSIONS: There were statistically significant differences in tongue feature between different stages of NSCLC, with advanced NSCLC tongue feature being more closely correlated with tumor marker. Due to the limited information, single data sources including baseline, tongue feature, and tumor marker cannot be used to identify the different stages of NSCLC in this pilot study. In addition to the logistic regression method, other machine learning methods, based on tumor marker and baseline data sets, can effectively improve the differential diagnosis efficiency of different stages of NSCLC by adding tongue image data, which requires further verification based on large sample studies in the future.

Deep Learning Multi-label Tongue Image Analysis and Its Application in a Population Undergoing Routine Medical Checkup.

Background: Research on intelligent tongue diagnosis is a main direction in the modernization of tongue diagnosis technology. Identification of tongue shape and texture features is a difficult task for tongue diagnosis in traditional Chinese medicine (TCM). This study aimed to explore the application of deep learning techniques in tongue image analyses. Methods: A total of 8676 tongue images were annotated by clinical experts, into seven categories, including the fissured tongue, tooth-marked tongue, stasis tongue, spotted tongue, greasy coating, peeled coating, and rotten coating. Based on the labeled tongue images, the deep learning model faster region-based convolutional neural networks (Faster R-CNN) was utilized to classify tongue images. Four performance indices, i.e., accuracy, recall, precision, and F1-score, were selected to evaluate the model. Also, we applied it to analyze tongue image features of 3601 medical checkup participants in order to explore gender and age factors and the correlations among tongue features in diseases through complex networks. Results: The average accuracy, recall, precision, and F1-score of our model achieved 90.67%, 91.25%, 99.28%, and 95.00%, respectively. Over the tongue images from the medical checkup population, the model Faster R-CNN detected 41.49% fissured tongue images, 37.16% tooth-marked tongue images, 29.66% greasy coating images, 18.66% spotted tongue images, 9.97% stasis tongue images, 3.97% peeled coating images, and 1.22% rotten coating images. There were significant differences in the incidence of the fissured tongue, tooth-marked tongue, spotted tongue, and greasy coating among age and gender. Complex networks revealed that fissured tongue and tooth-marked were closely related to hypertension, dyslipidemia, overweight and nonalcoholic fatty liver disease (NAFLD), and a greasy coating tongue was associated with hypertension and overweight. Conclusion: The model Faster R-CNN shows good performance in the tongue image classification. And we have preliminarily revealed the relationship between tongue features and gender, age, and metabolic diseases in a medical checkup population.

A multi-step approach for tongue image classification in patients with diabetes.

BACKGROUND: In China, diabetes is a common, high-incidence chronic disease. Diabetes has become a severe public health problem. However, the current diagnosis and treatment methods are difficult to control the progress of diabetes. Traditional Chinese Medicine (TCM) has become an option for the treatment of diabetes due to its low cost, good curative effect, and good accessibility. OBJECTIVE: Based on the tongue images data to realize the fine classification of the diabetic population, provide a diagnostic basis for the formulation of individualized treatment plans for diabetes, ensure the accuracy and consistency of the TCM diagnosis, and promote the objective and standardized development of TCM diagnosis. METHODS: We use the TFDA-1 tongue examination instrument to collect the tongue images of the subjects. Tongue Diagnosis Analysis System (TDAS) is used to extract the TDAS features of the tongue images. Vector Quantized Variational Autoencoder (VQ-VAE) extracts VQ-VAE features from tongue images. Based on VQ-VAE features, K-means clustering tongue images. TDAS features are used to describe the differences between clusters. Vision Transformer (ViT) combined with Grad-weighted Class Activation Mapping (Grad-CAM) is used to verify the clustering results and calculate positioning diagnostic information. RESULTS: Based on VQ-VAE features, K-means divides the diabetic population into 4 clusters with clear boundaries. The silhouette, calinski harabasz, and davies bouldin scores are 0.391, 673.256, and 0.809, respectively. Cluster 1 had the highest Tongue Body L (TB-L) and Tongue Coating L (TC-L) and the lowest Tongue Coating Angular second moment (TC-ASM), with a pale red tongue and white coating. Cluster 2 had the highest TC-b with a yellow tongue coating. Cluster 3 had the highest TB-a with a red tongue. Group 4 had the lowest TB-L, TC-L, and TB-b and the highest Per-all with a purple tongue and the largest tongue coating area. ViT verifies the clustering results of K-means, the highest Top-1 Classification Accuracy (CA) is 87.8%, and the average CA is 84.4%. CONCLUSIONS: The study organically combined unsupervised learning, self-supervised learning, and supervised learning and designed a complete diabetic tongue image classification method. This method does not rely on human intervention, makes decisions based entirely on tongue image data, and achieves state-of-the-art results. Our research will help TCM deeply participate in the individualized treatment of diabetes and provide new ideas for promoting the standardization of TCM diagnosis.

