时间：6月4日 (周四)  18:00-20:00

地点：恕园28号楼205室

主题：文献报告

1、文献汇报：额下回在两位数数字加工处理中的认知控制作用：来自数量比较任务的fNIRS证据

主讲：李蓝多 同学

2、文献汇报：应用基于规则的方法、机器学习以及预训练的语言模型从精神科入院记录中识别精神症状发作

主讲：柳可 同学

3、文献汇报：进化过程中外周神经系统小胶质样细胞参与调控神经元胞体大小

主讲：陆洁莹 同学

欢迎老师与同学们踊跃出席！

脑科学研究所

文献详细信息

1、题目：Inferior frontal gyrus is responsible for cognitive control during two-digit number processing: fNIRS evidence from magnitude comparison task

期刊名称：NeuroImage   IF:4.5   SCI: Q1   中科院：2区

摘要：Cognitive control plays an indispensable role in multi-digit number processing. The place-value structure of multi-digit numbers is apparent when comparing numbers. If the comparison of units yields a different result from the comparison of decades, the process takes longer than comparing compatible number pairs. Behavioral and eye-tracking studies have shown that this unit-decade compatibility effect was larger under high as compared to low cognitive control demands. However, the question arises whether cognitive control operates mainly as a domain-general effect in the frontal cortex, or if it directly affects domain-specific number magnitude processes in the parietal cortex. In the current fNIRS study, cognitive control demands were manipulated by adjusting the proportion of within-decade fillers in two-digit number comparison tasks (N = 80). The compatibility effect was replicated, and the fNIRS results showed that cognitive control is associated with the inferior frontal gyrus in two-digit number comparison. Thus, cognitive control operates mainly in the frontal cortex and does not directly affect domain-specific number magnitude processes in the parietal cortex. Methodological and linguistic limitations are discussed. Overall, this neurocognitive evidence supports that domain-general cognitive processes are relevant for multi-digit number processing.

2、题目：Identifying psychosis episodes in psychiatric admission notes via rule-based methods, machine learning, and pre-trained language models

期刊名称：Translational Psychiatry   IF：6.2   SCI：Q1   中科院：二区

摘要：Early and accurate diagnosis is crucial for effective treatment and improved outcomes, yet identifying psychotic episodes presents significant challenges due to its complex nature and the varied presentation of symptoms among individuals. One of the primary difficulties lies in the underreporting and underdiagnosis of psychosis, compounded by the stigma surrounding mental health and the individuals' often diminished insight into their condition. Existing efforts leveraging Electronic Health Records (EHRs) to retrospectively identify psychosis typically rely on structured data, such as medical codes and patient demographics, which frequently lack essential information. Addressing these challenges, our study leverages Natural Language Processing (NLP) algorithms to analyze psychiatric admission notes for the diagnosis of psychosis, providing a detailed evaluation of rule-based algorithms, machine learning models, and pre-trained language models. Additionally, the study investigates the effectiveness of employing keywords to streamline extensive note data before training and evaluating the models. Analyzing 4629 initial psychiatric admission notes (1196 cases of psychosis versus 3433 controls ) from 2005 to 2019, including patients aged 16-35 years, selected based on the 75th percentile for age at onset of schizophrenia, we discovered that the XGBoost classifier employing Term Frequency-Inverse Document Frequency (TF-IDF) features derived from notes pre-selected by expert-curated keywords, attained the highest performance with an F1 score of 0.8881 (AUROC [95% CI]: 0.9725 [0.9717, 0.9733]). BlueBERT demonstrated comparable efficacy with an F1 score of 0.8841 (AUROC [95% CI]: 0.97 [0.9580, 0.9820]) on the same set of notes. Both models markedly outperformed traditional International Classification of Diseases (ICD) code-based detection methods from discharge summaries, which had an F1 score of 0.7608, thus improving the margin by 0.12. Furthermore, our findings indicate that keyword pre-selection markedly enhances the performance of both machine learning and pre-trained language models. This study illustrates the potential of NLP techniques to improve psychosis detection within admission notes and aims to serve as a foundational reference for future research on applying NLP for psychosis identification in EHR notes.

3、题目：Peripheral nervous system microglia-like cells regulate neuronal soma size throughout evolution

期刊名称：Cell   IF: 64.5   SCI: Q1   中科院：一区

摘要：Microglia, essential in the central nervous system (CNS), were historically considered absent from the peripheral nervous system (PNS). Here, we show a PNS-resident macrophage population that shares transcriptomic and epigenetic profiles as well as an ontogenetic trajectory with CNS microglia. This population (termed PNS microglia-like cells) enwraps the neuronal soma inside the satellite glial cell envelope, preferentially associates with larger neurons during PNS development, and is required for neuronal functions by regulating soma enlargement and axon growth. A phylogenetic survey of 24 vertebrates revealed an early origin of PNS microglia-like cells, whose presence is correlated with neuronal soma size (and body size) rather than evolutionary distance. Consistent with their requirement for soma enlargement, PNS microglia-like cells are maintained in vertebrates with large peripheral neuronal soma but absent when neurons evolve to have smaller soma. Our study thus reveals a PNS counterpart of CNS microglia that regulates neuronal soma size during both evolution and ontogeny.