Comparative proteomic analysis of donor human milk treated by high-pressure processing or Holder pasteurization on undigested proteins across dynamic simulated preterm infant digestion.

High-pressure processing (HPP) of donor human milk (DM) minimally impacts the concentration and bioactivity of some important bioactive proteins including lactoferrin, and bile salt-stimulated lipase (BSSL) compared to Holder pasteurization (HoP), yet the impact of HPP and subsequent digestion on the full array of proteins detectable by proteomics remains unclear. We investigated how HPP impacts undigested proteins in DM post-processing and across digestion by proteomic analysis. Each pool of milk (n = 3) remained raw, or was treated by HPP (500 MPa, 10 min) or HoP (62.5 °C, 30 min), and underwent dynamic in vitro digestion simulating the preterm infant. In the meal, major proteins were minimally changed post-processing. HPP-treated milk proteins better resisted proximal digestion (except for immunoglobulins, jejunum 180 min) and the extent of undigested proteins after gastric digestion of major proteins in HPP-treated milk was more similar to raw (e.g., BSSL, lactoferrin, macrophage-receptor-1, CD14, complement-c3/c4, xanthine dehydrogenase) than HoP.

Extracting Knowledge from MS Clinical Metabolomic Data: Processing and Analysis Strategies.

Assessing potential alterations of metabolic pathways using large-scale approaches plays today a central role in clinical research. Because several thousands of mass features can be measured for each sample with separation techniques hyphenated to mass spectrometry (MS) detection, adapted strategies have to be implemented to detect altered pathways and help to elucidate the mechanisms of pathologies. These procedures include peak detection, sample alignment, normalization, statistical analysis, and metabolite annotation. Interestingly, considerable advances have been made over the last years in terms of analytics, bioinformatics, and chemometrics to help massive and complex metabolomic data to be more adequately handled with automated processing and data analysis workflows. Recent developments and remaining challenges related to MS signal processing, metabolite annotation, and biomarker discovery based on statistical models are illustrated in this chapter in light of their application to clinical research.

Method to Study the Survival Abilities of Foodborne Bacterial Pathogens Under Food Processing Conditions.

Properly using controllable atmospheric containers can facilitate investigations of the survival abilities and physiological states of key and emerging-foodborne pathogens under recreated applicable food processing environmental conditions. Notably, saturated salt solutions can efficiently control relative humidity in airtight containers. This chapter describes a practical experimental setup, with necessary prerequisites for exposing foodborne pathogens to simulated and relevant food processing environmental conditions. Subsequent analyses for studying cell physiology will also be suggested.

Illumina MiSeq 16S rRNA Gene Library Preparation for Poultry Processing Microbiome Analyses.

The standardization of the microbiome sequencing of poultry rinsates is essential for generating comparable microbial composition data among poultry processing facilities if this technology is to be adopted by the industry. Samples must first be acquired, DNA must be extracted, and libraries must be constructed. In order to proceed to library sequencing, the samples should meet quality control standards. Finally, data must be analyzed using computer bioinformatics pipelines. This data can subsequently be incorporated into more advanced computer algorithms for risk assessment. Ultimately, *a uniform sequencing pipeline will enable both the government regulatory agencies and the poultry industry to identify potential weaknesses in food safety.This chapter presents the different steps for monitoring the population dynamics of the microbiome in poultry processing using 16S rDNA sequencing.

Preliminary risk assessment of exposure to 3-monochloropropanediol and glycidyl fatty acid esters from infant formula and baby food products on the Saudi market.

3-Monochloropropanediol fatty acid esters (3-MCPDE) and glycidyl esters (GE) are well-identified processing-induced chemical toxicants detected in infant formula and baby foods worldwide. We analysed the levels of 3-MCPDE and GE in infant formula and baby food products available in Saudi Arabia, followed by a dietary risk assessment for exposure to these contaminants in infants and young children from birth to 3 years. Eighty-five commercial infant formulas (n = 35) and baby foods (n = 50) available for consumption by infants and babies purchased from the Saudi market during 2022 were analysed for these contaminants using gas chromatography-tandem mass spectrometry. 3-MCPDE and GE were detected in 100 and 80% of the samples, with a mean concentration of 57 µg/kg (range: 2-285 µg/kg) and 30 µg/kg (range: not detected-217 µg/kg), respectively. The highest concentration was found in milk-based formula for infants 0-6 months (285 µg/kg) and the lowest was found in fruit purees (2 µg/kg). Preliminary exposure and risk assessment showed increased exposure to 3-MCPDE for infants exclusively fed infant formula with exposure declining with age due to the introduction of solid foods. GE exposure levels reached 0.8 µg/kg body weight per day, which declined over time with margin of exposure values below 25,000. These results indicate that the levels of 3-MCPDE and GE in infant formula may pose potential risks to infants exclusively fed formula; therefore, adopting EU regulations should reduce the presence of these processing contaminants in essential infant foods.

