Neuropsychiatric electrophysiology最新文献

Nonlinear EEG biomarker profiles for autism and absence epilepsy 自闭症和失神性癫痫的非线性脑电图生物标志物图谱

Neuropsychiatric electrophysiology Pub Date : 2017-03-23 DOI: 10.1186/S40810-017-0023-X

W. Bosl, T. Loddenkemper, C. Nelson

引用次数: 43

Trait aspects of auditory mismatch negativity predict response to auditory training in individuals with early illness schizophrenia. 听觉错配负性的特质可预测早期精神分裂症患者对听觉训练的反应。

Neuropsychiatric electrophysiology Pub Date : 2017-01-01 Epub Date: 2017-06-09 DOI: 10.1186/s40810-017-0024-9

Bruno Biagianti, Brian J Roach, Melissa Fisher, Rachel Loewy, Judith M Ford, Sophia Vinogradov, Daniel H Mathalon

{"title":"Trait aspects of auditory mismatch negativity predict response to auditory training in individuals with early illness schizophrenia.","authors":"Bruno Biagianti, Brian J Roach, Melissa Fisher, Rachel Loewy, Judith M Ford, Sophia Vinogradov, Daniel H Mathalon","doi":"10.1186/s40810-017-0024-9","DOIUrl":"10.1186/s40810-017-0024-9","url":null,"abstract":"Background: Individuals with schizophrenia have heterogeneous impairments of the auditory processing system that likely mediate differences in the cognitive gains induced by auditory training (AT). Mismatch negativity (MMN) is an event-related potential component reflecting auditory echoic memory, and its amplitude reduction in schizophrenia has been linked to cognitive deficits. Therefore, MMN may predict response to AT and identify individuals with schizophrenia who have the most to gain from AT. Furthermore, to the extent that AT strengthens auditory deviance processing, MMN may also serve as a readout of the underlying changes in the auditory system induced by AT.Methods: Fifty-six individuals early in the course of a schizophrenia-spectrum illness (ESZ) were randomly assigned to 40 h of AT or Computer Games (CG). Cognitive assessments and EEG recordings during a multi-deviant MMN paradigm were obtained before and after AT and CG. Changes in these measures were compared between the treatment groups. Baseline and trait-like MMN data were evaluated as predictors of treatment response. MMN data collected with the same paradigm from a sample of Healthy Controls (HC; n = 105) were compared to baseline MMN data from the ESZ group.Results: Compared to HC, ESZ individuals showed significant MMN reductions at baseline (p = .003). Reduced Double-Deviant MMN was associated with greater general cognitive impairment in ESZ individuals (p = .020). Neither ESZ intervention group showed significant change in MMN. We found high correlations in all MMN deviant types (rs = .59-.68, all ps < .001) between baseline and post-intervention amplitudes irrespective of treatment group, suggesting trait-like stability of the MMN signal. Greater deficits in trait-like Double-Deviant MMN predicted greater cognitive improvements in the AT group (p = .02), but not in the CG group.Conclusions: In this sample of ESZ individuals, AT had no effect on auditory deviance processing as assessed by MMN. In ESZ individuals, baseline MMN was significantly reduced relative to HCs, and associated with global cognitive impairment. MMN did not show changes after AT and exhibited trait-like stability. Greater deficits in the trait aspects of Double-Deviant MMN predicted greater gains in global cognition in response to AT, suggesting that MMN may identify individuals who stand to gain the most from AT.Trial registration: NCT00694889. Registered 1 August 2007.","PeriodicalId":91583,"journal":{"name":"Neuropsychiatric electrophysiology","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5568850/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35449186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Disturbed theta and gamma coupling as a potential mechanism for visuospatial working memory dysfunction in people with schizophrenia 干扰的θ和γ耦合作为精神分裂症患者视觉空间工作记忆功能障碍的潜在机制

Neuropsychiatric electrophysiology Pub Date : 2016-11-15 DOI: 10.1186/S40810-016-0022-3

Peter A Lynn, S. Sponheim

引用次数: 9

Electrophysiological insights into connectivity anomalies in schizophrenia: a systematic review 精神分裂症中连通性异常的电生理学见解:系统回顾

Neuropsychiatric electrophysiology Pub Date : 2016-11-05 DOI: 10.1186/S40810-016-0020-5

Matteo Maran, T. Grent-‘t-Jong, P. Uhlhaas

引用次数: 43

19th biennial IPEG Meeting 第19届两年一次的IPEG会议

Neuropsychiatric electrophysiology Pub Date : 2016-11-01 DOI: 10.1186/S40810-016-0021-4

