Auditory paired-stimuli responses across the psychosis and bipolar spectrum and their relationship to clinical features

Q2 Medicine
David A. Parker , Rebekah L. Trotti , Jennifer E. McDowell , Sarah K. Keedy , Elliot S. Gershon , Elena I. Ivleva , Godfrey D. Pearlson , Matcheri S. Keshavan , Carol A. Tamminga , John A. Sweeney , Brett A. Clementz
{"title":"Auditory paired-stimuli responses across the psychosis and bipolar spectrum and their relationship to clinical features","authors":"David A. Parker ,&nbsp;Rebekah L. Trotti ,&nbsp;Jennifer E. McDowell ,&nbsp;Sarah K. Keedy ,&nbsp;Elliot S. Gershon ,&nbsp;Elena I. Ivleva ,&nbsp;Godfrey D. Pearlson ,&nbsp;Matcheri S. Keshavan ,&nbsp;Carol A. Tamminga ,&nbsp;John A. Sweeney ,&nbsp;Brett A. Clementz","doi":"10.1016/j.bionps.2020.100014","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>EEG responses during auditory paired-stimuli paradigms are putative biomarkers of psychosis syndromes. The initial iteration of the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP1) showed unique and common patterns of abnormalities across schizophrenia (SZ), schizoaffective disorder (SAD), and bipolar disorder with psychosis (BDP). This study replicates those findings in new and large samples of psychosis cases and extends them to an important comparison group, bipolar disorder without psychosis (BDNP).</p></div><div><h3>Methods</h3><p>Paired stimuli responses from 64-sensor EEG recording were compared across psychosis (n = 597; SZ = 225, SAD = 201, BDP = 171), BDNP (n = 66), and healthy (n = 415) subjects from the second iteration of B-SNIP. EEG activity was analyzed in voltage and in the time-frequency domain. Principal component analysis (PCA) over sensors (sPCA) was used to efficiently capture EEG voltage responses to the paired stimuli. Evoked power was calculated via a Morlet wavelet procedure. A frequency PCA divided evoked power data into three frequency bands: Low (4−17 Hz), Beta (18−32 Hz), and Gamma (33−55 Hz). Each time-course (ERP Voltage, Low, Beta, and Gamma) were then segmented into 20 ms bins and analyzed for group differences. To efficiently summarize the multiple EEG components that best captured group differences we used multivariate discriminant and correlational analyses. This approach yields a reduced set of measures that may be useful in subsequent biomarker investigations.</p></div><div><h3>Results</h3><p>Group ANOVAs identified 17 time-ranges that showed significant group differences (p &lt; .05 after FDR correction), constructively replicating B-SNIP1 findings. Multivariate linear discriminant analysis parsimoniously selected variables that best accounted for group differences: The P50 response to S1 and S2 uniquely separated BDNP from healthy and psychosis subjects (BDNP &gt; all other groups); the S1 N100 response separated groups along an axis of psychopathology severity (HC &gt; BDNP &gt; BDP &gt; SAD &gt; SZ); the S1 P200 response indexed psychosis psychopathology (HC/BDNP &gt; SAD/SZ/BDP); and the preparatory period to the S2 stimulus separated SZ from other groups (SZ &gt; SAD/BDP&gt;HC/BDNP).</p><p>Canonical correlation identified an association between the neural responses during the S1 N100, S1 N200 and S2 preparatory period and PANSS positive symptoms and social functioning. The neural responses during the S1 P50 and S1 N100 were associated with PANSS Negative/General, MADRS and Young Mania symptoms.</p></div><div><h3>Conclusions</h3><p>This study constructively replicated prior B-SNIP1 research on auditory deviations observed during the paired stimuli task in SZ, SAD and BDP. Inclusion of a group of BDNP allows for the identification of biomarkers more closely related to affective versus nonaffective clinical phenotypes and neural distinctions between BDP and BDNP. Findings have implications for nosology and future translational work given that some biomarkers are shared across all psychosis and some are unique to affective syndromes.</p></div>","PeriodicalId":52767,"journal":{"name":"Biomarkers in Neuropsychiatry","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.bionps.2020.100014","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomarkers in Neuropsychiatry","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666144620300046","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 6

Abstract

Background

EEG responses during auditory paired-stimuli paradigms are putative biomarkers of psychosis syndromes. The initial iteration of the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP1) showed unique and common patterns of abnormalities across schizophrenia (SZ), schizoaffective disorder (SAD), and bipolar disorder with psychosis (BDP). This study replicates those findings in new and large samples of psychosis cases and extends them to an important comparison group, bipolar disorder without psychosis (BDNP).