Ascle-A Python Natural Language Processing Toolkit for Medical Text Generation: Development and Evaluation Study.

BACKGROUND: Medical texts present significant domain-specific challenges, and manually curating these texts is a time-consuming and labor-intensive process. To address this, natural language processing (NLP) algorithms have been developed to automate text processing. In the biomedical field, various toolkits for text processing exist, which have greatly improved the efficiency of handling unstructured text. However, these existing toolkits tend to emphasize different perspectives, and none of them offer generation capabilities, leaving a significant gap in the current offerings. OBJECTIVE: This study aims to describe the development and preliminary evaluation of Ascle. Ascle is tailored for biomedical researchers and clinical staff with an easy-to-use, all-in-one solution that requires minimal programming expertise. For the first time, Ascle provides 4 advanced and challenging generative functions: question-answering, text summarization, text simplification, and machine translation. In addition, Ascle integrates 12 essential NLP functions, along with query and search capabilities for clinical databases. METHODS: We fine-tuned 32 domain-specific language models and evaluated them thoroughly on 27 established benchmarks. In addition, for the question-answering task, we developed a retrieval-augmented generation (RAG) framework for large language models that incorporated a medical knowledge graph with ranking techniques to enhance the reliability of generated answers. Additionally, we conducted a physician validation to assess the quality of generated content beyond automated metrics. RESULTS: The fine-tuned models and RAG framework consistently enhanced text generation tasks. For example, the fine-tuned models improved the machine translation task by 20.27 in terms of BLEU score. In the question-answering task, the RAG framework raised the ROUGE-L score by 18% over the vanilla models. Physician validation of generated answers showed high scores for readability (4.95/5) and relevancy (4.43/5), with a lower score for accuracy (3.90/5) and completeness (3.31/5). CONCLUSIONS: This study introduces the development and evaluation of Ascle, a user-friendly NLP toolkit designed for medical text generation. All code is publicly available through the Ascle GitHub repository. All fine-tuned language models can be accessed through Hugging Face.

MediAlbertina: An European Portuguese medical language model.

BACKGROUND: Patient medical information often exists in unstructured text containing abbreviations and acronyms deemed essential to conserve time and space but posing challenges for automated interpretation. Leveraging the efficacy of Transformers in natural language processing, our objective was to use the knowledge acquired by a language model and continue its pre-training to develop an European Portuguese (PT-PT) healthcare-domain language model. METHODS: After carrying out a filtering process, Albertina PT-PT 900M was selected as our base language model, and we continued its pre-training using more than 2.6 million electronic medical records from Portugal's largest public hospital. MediAlbertina 900M has been created through domain adaptation on this data using masked language modelling. RESULTS: The comparison with our baseline was made through the usage of both perplexity, which decreased from about 20 to 1.6 values, and the fine-tuning and evaluation of information extraction models such as Named Entity Recognition and Assertion Status. MediAlbertina PT-PT outperformed Albertina PT-PT in both tasks by 4-6% on recall and f1-score. CONCLUSIONS: This study contributes with the first publicly available medical language model trained with PT-PT data. It underscores the efficacy of domain adaptation and offers a contribution to the scientific community in overcoming obstacles of non-English languages. With MediAlbertina, further steps can be taken to assist physicians, in creating decision support systems or building medical timelines in order to perform profiling, by fine-tuning MediAlbertina for PT- PT medical tasks.

Towards dental diagnostic systems: Synergizing wavelet transform with generative adversarial networks for enhanced image data fusion.

