Yinglin Han , Linglin Hua , Yi Xia , Hao Sun , JunLing Sheng , Zhongpeng Dai , Zhijian Yao , Qing Lu
{"title":"首发精神分裂症患者行为控制和冲动的神经关联:基于meg的β振荡分析","authors":"Yinglin Han , Linglin Hua , Yi Xia , Hao Sun , JunLing Sheng , Zhongpeng Dai , Zhijian Yao , Qing Lu","doi":"10.1016/j.jpsychires.2025.05.079","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Impaired behavioral control and heightened impulsivity are core neurocogntive deficits in schizophrenia. However, the underlying neural mechanisms remain poorly understood. Studying individuals at inherited risk for schizophrenia may reveal potential endophenotypes, aiding early biomarker identification. Magnetoencephalography (MEG) provides high temporal to investigate inhibitory control deficits at the neurophysiological level. This study hypothesized that patients with first-episode schizophrenia (FES) and their first-degree relatives (FDR) would exhibit altered beta-band oscillatory activity and discrupted functional connectivity during behavioral control tasks, reflecting distinct neural signatures of impaired inhibitory control impairment and impulsivity.</div></div><div><h3>Methods</h3><div>Our study comprised 20 patients with FES, 20 FDR, and 22 matched healthy controls (HCs) to perform a Go/NoGo task during MEG scanning. Beta-band oscillatory activity and functional connectivity (FC) were analyzed in key behavioral control regions, particularly the pre-supplementary motor area (pre-SMA) and left motor cortex (lM1). A machine learning classifier was applied to assess the discriminative power of these neurophysiological features.</div></div><div><h3>Results</h3><div>Compared to HCs, FES participants exhibited significant reduced beta power in both pre-SMA and lM1 (<em>P</em> < 0.005), along with increased beta-band connectivity between these regions during the late-stage of inhibition (<em>P</em> = 0.013). FDR sho<u>w</u>ed intermediate beta power reductions and FC increases, suggesting a potential inherited liability. BIS-11 impulsivity scores were significantly correlated with beta power in both regions (<em>P</em> < 0.01). A classification model integrating neural and behavioral features achieved an original accuracy of 88.7 % and a cross-validated accuracy of 72.6 %, with the highest classification performance observed in the FES group (95 %).</div></div><div><h3>Conclusions</h3><div>These findings highlight beta-band oscillations and pre-SMA-lM1 connectivity as potential neurophysiological markers of behavioral control deficits in schizophrenia. These results provide novel insights into the neural mechanisms underlying impulsivity in schizophrenia and highlight the potential utility of beta-band dynamics as biomarkers for early detection and intervention.</div></div>","PeriodicalId":16868,"journal":{"name":"Journal of psychiatric research","volume":"189 ","pages":"Pages 104-115"},"PeriodicalIF":3.7000,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Neural correlates of behavioral control and impulsivity in first-episode schizophrenia: A MEG-Based beta oscillation analysis\",\"authors\":\"Yinglin Han , Linglin Hua , Yi Xia , Hao Sun , JunLing Sheng , Zhongpeng Dai , Zhijian Yao , Qing Lu\",\"doi\":\"10.1016/j.jpsychires.2025.05.079\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>Impaired behavioral control and heightened impulsivity are core neurocogntive deficits in schizophrenia. However, the underlying neural mechanisms remain poorly understood. Studying individuals at inherited risk for schizophrenia may reveal potential endophenotypes, aiding early biomarker identification. Magnetoencephalography (MEG) provides high temporal to investigate inhibitory control deficits at the neurophysiological level. This study hypothesized that patients with first-episode schizophrenia (FES) and their first-degree relatives (FDR) would exhibit altered beta-band oscillatory activity and discrupted functional connectivity during behavioral control tasks, reflecting distinct neural signatures of impaired inhibitory control impairment and impulsivity.</div></div><div><h3>Methods</h3><div>Our study comprised 20 patients with FES, 20 FDR, and 22 matched healthy controls (HCs) to perform a Go/NoGo task during MEG scanning. Beta-band oscillatory activity and functional connectivity (FC) were analyzed in key behavioral control regions, particularly the pre-supplementary motor area (pre-SMA) and left motor cortex (lM1). A machine learning classifier was applied to assess the discriminative power of these neurophysiological features.