Methods

Paired stimuli responses from 64-sensor EEG recording were compared across psychosis (n = 597; SZ = 225, SAD = 201, BDP = 171), BDNP (n = 66), and healthy (n = 415) subjects from the second iteration of B-SNIP. EEG activity was analyzed in voltage and in the time-frequency domain. Principal component analysis (PCA) over sensors (sPCA) was used to efficiently capture EEG voltage responses to the paired stimuli. Evoked power was calculated via a Morlet wavelet procedure. A frequency PCA divided evoked power data into three frequency bands: Low (4−17 Hz), Beta (18−32 Hz), and Gamma (33−55 Hz). Each time-course (ERP Voltage, Low, Beta, and Gamma) were then segmented into 20 ms bins and analyzed for group differences. To efficiently summarize the multiple EEG components that best captured group differences we used multivariate discriminant and correlational analyses. This approach yields a reduced set of measures that may be useful in subsequent biomarker investigations.

Results

Group ANOVAs identified 17 time-ranges that showed significant group differences (p < .05 after FDR correction), constructively replicating B-SNIP1 findings. Multivariate linear discriminant analysis parsimoniously selected variables that best accounted for group differences: The P50 response to S1 and S2 uniquely separated BDNP from healthy and psychosis subjects (BDNP > all other groups); the S1 N100 response separated groups along an axis of psychopathology severity (HC > BDNP > BDP > SAD > SZ); the S1 P200 response indexed psychosis psychopathology (HC/BDNP > SAD/SZ/BDP); and the preparatory period to the S2 stimulus separated SZ from other groups (SZ > SAD/BDP>HC/BDNP).

Canonical correlation identified an association between the neural responses during the S1 N100, S1 N200 and S2 preparatory period and PANSS positive symptoms and social functioning. The neural responses during the S1 P50 and S1 N100 were associated with PANSS Negative/General, MADRS and Young Mania symptoms.

Conclusions

This study constructively replicated prior B-SNIP1 research on auditory deviations observed during the paired stimuli task in SZ, SAD and BDP. Inclusion of a group of BDNP allows for the identification of biomarkers more closely related to affective versus nonaffective clinical phenotypes and neural distinctions between BDP and BDNP. Findings have implications for nosology and future translational work given that some biomarkers are shared across all psychosis and some are unique to affective syndromes.

Abstract Image

Abstract Image

Abstract Image

精神病和双相情感障碍的听觉配对刺激反应及其与临床特征的关系
背景:听觉配对刺激范式中的deeg反应被认为是精神病综合征的生物标志物。双相-精神分裂症中间表型网络(B-SNIP1)的初始迭代显示了精神分裂症(SZ)、分裂情感性障碍(SAD)和双相精神障碍伴精神病(BDP)的独特和共同的异常模式。这项研究在新的大量精神病病例样本中重复了这些发现,并将其扩展到一个重要的对照组,双相情感障碍无精神病(BDNP)。方法比较64个传感器脑电图记录的西班牙刺激反应(n = 597;SZ = 225, SAD = 201, BDP = 171), BDNP (n = 66)和健康(n = 415)来自第二次B-SNIP的受试者。在电压域和时频域对脑电活动进行分析。利用传感器上的主成分分析(PCA)有效地捕捉成对刺激下的脑电电压响应。通过Morlet小波计算诱发功率。频率PCA将诱发功率数据分为三个频段:Low (4 - 17 Hz), Beta (18 - 32 Hz)和Gamma (33 - 55 Hz)。然后将每个时间过程(ERP Voltage, Low, Beta和Gamma)分割为20 ms bin并分析组间差异。为了有效地总结最能捕捉组间差异的多个脑电分量,我们使用了多变量判别和相关分析。这种方法产生了一套减少的措施,可能在随后的生物标志物研究中有用。结果组间方差分析确定了17个时间范围,组间差异显著(p <FDR校正后0.05),建设性地复制了B-SNIP1的发现。多变量线性判别分析简约地选择了最能解释组差异的变量:P50对S1和S2的反应唯一地将BDNP与健康和精神病受试者分开(BDNP >所有其他组);S1 N100反应沿精神病理严重程度轴(HC >BDNP祝辞和平民主党比;悲伤的祝辞SZ);S1 P200反应索引精神病精神病理(HC/BDNP >悲伤/深圳/ BDP);S2刺激的准备时间将SZ与其他组分开(SZ >悲伤/ BDP> HC / BDNP)。典型相关发现S1 N100、S1 N200和S2准备期的神经反应与PANSS阳性症状和社会功能之间存在关联。S1 P50和S1 N100期间的神经反应与PANSS阴性/一般、MADRS和青年躁狂症状相关。结论本研究建设性地重复了先前在SZ、SAD和BDP配对刺激任务中观察到的听觉偏差的B-SNIP1研究。纳入一组BDNP,可以识别与BDP和BDNP之间的情感与非情感临床表型以及神经差异更密切相关的生物标志物。鉴于一些生物标志物在所有精神病中都是共享的,而一些是情感综合征所特有的,这些发现对分类学和未来的转化工作具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Biomarkers in Neuropsychiatry
Biomarkers in Neuropsychiatry Medicine-Psychiatry and Mental Health
CiteScore
4.00
自引率
0.00%
发文量
12
审稿时长
7 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信