Neural responses to syllable-induced P1m and social impairment in children with autism spectrum disorder and typically developing Peers

In previous magnetoencephalography (MEG) studies, children with autism spectrum disorder (ASD) have been shown to respond differently to speech stimuli than typically developing (TD) children. Quantitative evaluation of this difference in responsiveness may support early diagnosis and intervention for ASD. The objective of this research is to investigate the relationship between syllable-induced P1m and social impairment in children with ASD and TD children. We analyzed 49 children with ASD aged 40–92 months and age-matched 26 TD children. We evaluated their social impairment by means of the Social Responsiveness Scale (SRS) and their intelligence ability using the Kaufman Assessment Battery for Children (K-ABC). Multiple regression analysis with SRS score as the dependent variable and syllable-induced P1m latency or intensity and intelligence ability as explanatory variables revealed that SRS score was associated with syllable-induced P1m latency in the left hemisphere only in the TD group and not in the ASD group. A second finding was that increased leftward-lateralization of intensity was correlated with higher SRS scores only in the ASD group. These results provide valuable insights but also highlight the intricate nature of neural mechanisms and their relationship with autistic traits

Association Between Magnetoencephalographic Interictal Epileptiform Discharge and Cognitive Function in Young Children With Typical Development and With Autism Spectrum Disorders

Electroencephalograms of individuals with autism spectrum disorders (ASD) show higher rates of interictal epileptiform discharges (IEDs), which are known to have an inverse association with cognitive function in typically developed (TD) children. Nevertheless, that phenomenon has not been investigated adequately in children with ASD. From university and affiliated hospitals, 163 TD children (84 male, 79 female, aged 32–89 months) and 107 children (85 male, 22 female, aged 36–98 months) with ASD without clinical seizure were recruited. We assessed their cognitive function using the Kaufman Assessment Battery for Children (K-ABC) and recorded 10 min of MEG. Original waveforms were visually inspected. Then a linear regression model was applied to evaluate the association between the IED frequency and level of their cognitive function. Significantly higher rates of IEDs were found in the ASD group than in the TD group. In the TD group, we found significant negative correlation between mental processing scale scores (MPS) and the IED frequency. However, for the ASD group, we found significant positive correlation between MPS scores and the IED frequency. In terms of the achievement scale, correlation was not significant in either group. Although we found a correlative rather than a causal effect, typically developed children with higher IED frequency might better be followed up carefully. Furthermore, for children with ASD without clinical seizure, clinicians might consider IEDs as less harmful than those observed in TD children

A custom magnetoencephalography device reveals brain connectivity and high reading/decoding ability in children with autism

A subset of individuals with autism spectrum disorder (ASD) performs more proficiently on certain visual tasks than may be predicted by their general cognitive performances. However, in younger children with ASD (aged 5 to 7), preserved ability in these tasks and the neurophysiological correlates of their ability are not well documented. In the present study, we used a custom child-sized magnetoencephalography system and demonstrated that preserved ability in the visual reasoning task was associated with rightward lateralisation of the neurophysiological connectivity between the parietal and temporal regions in children with ASD. In addition, we demonstrated that higher reading/decoding ability was also associated with the same lateralisation in children with ASD. These neurophysiological correlates of visual tasks are considerably different from those that are observed in typically developing children. These findings indicate that children with ASD have inherently different neural pathways that contribute to their relatively preserved ability in visual tasks

オキシトシン長期投与による社会性の変化: 脳磁図を用いた検討

自閉症スペクトラム障害(ASD)は社会性の障害を主とする発達障害である。共感とは他者と内面を分かち合う事であるが、ASDでは共感性に欠ける。Mismatch field(MMF)は話者が聞き手の注意を促すために発する声に対する脳の反応を脳磁図で計測したものであり社会性の指標となる。我々は今回10名のASD者にオキシトシン(OT)を8-10週間にわたり、MMFの変化を測定するとともに質問紙法(EQ)により行動面から共感性の変化を評価した。結果、OT長期投与中にはMMFの振幅の変化と、EQのスコアの変化に有意な正の相関が認められた。一方、長期投与終了後の評価にはこのような関係は認められなかった。Autism spectrum disorders (ASDs) are neurodevelopmental conditions with impairments in social communica- tion and interaction. Empathy is the ability to understand and share another person\u27s inner life, and it is an essential process in social cognition, which is deficient in ASD. The mismatch field (MMF) has been used as a neurophysiological marker for the automatic detection of changes in auditory stimuli. In the present study, we focused on long-term changes in MMF evoked by an empathic voice and changes in the empathy quotient (EQ) in ASD during an 8-week clinical trial using oxytocin (OT). Ten males with ASD without intellectual disability participated in this pilot study. The results demonstrated a significant positive correlation between the change in the MMF amplitude in the auditory cortex and the change in the EQ score during the 8-week clinical trial, whereas no significant change was observed in the MMF amplitude or EQ score after the administration period of OT.研究課題/領域番号:16K19756, 研究期間(年度):2016-04-01 - 2019-03-3