I. Timofeev, L. Kenemans, P. Fabene, A. Ahnaou, S. Olbrich, R. Oostenveld, M. Arns, N. Boutros, Fernando Lopes da Silva, Ole J. Jensen, S. Loo, H. Landolt, J. Schoffelen, A. Gouw, A. Hillebrand, M. Demuru, Peterjan Ris, P. Scheltens, C. Stam, I. A. Nissen, Ilse E C W van Straaten, J. Reijneveld, Sonja Simpraga, R. Alvarez-Jimenez, H. Mansvelder, J. V. van Gerven, G. Groeneveld, Simon-Shlomo Poil, K. Linkenkaer-Hansen, M. V. van Putten, M. Tjepkema-Cloostermans, J. Hofmeijer, C. Babiloni, A. I. Triggiani, R. Lizio, S. Cordone, Antonio Brunetti, Giacomo Tattoli, Vitoantonio Bevilacqua, A. Soricelli, R. Ferri, F. Nobili, L. Gesualdo, J. Millán-Calenti, A. Buján, R. Tortelli, V. Cardinali, Orietta Barulli, A. Giannini, Pantaleo Spagnolo, Silvia Armenise, Grazia Buenza, G. Scianatico, G. Logroscino, G. Frisoni, C. Del Percio, J. Hipp, R. Comley, D. Bentley, Michael G. M. Derks, P. Garcés, F. Knoflach, S. Lennon-Chrimes, S. Nave, Jana Noldeke, N. Seneca, G. Trube, C. Wandel, Andrew WThomas, Maria-Clemencia Hern

引用次数: 1

The impact of Arterial Pulse Impedance Artifact (APIA) on test-retest reliability of quantitative EEG 动脉脉冲阻抗伪影对定量脑电图重测信度的影响

Neuropsychiatric electrophysiology Pub Date : 2016-10-04 DOI: 10.1186/S40810-016-0019-Y

G. Ulrich, Willi Schlosser, G. Juckel

引用次数: 2

The modulating effects of brain stimulation on emotion regulation and decision-making 脑刺激对情绪调节和决策的调节作用

Neuropsychiatric electrophysiology Pub Date : 2016-06-10 DOI: 10.1186/S40810-016-0018-Z

Kyung Mook Choi, D. T. Scott, Seung-Lark Lim

引用次数: 14

Cortical activation patterns in healthy subjects during the traditional Japanese word generation task Shiritori determined by multichannel near-infrared spectroscopy 用多通道近红外光谱测定健康受试者在传统日语词生成任务Shiritori中的皮层激活模式

Neuropsychiatric electrophysiology Pub Date : 2016-01-24 DOI: 10.1186/S40810-016-0016-1

T. Nakahachi, Ryouhei Ishii, L. Canuet, Hidetoshi Takahashi, M. Ishitobi, Y. Kamio, M. Iwase

引用次数: 2

Coherence a measure of the brain networks: past and present 连贯性:衡量过去和现在的大脑网络

Neuropsychiatric electrophysiology Pub Date : 2016-01-17 DOI: 10.1186/S40810-015-0015-7

S. Bowyer

引用次数: 227

Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults. 机器学习识别预测精神分裂症和健康成人工作记忆表现的脑电图特征。

Neuropsychiatric electrophysiology Pub Date : 2016-01-01 Epub Date: 2016-02-11 DOI: 10.1186/s40810-016-0017-0

Jason K Johannesen, Jinbo Bi, Ruhua Jiang, Joshua G Kenney, Chi-Ming A Chen

{"title":"Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults.","authors":"Jason K Johannesen, Jinbo Bi, Ruhua Jiang, Joshua G Kenney, Chi-Ming A Chen","doi":"10.1186/s40810-016-0017-0","DOIUrl":"https://doi.org/10.1186/s40810-016-0017-0","url":null,"abstract":"Background: With millisecond-level resolution, electroencephalographic (EEG) recording provides a sensitive tool to assay neural dynamics of human cognition. However, selection of EEG features used to answer experimental questions is typically determined a priori. The utility of machine learning was investigated as a computational framework for extracting the most relevant features from EEG data empirically.Methods: Schizophrenia (SZ; n = 40) and healthy community (HC; n = 12) subjects completed a Sternberg Working Memory Task (SWMT) during EEG recording. EEG was analyzed to extract 5 frequency components (theta1, theta2, alpha, beta, gamma) at 4 processing stages (baseline, encoding, retention, retrieval) and 3 scalp sites (frontal-Fz, central-Cz, occipital-Oz) separately for correctly and incorrectly answered trials. The 1-norm support vector machine (SVM) method was used to build EEG classifiers of SWMT trial accuracy (correct vs. incorrect; Model 1) and diagnosis (HC vs. SZ; Model 2). External validity of SVM models was examined in relation to neuropsychological test performance and diagnostic classification using conventional regression-based analyses.Results: SWMT performance was significantly reduced in SZ (p < .001). Model 1 correctly classified trial accuracy at 84 % in HC, and at 74 % when cross-validated in SZ data. Frontal gamma at encoding and central theta at retention provided highest weightings, accounting for 76 % of variance in SWMT scores and 42 % variance in neuropsychological test performance across samples. Model 2 identified frontal theta at baseline and frontal alpha during retrieval as primary classifiers of diagnosis, providing 87 % classification accuracy as a discriminant function.Conclusions: EEG features derived by SVM are consistent with literature reports of gamma's role in memory encoding, engagement of theta during memory retention, and elevated resting low-frequency activity in schizophrenia. Tests of model performance and cross-validation support the stability and generalizability of results, and utility of SVM as an analytic approach for EEG feature selection.","PeriodicalId":91583,"journal":{"name":"Neuropsychiatric electrophysiology","volume":"2 ","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40810-016-0017-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34633804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 101