Relationships Between Diurnal Changes of Tongue Coating Microbiota and Intestinal Microbiota.

The oral cavity and the intestine are the main distribution locations of human digestive bacteria. Exploring the relationships between the tongue coating and gut microbiota, the influence of the diurnal variations of the tongue coating microbiota on the intestinal microbiota can provide a reference for the development of the disease diagnosis and monitoring, as well as the medication time. In this work, a total of 39 healthy college students were recruited. We collected their tongue coating microbiota which was collected before and after sleep and fecal microbiota. The diurnal variations of tongue coating microbiota are mainly manifested on the changes in diversity and relative abundance. There are commensal bacteria in the tongue coating and intestines, especially Prevotella which has the higher proportion in both sites. The relative abundance of Prevotella in the tongue coating before sleep has a positive correlation with intestinal Prevotella; the r is 0.322 (p < 0.05). Bacteroides in the intestine had the most bacteria associated with the tongue coating and had the highest correlation coefficient with Veillonella in the oral cavity, which was 0.468 (p < 0.01). These results suggest that the abundance of the same flora in the two sites may have a common change trend. The SourceTracker results show that the proportion of intestinal bacteria sourced from tongue coating is less than 1%. It indicates that oral flora is difficult to colonize in the intestine in healthy people. This will provide a reference for the study on the oral and intestinal microbiota in diseases.

Assessment Parameters for Arrayed Pulse Wave Analysis and Application in Hypertensive Disorders.

Study on the objectivity of pulse diagnosis is inseparable from the instruments to obtain the pulse waves. The single-pulse diagnostic instrument is relatively mature in acquiring and analysing pulse waves, but the pulse information captured by single-pulse diagnostic instrument is limited. The sensor arrays can simulate rich sense of the doctor's fingers and catch multipoint and multiparameter array signals. How to analyse the acquired array signals is still a major problem in the objective research of pulse diagnosis. The goal of this study was to establish methods for analysing arrayed pulse waves and preliminarily apply them in hypertensive disorders. While a sensor array can be used for the real-time monitoring of twelve pulse wave channels, for each subject in this study, only the pulse wave signals of the left hand at the "guan" location were obtained. We calculated the average pulse wave (APW) per channel over a thirty-second interval. The most representative pulse wave (MRPW) and the APW were matched by their correlation coefficient (CC). The features of the MRPW and the features that corresponded to the array pulse volume (APV) parameters were identified manually. Finally, a clinical trial was conducted to detect these feature performance indicators in patients with hypertensive disorders. The independent-samples t-tests and the Mann-Whitney U-tests were performed to assess the differences in these pulse parameters between the healthy and hypertensive groups. We found that the radial passage (RP) APV h1, APV h3, APV h4, APV h3/h1 (P < 0.01), and APV h4/h1 (P < 0.05) were significantly higher in the hypertensive group than in the healthy group; the intermediate passage (IP) APV h4, APV h3/h1 (P < 0.05), and APV h4/h1 (P < 0.01) and the mean APV h3, APV h3/h1 (P < 0.05), and APV h4/h1 (P < 0.01) were significantly higher in the hypertensive group than in the healthy group, and the ulnar passage (UP) APV h4/h1 (P < 0.05) was clearly elevated in the hypertensive group. These results provide a preliminary validation of this novel approach for determining the APV by arrayed pulse wave analysis. In conclusion, we identified effective indicators of hypertensive vascular function. Traditional Chinese medicine (TCM) pulses comprise multidimensional information, and a sensor array could provide a better indication of TCM pulse characteristics. In this study, the validation of the arrayed pulse wave analysis demonstrates that the APV can reliably mirror TCM pulse characteristics.