Diagnostic Accuracy of Differential-Diagnosis Lists Generated by Generative Pretrained Transformer 3 Chatbot for Clinical Vignettes with Common Chief Complaints: A Pilot Study

The diagnostic accuracy of differential diagnoses generated by artificial intelligence (AI) chatbots, including the generative pretrained transformer 3 (GPT-3) chatbot (ChatGPT-3) is unknown. This study evaluated the accuracy of differential-diagnosis lists generated by ChatGPT-3 for clinical vignettes with common chief complaints. General internal medicine physicians created clinical cases, correct diagnoses, and five differential diagnoses for ten common chief complaints. The rate of correct diagnosis by ChatGPT-3 within the ten differential-diagnosis lists was 28/30 (93.3%). The rate of correct diagnosis by physicians was still superior to that by ChatGPT-3 within the five differential-diagnosis lists (98.3% vs. 83.3%, p = 0.03). The rate of correct diagnosis by physicians was also superior to that by ChatGPT-3 in the top diagnosis (53.3% vs. 93.3%, p < 0.001). The rate of consistent differential diagnoses among physicians within the ten differential-diagnosis lists generated by ChatGPT-3 was 62/88 (70.5%). In summary, this study demonstrates the high diagnostic accuracy of differential-diagnosis lists generated by ChatGPT-3 for clinical cases with common chief complaints. This suggests that AI chatbots such as ChatGPT-3 can generate a well-differentiated diagnosis list for common chief complaints. However, the order of these lists can be improved in the future

Evaluating ChatGPT-4’s Accuracy in Identifying Final Diagnoses Within Differential Diagnoses Compared With Those of Physicians: Experimental Study for Diagnostic Cases

BackgroundThe potential of artificial intelligence (AI) chatbots, particularly ChatGPT with GPT-4 (OpenAI), in assisting with medical diagnosis is an emerging research area. However, it is not yet clear how well AI chatbots can evaluate whether the final diagnosis is included in differential diagnosis lists. ObjectiveThis study aims to assess the capability of GPT-4 in identifying the final diagnosis from differential-diagnosis lists and to compare its performance with that of physicians for case report series. MethodsWe used a database of differential-diagnosis lists from case reports in the American Journal of Case Reports, corresponding to final diagnoses. These lists were generated by 3 AI systems: GPT-4, Google Bard (currently Google Gemini), and Large Language Models by Meta AI 2 (LLaMA2). The primary outcome was focused on whether GPT-4’s evaluations identified the final diagnosis within these lists. None of these AIs received additional medical training or reinforcement. For comparison, 2 independent physicians also evaluated the lists, with any inconsistencies resolved by another physician. ResultsThe 3 AIs generated a total of 1176 differential diagnosis lists from 392 case descriptions. GPT-4’s evaluations concurred with those of the physicians in 966 out of 1176 lists (82.1%). The Cohen κ coefficient was 0.63 (95% CI 0.56-0.69), indicating a fair to good agreement between GPT-4 and the physicians’ evaluations. ConclusionsGPT-4 demonstrated a fair to good agreement in identifying the final diagnosis from differential-diagnosis lists, comparable to physicians for case report series. Its ability to compare differential diagnosis lists with final diagnoses suggests its potential to aid clinical decision-making support through diagnostic feedback. While GPT-4 showed a fair to good agreement for evaluation, its application in real-world scenarios and further validation in diverse clinical environments are essential to fully understand its utility in the diagnostic process

sj-doc-1-dhj-10.1177_20552076241233689 - Supplemental material for Clinical decision support system using a machine learning model to assist simultaneous cardiopulmonary auscultation: Open-label randomized controlled trial

Supplemental material, sj-doc-1-dhj-10.1177_20552076241233689 for Clinical decision support system using a machine learning model to assist simultaneous cardiopulmonary auscultation: Open-label randomized controlled trial by Takanobu Hirosawa, Tetsu Sakamoto, Yukinori Harada, Kazuki Tokumasu and Taro Shimizu in DIGITAL HEALTH</p