The advent of precision diagnostics in pediatric dentistry is shifting towards ensuring early detection of dental diseases, a critical factor in safeguarding the oral health of the younger population. In this study, an innovative approach is introduced, wherein Discrete Wavelet Transform (DWT) and Generative Adversarial Networks (GANs) are synergized within an Image Data Fusion (IDF) framework to enhance the accuracy of dental disease diagnosis through dental diagnostic systems. Dental panoramic radiographs from pediatric patients were utilized to demonstrate how the integration of DWT and GANs can significantly improve the informativeness of dental images. In the IDF process, the original images, GAN-augmented images, and wavelet-transformed images are combined to create a comprehensive dataset. DWT was employed for the decomposition of images into frequency components to enhance the visibility of subtle pathological features. Simultaneously, GANs were used to augment the dataset with high-quality, synthetic radiographic images indistinguishable from real ones, to provide robust data training. These integrated images are then fed into an Artificial Neural Network (ANN) for the classification of dental diseases. The utilization of the ANN in this context demonstrates the system's robustness and culminates in achieving an unprecedented accuracy rate of 0.897, 0.905 precision, recall of 0.897, and specificity of 0.968. Additionally, this study explores the feasibility of embedding the diagnostic system into dental X-ray scanners by leveraging lightweight models and cloud-based solutions to minimize resource constraints. Such integration is posited to revolutionize dental care by providing real-time, accurate disease detection capabilities, which significantly reduces diagnostical delays and enhances treatment outcomes.

Sour taste perception in fluids: The impact of sweet tastant, fluid viscosity, and individual salivary properties.

Binary taste perception is widely studied in aqueous solutions but less investigated in non-Newtonian fluid systems. In this study, the effect of sweet tastants on the dynamic sour taste perception in thickened fluids and its underpinning oral processing factors were investigated. Subjects were tested for taste thresholds and salivary biochemical properties. By using hydroxypropyl methylcellulose (HPMC) as a thickening agent, subjects conducted sour taste evaluation, with and without maltose and/or HPMC, using descriptive sensory analyses. A simulated fluid shear elicited by fixed-frequency mastication was applied on thickened fluid sample oral processing during time-intensity sour taste evaluation. Results showed that adding maltose to fluid samples enhanced sour taste perception, and increasing fluid viscosity generally suppressed perceived maximum sour taste. Moreover, subjects with lower sour taste sensitivity and higher salivary buffering capacity reported overall lower sour taste intensity in most samples, validating the hypothesis that salivary properties importantly affect sour taste perception.

TRU-IMP: techniques for reliable use of images in medical physics; a graphical user interface to analyze and compare segmentations in nuclear medicine.

BACKGROUND: In the context of pharmacokinetic analyses, the segmentation method one uses has a large impact on the results obtained, thus the importance of transparency. Innovation: This paper introduces a graphical user interface (GUI), TRU-IMP, that analyzes time-activity curves and segmentations in dynamic nuclear medicine. This GUI fills a gap in the current technological tools available for the analysis of quantitative dynamic nuclear medicine image acquisitions. The GUI includes various techniques of segmentations, with possibilities to compute related uncertainties. Results: The GUI was tested on image acquisitions made on a dynamic nuclear medicine phantom. This allows the comparison of segmentations via their time-activity curves and the extracted pharmacokinetic parameters. Implications: The flexibility and user-friendliness allowed by the proposed interface make the analyses both easy to perform and adjustable to any specific case. This GUI permits researchers to better show and understand the reproducibility, precision, and accuracy of their work in quantitative dynamic nuclear medicine. Availability and Implementation: Source code freely available on GitHub: https://github.com/ArGilfea/TRU-IMP and location of the interface available from there. The GUI is fully compatible with iOS and Windows operating systems (not tested on Linux). A phantom acquisition is also available to test the GUI easily. .

Zirconia crowns manufactured using digital light processing: Effects of printing angle and layer thickness on the accuracy.

OBJECTIVES: This study investigated the effects of build angle and layer thickness on the trueness and precision of zirconia crowns manufactured using digital light processing (DLP) technology. MATERIALS AND METHODS: Single crowns were fabricated from zirconia using DLP technology. The crowns were manufactured with three different representative build angles (0°, 45°, and 90°) and two different layer thicknesses (30 µm and 50 µm). After printing, the specimens were non-contact-scanned, and their accuracy was assessed using a 3D analysis software. Root mean square (RMS) values were used to determine trueness and precision. Color maps were generated to detect deviations within the specimens. Statistical analyses were conducted using two-way ANOVA. RESULTS: Build angle and layer thickness significantly affected trueness and precision (p < 0.05). At a 30-µm layer thickness, the crowns printed at angles of 0° (32.2 ± 3.2 µm) and 45° (33.9 ± 2.4 µm) demonstrated the best marginal trueness compared to those in other groups (p < 0.05). Notably, those printed at an angle of 90° exhibited the best intaglio surface trueness (37.4 ± 4.0 µm). At a 50-µm layer thickness, the crowns printed at an angle of 90° exhibited the lowest accuracy concerning marginal and intaglio surface aspects (27.7 ± 8.2 µm). CONCLUSIONS: Both the build angle and layer thickness significantly affected the dimensional accuracy of DLP-printed zirconia crowns, with the 30-µm layer thickness offering superior trueness. Optimal results were achieved using build angles of 0° and 45° in conjunction with thinner layers, minimizing marginal defects. CLINICAL SIGNIFICANCE: All zirconia crowns produced at different build angles and layer thicknesses satisfied clinical requirements. Specific combinations of these factors realized the fabrication of single crowns that possessed the highest accuracy.