Application of computer tongue image analysis technology in the diagnosis of NAFLD.

Nonalcoholic fatty liver disease (NAFLD), a leading cause of chronic hepatic disease, can progress to liver fibrosis, cirrhosis, and hepatocellular carcinoma. Therefore, it is extremely important to explore early diagnosis and screening methods. In this study, we developed models based on computer tongue image analysis technology to observe the tongue characteristics of 1778 participants (831 cases of NAFLD and 947 cases of non-NAFLD). Combining quantitative tongue image features, basic information, and serological indexes, including the hepatic steatosis index (HSI) and fatty liver index (FLI), we utilized machine learning methods, including Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Adaptive Boosting Algorithm (AdaBoost), Naïve Bayes, and Neural Network for NAFLD diagnosis. The best fusion model for diagnosing NAFLD by Logistic Regression, which contained the tongue image parameters, waist circumference, BMI, GGT, TG, and ALT/AST, achieved an AUC of 0.897 (95% CI, 0.882-0.911), an accuracy of 81.70% with a sensitivity of 77.62% and a specificity of 85.22%; in addition, the positive likelihood ratio and negative likelihood ratio were 5.25 and 0.26, respectively. The application of computer intelligent tongue diagnosis technology can improve the accuracy of NAFLD diagnosis and may provide a convenient technical reference for the establishment of early screening methods for NAFLD, which is worth further research and verification.

Tongue image quality assessment based on a deep convolutional neural network.

BACKGROUND: Tongue diagnosis is an important research field of TCM diagnostic technology modernization. The quality of tongue images is the basis for constructing a standard dataset in the field of tongue diagnosis. To establish a standard tongue image database in the TCM industry, we need to evaluate the quality of a massive number of tongue images and add qualified images to the database. Therefore, an automatic, efficient and accurate quality control model is of significance to the development of intelligent tongue diagnosis technology for TCM. METHODS: Machine learning methods, including Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Adaptive Boosting Algorithm (Adaboost), Naïve Bayes, Decision Tree (DT), Residual Neural Network (ResNet), Convolution Neural Network developed by Visual Geometry Group at University of Oxford (VGG), and Densely Connected Convolutional Networks (DenseNet), were utilized to identify good-quality and poor-quality tongue images. Their performances were made comparisons by using metrics such as accuracy, precision, recall, and F1-Score. RESULTS: The experimental results showed that the accuracy of the three deep learning models was more than 96%, and the accuracy of ResNet-152 and DenseNet-169 was more than 98%. The model ResNet-152 obtained accuracy of 99.04%, precision of 99.05%, recall of 99.04%, and F1-score of 99.05%. The performances were better than performances of other eight models. The eight models are VGG-16, DenseNet-169, SVM, RF, GBDT, Adaboost, Naïve Bayes, and DT. ResNet-152 was selected as quality-screening model for tongue IQA. CONCLUSIONS: Our research findings demonstrate various CNN models in the decision-making process for the selection of tongue image quality assessment and indicate that applying deep learning methods, specifically deep CNNs, to evaluate poor-quality tongue images is feasible.

Establishment of noninvasive diabetes risk prediction model based on tongue features and machine learning techniques.

