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精神病和双相情感障碍谱系中的听觉配对刺激反应及其与临床特征的关系。

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

作者信息

Parker David A, Trotti Rebekah L, McDowell Jennifer E, Keedy Sarah K, Gershon Elliot S, Ivleva Elena I, Pearlson Godfrey D, Keshavan Matcheri S, Tamminga Carol A, Sweeney John A, Clementz Brett A

机构信息

Department of Psychology, University of Georgia, Georgia.

Departments of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Georgia.

出版信息

Biomark Neuropsychiatry. 2020 Dec;3. doi: 10.1016/j.bionps.2020.100014. Epub 2020 May 12.

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.

摘要

背景

听觉配对刺激范式期间的脑电图(EEG)反应被认为是精神病综合征的生物标志物。双相情感障碍 - 精神分裂症中间型网络(B - SNIP1)的首次迭代显示,精神分裂症(SZ)、分裂情感性障碍(SAD)和伴有精神病性症状的双相情感障碍(BDP)存在独特且常见的异常模式。本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,即无精神病性症状的双相情感障碍(BDNP)。

方法

比较了来自B - SNIP第二次迭代的精神病患者(n = 597;SZ = 225,SAD = 201,BDP = 171)、BDNP患者(n = 66)和健康受试者(n = 415)在64导EEG记录中的配对刺激反应。对EEG活动在电压和时频域进行了分析。通过传感器主成分分析(sPCA)有效地捕捉EEG对配对刺激的电压反应。通过Morlet小波程序计算诱发功率。频率主成分分析将诱发功率数据分为三个频段:低频(4 - 17Hz)、β频段(18 - 32Hz)和γ频段(33 - 55Hz)。然后将每个时间进程(ERP电压、低频、β频段和γ频段)分割为20ms的时间段,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EEG成分,我们使用了多变量判别分析和相关分析。这种方法产生了一组简化的测量指标,可能对后续的生物标志物研究有用。

结果

组间方差分析确定了17个时间范围显示出显著的组间差异(经FDR校正后p <.05),建设性地重复了B - SNIP1的研究结果。多变量线性判别分析简洁地选择了最能解释组间差异的变量:对S1和S2的P50反应将BDNP与健康和精神病患者独特地区分开来(BDNP > 所有其他组);S1的N100反应沿着精神病理学严重程度轴区分各组(健康对照组 > BDNP > BDP > SAD > SZ);S1的P200反应反映了精神病性精神病理学(健康对照组/BDNP > SAD/SZ/BDP);S2刺激的准备期将SZ与其他组区分开来(SZ > SAD/BDP > 健康对照组/BDNP)。典型相关分析确定了S1的N100、S1的N200和S2准备期的神经反应与阳性和阴性症状评定量表(PANSS)的阳性症状及社会功能之间的关联。S1的P50和S1的N100期间的神经反应与PANSS阴性/一般症状、抑郁自评量表(MADRS)和躁狂症状相关。

结论

本研究建设性地重复了先前B - SNIP1关于在SZ、SAD和BDP的配对刺激任务中观察到的听觉偏差的研究。纳入BDNP组有助于识别与情感性与非情感性临床表型更密切相关的生物标志物,以及BDP和BDNP之间的神经差异。鉴于一些生物标志物在所有精神病中都有,而有些是情感综合征所特有的,这些发现对疾病分类学和未来的转化研究具有重要意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a67c/9837793/d90072f6da26/nihms-1849680-f0001.jpg

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