</div></div><div><h3>Results</h3><div>Compared to HCs, FES participants exhibited significant reduced beta power in both pre-SMA and lM1 (<em>P</em> < 0.005), along with increased beta-band connectivity between these regions during the late-stage of inhibition (<em>P</em> = 0.013). FDR sho<u>w</u>ed intermediate beta power reductions and FC increases, suggesting a potential inherited liability. BIS-11 impulsivity scores were significantly correlated with beta power in both regions (<em>P</em> < 0.01). A classification model integrating neural and behavioral features achieved an original accuracy of 88.7 % and a cross-validated accuracy of 72.6 %, with the highest classification performance observed in the FES group (95 %).</div></div><div><h3>Conclusions</h3><div>These findings highlight beta-band oscillations and pre-SMA-lM1 connectivity as potential neurophysiological markers of behavioral control deficits in schizophrenia. These results provide novel insights into the neural mechanisms underlying impulsivity in schizophrenia and highlight the potential utility of beta-band dynamics as biomarkers for early detection and intervention.</div></div>\",\"PeriodicalId\":16868,\"journal\":{\"name\":\"Journal of psychiatric research\",\"volume\":\"189 \",\"pages\":\"Pages 104-115\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2025-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of psychiatric research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0022395625003899\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHIATRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of psychiatric research","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0022395625003899","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHIATRY","Score":null,"Total":0}
Neural correlates of behavioral control and impulsivity in first-episode schizophrenia: A MEG-Based beta oscillation analysis
Background
Impaired behavioral control and heightened impulsivity are core neurocogntive deficits in schizophrenia. However, the underlying neural mechanisms remain poorly understood. Studying individuals at inherited risk for schizophrenia may reveal potential endophenotypes, aiding early biomarker identification. Magnetoencephalography (MEG) provides high temporal to investigate inhibitory control deficits at the neurophysiological level. This study hypothesized that patients with first-episode schizophrenia (FES) and their first-degree relatives (FDR) would exhibit altered beta-band oscillatory activity and discrupted functional connectivity during behavioral control tasks, reflecting distinct neural signatures of impaired inhibitory control impairment and impulsivity.
Methods
Our study comprised 20 patients with FES, 20 FDR, and 22 matched healthy controls (HCs) to perform a Go/NoGo task during MEG scanning. Beta-band oscillatory activity and functional connectivity (FC) were analyzed in key behavioral control regions, particularly the pre-supplementary motor area (pre-SMA) and left motor cortex (lM1). A machine learning classifier was applied to assess the discriminative power of these neurophysiological features.
Results
Compared to HCs, FES participants exhibited significant reduced beta power in both pre-SMA and lM1 (P < 0.005), along with increased beta-band connectivity between these regions during the late-stage of inhibition (P = 0.013). FDR showed intermediate beta power reductions and FC increases, suggesting a potential inherited liability. BIS-11 impulsivity scores were significantly correlated with beta power in both regions (P < 0.01). A classification model integrating neural and behavioral features achieved an original accuracy of 88.7 % and a cross-validated accuracy of 72.6 %, with the highest classification performance observed in the FES group (95 %).
Conclusions
These findings highlight beta-band oscillations and pre-SMA-lM1 connectivity as potential neurophysiological markers of behavioral control deficits in schizophrenia. These results provide novel insights into the neural mechanisms underlying impulsivity in schizophrenia and highlight the potential utility of beta-band dynamics as biomarkers for early detection and intervention.
期刊介绍:
Founded in 1961 to report on the latest work in psychiatry and cognate disciplines, the Journal of Psychiatric Research is dedicated to innovative and timely studies of four important areas of research:
(1) clinical studies of all disciplines relating to psychiatric illness, as well as normal human behaviour, including biochemical, physiological, genetic, environmental, social, psychological and epidemiological factors;
(2) basic studies pertaining to psychiatry in such fields as neuropsychopharmacology, neuroendocrinology, electrophysiology, genetics, experimental psychology and epidemiology;
(3) the growing application of clinical laboratory techniques in psychiatry, including imagery and spectroscopy of the brain, molecular biology and computer sciences;