BACKGROUND: Diabetes is a chronic noncommunicable disease with high incidence rate. Diabetics without early diagnosis or standard treatment may contribute to serious multisystem complications, which can be life threatening. Timely detection and intervention of prediabetes is very important to prevent diabetes, because it is inevitable in the development and progress of the disease. OBJECTIVE: Our objective was to establish the predictive model that can be applied to evaluate people with blood glucose in high and critical state. METHODS: We established the diabetes risk prediction model formed by a combined TCM tongue diagnosis with machine learning techniques. 1512 subjects were recruited from the hospital. After data preprocessing, we got the dataset 1 and dataset 2. Dataset 1 was used to train classical machine learning model, while dataset 2 was used to train deep learning model. To evaluate the performance of the prediction model, we used Classification Accuracy(CA), Precision, Recall, F1-score, Precision-Recall curve(P-R curve), Area Under the Precision-Recall curve(AUPRC), Receiver Operating Characteristic curve(ROC curve), Area Under the Receiver Operating Characteristic curve(AUROC), then selected the best diabetes risk prediction model. RESULTS: On the test set of dataset 1, the CA of non-invasive Stacking model was 71 %, micro average AUROC was 0.87, macro average AUROC was 0.84, and micro average AUPRC was 0.77. In the critical blood glucose group, the AUROC was 0.84, AUPRC was 0.67. In the high blood glucose group, AUROC was 0.87, AUPRC was 0.83. On the validation set of dataset 2, the CA of ResNet50 model was 69 %, micro average AUROC was 0.84, macro average AUROC was 0.83, and micro average AUPRC was 0.73. In the critical blood glucose group, AUROC was 0.88, AUPRC was 0.71. In the high blood glucose group, AUROC was 0.80, AUPRC was 0.76. On the test set of dataset 2, the CA of ResNet50 model was 65 %, micro average AUROC was 0.83, macro average AUROC was 0.82, and micro average AUPRC was 0.71. In the critical blood glucose group, the prediction of AUROC was 0.84, AUPRC was 0.60. In the high blood glucose group, AUROC was 0.87, AUPRC was 0.71. CONCLUSIONS: Tongue features can improve the prediction accuracy of the diabetes risk prediction model formed by classical machine learning model significantly. In addition to the excellent performance, Stacking model and ResNet50 model which were recommended had non-invasive operation and were easy to use. Stacking model and ResNet50 model had high precision, low false positive rate and low misdiagnosis rate on detecting hyperglycemia. While on detecting blood glucose value in critical state, Stacking model and ResNet50 model had a high sensitivity, a low false negative rate and a low missed diagnosis rate. The study had proved that the differential changes of tongue features reflected the abnormal glucose metabolism, thus the diabetes risk prediction model formed by a combined TCM tongue diagnosis and machine learning technique was feasible.

A tongue features fusion approach to predicting prediabetes and diabetes with machine learning.

BACKGROUND: Diabetics has become a serious public health burden in China. Multiple complications appear with the progression of diabetics pose a serious threat to the quality of human life and health. We can prevent the progression of prediabetics to diabetics and delay the progression to diabetics by early identification of diabetics and prediabetics and timely intervention, which have positive significance for improving public health. OBJECTIVE: Using machine learning techniques, we establish the noninvasive diabetics risk prediction model based on tongue features fusion and predict the risk of prediabetics and diabetics. METHODS: Applying the type TFDA-1 Tongue Diagnosis Instrument, we collect tongue images, extract tongue features including color and texture features using TDAS, and extract the advanced tongue features with ResNet-50, achieve the fusion of the two features with GA_XGBT, finally establish the noninvasive diabetics risk prediction model and evaluate the performance of testing effectiveness. RESULTS: Cross-validation suggests the best performance of GA_XGBT model with fusion features, whose average CA is 0.821, the average AUROC is 0.924, the average AUPRC is 0.856, the average Precision is 0.834, the average Recall is 0.822, the average F1-score is 0.813. Test set suggests the best testing performance of GA_XGBT model, whose average CA is 0.81, the average AUROC is 0.918, the average AUPRC is 0.839, the average Precision is 0.821, the average Recall is 0.81, the average F1-score is 0.796. When we test prediabetics with GA_XGBT model, we find that the AUROC is 0.914, the Precision is 0.69, the Recall is 0.952, the F1-score is 0.8. When we test diabetics with GA_XGBT model, we find that the AUROC is 0.984, the Precision is 0.929, the Recall is 0.951, the F1-score is 0.94. CONCLUSIONS: Based on tongue features, the study uses classical machine learning algorithm and deep learning algorithm to maximum the respective advantages. We combine the prior knowledge and potential features together, establish the noninvasive diabetics risk prediction model with features fusion algorithm, and detect prediabetics and diabetics noninvasively. Our study presents a feasible method for establishing the association between diabetics and the tongue image information and prove that tongue image information is a potential marker which facilitates effective early diagnosis of prediabetics and diabetics.