Computer-assisted evaluation of retinal vessel tortuosity in children with sickle cell disease without retinopathy.

OBJECTIVE: We assessed the predictive efficacy of automatically quantified retinal vascular tortuosity from the fundus pictures of patients with sickle cell disease (SCD) without evident retinopathy. METHODS: Retinal images were obtained from 31 healthy and 31 SCD participants using fundus imaging and analyzed using a novel computational automated metric assessment. The local and global vessel tortuosity and their relationship with systemic disease parameters were analyzed based on the images. RESULTS: SCD arteries had an increased local tortuosity index compared to the controls (0.0007 ±â€¯0.0019 vs. 0.0006 ±â€¯0.0014, p = 0.019). Furthermore, the SCD patients had wider vessel caliber mainly in the arteries (14.68 ±â€¯5.3 vs. 14.06 ±â€¯5.3, p < 0.001). The SCD global tortuosity did not differ significantly from that of the controls (p = 0.598). The female participants had significantly reduced retinal vessel tortuosity indices compared to the male participants (p = 0.018). CONCLUSION: Retinal arterial tortuosity and caliber were reliable and objective measures that could be used as a non-invasive prognostic and diagnostic indicator in sickle cell retinopathy. Further studies are required to correlate these local vascular parameters to systemic risk factors and monitor their progression and change over time.

Validation of large language models for detecting pathologic complete response in breast cancer using population-based pathology reports.

AIMS: The primary goal of this study is to evaluate the capabilities of Large Language Models (LLMs) in understanding and processing complex medical documentation. We chose to focus on the identification of pathologic complete response (pCR) in narrative pathology reports. This approach aims to contribute to the advancement of comprehensive reporting, health research, and public health surveillance, thereby enhancing patient care and breast cancer management strategies. METHODS: The study utilized two analytical pipelines, developed with open-source LLMs within the healthcare system's computing environment. First, we extracted embeddings from pathology reports using 15 different transformer-based models and then employed logistic regression on these embeddings to classify the presence or absence of pCR. Secondly, we fine-tuned the Generative Pre-trained Transformer-2 (GPT-2) model by attaching a simple feed-forward neural network (FFNN) layer to improve the detection performance of pCR from pathology reports. RESULTS: In a cohort of 351 female breast cancer patients who underwent neoadjuvant chemotherapy (NAC) and subsequent surgery between 2010 and 2017 in Calgary, the optimized method displayed a sensitivity of 95.3% (95%CI: 84.0-100.0%), a positive predictive value of 90.9% (95%CI: 76.5-100.0%), and an F1 score of 93.0% (95%CI: 83.7-100.0%). The results, achieved through diverse LLM integration, surpassed traditional machine learning models, underscoring the potential of LLMs in clinical pathology information extraction. CONCLUSIONS: The study successfully demonstrates the efficacy of LLMs in interpreting and processing digital pathology data, particularly for determining pCR in breast cancer patients post-NAC. The superior performance of LLM-based pipelines over traditional models highlights their significant potential in extracting and analyzing key clinical data from narrative reports. While promising, these findings highlight the need for future external validation to confirm the reliability and broader applicability of these methods.

Brain tumor detection and classification in MRI using hybrid ViT and GRU model with explainable AI in Southern Bangladesh.

Brain tumor, a leading cause of uncontrolled cell growth in the central nervous system, presents substantial challenges in medical diagnosis and treatment. Early and accurate detection is essential for effective intervention. This study aims to enhance the detection and classification of brain tumors in Magnetic Resonance Imaging (MRI) scans using an innovative framework combining Vision Transformer (ViT) and Gated Recurrent Unit (GRU) models. We utilized primary MRI data from Bangabandhu Sheikh Mujib Medical College Hospital (BSMMCH) in Faridpur, Bangladesh. Our hybrid ViT-GRU model extracts essential features via ViT and identifies relationships between these features using GRU, addressing class imbalance and outperforming existing diagnostic methods. We extensively processed the dataset, and then trained the model using various optimizers (SGD, Adam, AdamW) and evaluated through rigorous 10-fold cross-validation. Additionally, we incorporated Explainable Artificial Intelligence (XAI) techniques-Attention Map, SHAP, and LIME-to enhance the interpretability of the model's predictions. For the primary dataset BrTMHD-2023, the ViT-GRU model achieved precision, recall, and F1-score metrics of 97%. The highest accuracies obtained with SGD, Adam, and AdamW optimizers were 81.66%, 96.56%, and 98.97%, respectively. Our model outperformed existing Transfer Learning models by 1.26%, as validated through comparative analysis and cross-validation. The proposed model also shows excellent performances with another Brain Tumor Kaggle Dataset outperforming the existing research done on the same dataset with 96.08% accuracy. The proposed ViT-GRU framework significantly improves the detection and classification of brain tumors in MRI scans. The integration of XAI techniques enhances the model's transparency and reliability, fostering trust among clinicians and facilitating clinical application. Future work will expand the dataset and apply findings to real-time diagnostic devices, advancing the field.

A review of the botany, metabolites, pharmacology, toxicity, industrial applications, and processing of Polygalae Radix: the "key medicine for nourishing life".

Polygalae radix (PR) is the dried root of Polygala tenuifolia Willd. and Polygala sibirica L. and enjoys the reputation as the "key medicine for nourishing life." In this study, information about "Polygala tenuifolia Willd.," "Polygala sibirica L.," and "Yuanzhi" was retrieved from scientific databases, including Google Scholar, Baidu Scholar, Web of Science, PubMed, CNKI, and Wan Fang Data. Information from Chinese herbal medicine classics, Yaozhi Data, and the Gaide Chemical Network was also collected. Information related to botany, phytochemistry, pharmacology, toxicity, industrial applications, and processing is summarized in this paper to tap its potentialities and promote its further development and clinical application. More than 320 metabolites have been isolated from PR; saponins, xanthones, and oligosaccharide esters are the main functional metabolites. Pharmacological research shows that its pharmacological action mainly focuses on resisting nervous system diseases, and it also has the functions of anti-oxidation, anti-inflammation, anti-tumor, anti-pathogenic microorganisms and others. The gastrointestinal irritation of its saponins impeded its application, but this irritation can be reduced by controlling the dosage, compatibility with other herbs, or processing. The future progress of PR faces opportunities and challenges. More attention should be paid to the traditional application and processing methods of PR recorded in ancient books. The lack of safety and clinical studies has limited its application and transformation of achievements. Moreover, it is one-sided to take the content of only a few metabolites as the index of processing optimization and quality control, which cannot reflect the full pharmacological and toxicological activities of PR.

Pupillary responses to directional uncertainty while intercepting a moving target.

Pupillary responses serve as sensitive indicators of cognitive processes, attentional shifts and decision-making dynamics. Our study investigates how directional uncertainty and target speed (V T) influence pupillary responses in a foveal tracking task involving the interception of a moving dot. Directional uncertainty, reflecting the unpredictability of the target's direction changes, was manipulated by altering the angular range (AR) from which random directions for the moving dot were extracted. Higher AR values were associated with reduced pupillary diameters, indicating that heightened uncertainty led to smaller pupil sizes. Additionally, an inverse U-shaped relationship between V T and pupillary responses suggested maximal diameters at intermediate speeds. Analysis of saccade-triggered responses showed a negative correlation between pupil diameter and directional uncertainty. Dynamic linear modelling revealed the influence of past successful collisions and other behavioural parameters on pupillary responses, emphasizing the intricate interaction between task variables and cognitive processing. Our results highlight the dynamic interplay between the directional uncertainty of a single moving target, V T and pupillary responses, with implications for understanding attentional mechanisms, decision-making processes and potential applications in emerging technologies.

Cellular psychology: relating cognition to context-sensitive pyramidal cells.