Clinical data mining on network of symptom and index and correlation of tongue-pulse data in fatigue population.

BACKGROUND: Fatigue is a kind of non-specific symptom, which occurs widely in sub-health and various diseases. It is closely related to people's physical and mental health. Due to the lack of objective diagnostic criteria, it is often neglected in clinical diagnosis, especially in the early stage of disease. Many clinical practices and researches have shown that tongue and pulse conditions reflect the body's overall state. Establishing an objective evaluation method for diagnosing disease fatigue and non-disease fatigue by combining clinical symptom, index, and tongue and pulse data is of great significance for clinical treatment timely and effectively. METHODS: In this study, 2632 physical examination population were divided into healthy controls, sub-health fatigue group, and disease fatigue group. Complex network technology was used to screen out core symptoms and Western medicine indexes of sub-health fatigue and disease fatigue population. Pajek software was used to construct core symptom/index network and core symptom-index combined network. Simultaneously, canonical correlation analysis was used to analyze the objective tongue and pulse data between the two groups of fatigue population and analyze the distribution of tongue and pulse data. RESULTS: Some similarities were found in the core symptoms of sub-health fatigue and disease fatigue population, but with different node importance. The node-importance difference indicated that the diagnostic contribution rate of the same symptom to the two groups was different. The canonical correlation coefficient of tongue and pulse data in the disease fatigue group was 0.42 (P < 0.05), on the contrast, correlation analysis of tongue and pulse in the sub-health fatigue group showed no statistical significance. CONCLUSIONS: The complex network technology was suitable for correlation analysis of symptoms and indexes in fatigue population, and tongue and pulse data had a certain diagnostic contribution to the classification of fatigue population.

A New Approach of Fatigue Classification Based on Data of Tongue and Pulse With Machine Learning.

BACKGROUND: Fatigue is a common and subjective symptom, which is associated with many diseases and suboptimal health status. A reliable and evidence-based approach is lacking to distinguish disease fatigue and non-disease fatigue. This study aimed to establish a method for early differential diagnosis of fatigue, which can be used to distinguish disease fatigue from non-disease fatigue, and to investigate the feasibility of characterizing fatigue states in a view of tongue and pulse data analysis. METHODS: Tongue and Face Diagnosis Analysis-1 (TFDA-1) instrument and Pulse Diagnosis Analysis-1 (PDA-1) instrument were used to collect tongue and pulse data. Four machine learning models were used to perform classification experiments of disease fatigue vs. non-disease fatigue. RESULTS: The results showed that all the four classifiers over "Tongue & Pulse" joint data showed better performances than those only over tongue data or only over pulse data. The model accuracy rates based on logistic regression, support vector machine, random forest, and neural network were (85.51 ± 1.87)%, (83.78 ± 4.39)%, (83.27 ± 3.48)% and (85.82 ± 3.01)%, and with Area Under Curve estimates of 0.9160 ± 0.0136, 0.9106 ± 0.0365, 0.8959 ± 0.0254 and 0.9239 ± 0.0174, respectively. CONCLUSION: This study proposed and validated an innovative, non-invasive differential diagnosis approach. Results suggest that it is feasible to characterize disease fatigue and non-disease fatigue by using objective tongue data and pulse data.