'Cellular psychology' is a new field of inquiry that studies dendritic mechanisms for adapting mental events to the current context, thus increasing their coherence, flexibility, effectiveness, and comprehensibility. Apical dendrites of neocortical pyramidal cells have a crucial role in cognition - those dendrites receive input from diverse sources, including feedback, and can amplify the cell's feedforward transmission if relevant in that context. Specialized subsets of inhibitory interneurons regulate this cooperative context-sensitive processing by increasing or decreasing amplification. Apical input has different effects on cellular output depending on whether we are awake, deeply asleep, or dreaming. Furthermore, wakeful thought and imagery may depend on apical input. High-resolution neuroimaging in humans supports and complements evidence on these cellular mechanisms from other mammals.

Fine-Tuning Large Language Models to Enhance Programmatic Assessment in Graduate Medical Education.

Background: Natural language processing is a collection of techniques designed to empower computer systems to comprehend and/or produce human language. The purpose of this investigation was to train several large language models (LLMs) to explore the tradeoff between model complexity and performance while classifying narrative feedback on trainees into the Accreditation Council for Graduate Medical Education subcompetencies. We hypothesized that classification accuracy would increase with model complexity. Methods: The authors fine-tuned several transformer-based LLMs (Bidirectional Encoder Representations from Transformers [BERT]-base, BERT-medium, BERT-small, BERT-mini, BERT-tiny, and SciBERT) to predict Accreditation Council for Graduate Medical Education subcompetencies on a curated dataset of 10 218 feedback comments. Performance was compared with the authors' previous work, which trained a FastText model on the same dataset. Performance metrics included F1 score for global model performance and area under the receiver operating characteristic curve for each competency. Results: No models were superior to FastText. Only BERT-tiny performed worse than FastText. The smallest model with comparable performance to FastText, BERT-mini, was 94% smaller. Area under the receiver operating characteristic curve for each competency was similar on BERT-mini and FastText with the exceptions of Patient Care 7 (Situational Awareness and Crisis Management) and Systems-Based Practice. Discussion: Transformer-based LLMs were fine-tuned to understand anesthesiology graduate medical education language. Complex LLMs did not outperform FastText. However, equivalent performance was achieved with a model that was 94% smaller, which may allow model deployment on personal devices to enhance speed and data privacy. This work advances our understanding of best practices when integrating LLMs into graduate medical education.

Using a signal detection approach to understand the impacts of processing fluency and efficacy on accuracy in misinformation detection.

This experiment (N = 1,019) examined how a state of processing fluency, induced through either an easy or difficult task (reading a simple vs. complex message or recalling few vs. many examples) impacted participants' ability to subsequently detect misinformation. The results revealed that, as intended, easier tasks led to higher reports of processing fluency. In turn, increased processing fluency was positively associated with internal efficacy. Finally, internal efficacy was positively related to misinformation detection using a signal detection task. This work suggests that feelings of ease while processing information can promote confidence and a more discerning style of information processing. Given the proliferation of misinformation online, an understanding of how metacognitions - like processing fluency - can disrupt the tacit acceptance of information carries important democratic and normative implications.

A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence.

Emotion perception is essential to affective and cognitive development which involves distributed brain circuits. Emotion identification skills emerge in infancy and continue to develop throughout childhood and adolescence. Understanding the development of the brain's emotion circuitry may help us explain the emotional changes during adolescence. In this work, we aim to deepen our understanding of emotion-related functional connectivity (FC) from association to causation. We proposed a Bayesian incorporated linear non-Gaussian acyclic model (BiLiNGAM), which incorporated association model into the estimation pipeline. Simulation results indicated stable and accurate performance over various settings, especially when the sample size was small. We used fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) to validate the approach. It included 855 individuals aged 8-22 years who were divided into five different adolescent stages. Our network analysis revealed the development of emotion-related intra- and intermodular connectivity and pinpointed several emotion-related hubs. We further categorized the hubs into two types: in-hubs and out-hubs, as the center of receiving and distributing information, respectively. In addition, several unique developmental hub structures and group-specific patterns were discovered. Our findings help provide a directed FC template of brain network organization underlying emotion processing during adolescence.

Our study introduces a novel method for analyzing directed graphs across multiple groups and demonstrates its effectiveness through a series of simulation studies. This method is applied to investigate the development of directed functional connectivity for emotion processing across diverse adolescent periods. Our findings unveil a notable increase in interfunctional connectivity with age, specifically involved with the executive control and memory retrieval, indicating the maturation of emotion processing function. Additionally, significant development of intraconnectivity in the subcortical areas emerges in early adolescence, whereas development of cerebellum emerges in the very end of adolescence. These insights offer valuable contributions to our understanding of the dynamic neural processes underlying emotion regulation during adolescence.