Electrical coupling regulates layer 1 interneuron microcircuit formation in the neocortex.

The coexistence of electrical and chemical synapses among interneurons is essential for interneuron function in the neocortex. However, it remains largely unclear whether electrical coupling between interneurons influences chemical synapse formation and microcircuit assembly during development. Here, we show that electrical and GABAergic chemical connections robustly develop between interneurons in neocortical layer 1 over a similar time course. Electrical coupling promotes action potential generation and synchronous firing between layer 1 interneurons. Furthermore, electrically coupled interneurons exhibit strong GABA-A receptor-mediated synchronous synaptic activity. Disruption of electrical coupling leads to a loss of bidirectional, but not unidirectional, GABAergic connections. Moreover, a reduction in electrical coupling induces an increase in excitatory synaptic inputs to layer 1 interneurons. Together, these findings strongly suggest that electrical coupling between neocortical interneurons plays a critical role in regulating chemical synapse development and precise formation of circuits.

Development of layer 1 neurons in the mouse neocortex.

Layer 1 of the neocortex harbors a unique group of neurons that play crucial roles in synaptic integration and information processing. Although extensive studies have characterized the properties of layer 1 neurons in the mature neocortex, it remains unclear how these neurons progressively acquire their distinct morphological, neurochemical, and physiological traits. In this study, we systematically examined the dynamic development of Cajal-Retzius cells and γ-aminobutyric acid (GABA)-ergic interneurons in layer 1 during the first 2 postnatal weeks. Cajal-Retzius cells underwent morphological degeneration after birth and gradually disappeared from layer 1. The majority of GABAergic interneurons showed clear expression of at least 1 of the 6 distinct neurochemical markers, including Reelin, GABA-A receptor subunit delta (GABAARδ), neuropeptide Y, vasoactive intestinal peptide (VIP), calretinin, and somatostatin from postnatal day 8. Furthermore, according to firing pattern, layer 1 interneurons can be divided into 2 groups: late-spiking (LS) and burst-spiking (BS) neurons. LS neurons preferentially expressed GABAARδ, whereas BS neurons preferentially expressed VIP. Interestingly, both LS and BS neurons exhibited a rapid electrophysiological and morphological development during the first postnatal week. Our results provide new insights into the molecular, morphological, and functional developments of the neurons in layer 1 of the neocortex.

Preferential electrical coupling regulates neocortical lineage-dependent microcircuit assembly.

Radial glial cells are the primary neural progenitor cells in the developing neocortex. Consecutive asymmetric divisions of individual radial glial progenitor cells produce a number of sister excitatory neurons that migrate along the elongated radial glial fibre, resulting in the formation of ontogenetic columns. Moreover, sister excitatory neurons in ontogenetic columns preferentially develop specific chemical synapses with each other rather than with nearby non-siblings. Although these findings provide crucial insight into the emergence of functional columns in the neocortex, little is known about the basis of this lineage-dependent assembly of excitatory neuron microcircuits at single-cell resolution. Here we show that transient electrical coupling between radially aligned sister excitatory neurons regulates the subsequent formation of specific chemical synapses in the neocortex. Multiple-electrode whole-cell recordings showed that sister excitatory neurons preferentially form strong electrical coupling with each other rather than with adjacent non-sister excitatory neurons during early postnatal stages. This preferential coupling allows selective electrical communication between sister excitatory neurons, promoting their action potential generation and synchronous firing. Interestingly, although this electrical communication largely disappears before the appearance of chemical synapses, blockade of the electrical communication impairs the subsequent formation of specific chemical synapses between sister excitatory neurons in ontogenetic columns. These results suggest a strong link between lineage-dependent transient electrical coupling and the assembly of precise excitatory neuron microcircuits in the neocortex.