Olin Neuropsychiatry Research Center, Institute of Living at Hartford Hospital, Hartford, Connecticut.
Department of Psychology, University of Georgia, Athens, Georgia.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2016 Nov;1(6):488-497. doi: 10.1016/j.bpsc.2016.07.001. Epub 2016 Jul 16.
We sought to examine resting-state functional magnetic resonance imaging connectivity measures in psychotic patients to both identify cumulative differences across psychosis and subsequently probe deficits across conventional DSM-IV diagnoses and a newly identified classification using cognitive/neurophysiological data (Biotypes).
We assessed 1125 subjects, including healthy control subjects, probands (schizophrenia, schizoaffective disorder, psychotic bipolar disorder), and relatives of probands. Probands and relatives were also segregated into Biotype groups (B1-B3, B1R-B3R using a method reported previously). Empirical resting-state functional magnetic resonance imaging networks were derived using independent component analysis. Global psychosis-related abnormalities were first identified. Subsequent post hoc t tests were performed across various diagnostic categories. Follow-up linear mixed model compared significance of within-proband differences across categories. Secondary analyses assessed correlations with biological profile scores.
Voxelwise tests between proband and control subjects revealed nine abnormal networks. Post hoc analysis revealed lower connectivity in most networks for all proband subgroups (DSM and Biotypes). Within-proband effect sizes of discrimination were marginally better for Biotypes over DSM. Reduced connectivity was noted in relatives of patients with schizophrenia in two networks and relatives of patients with psychotic bipolar disorder in one network. Biotype relatives showed similar deficits in one network. Connectivity deficits across four networks were significantly associated with cognitive control profile scores.
Overall, we found psychosis-related connectivity deficits in nine large-scale networks. Deficits in these networks tracked more closely with cognitive control factors, suggesting potential implications for disease profiling and therapeutic intervention. Biotypes performed marginally better in terms of separating out psychosis subgroups compared with conventional DSM or psychiatric diagnoses.
我们试图通过静息态功能磁共振成像连接测量来研究精神病患者,以确定跨精神病的累积差异,并随后探查传统 DSM-IV 诊断和使用认知/神经生理学数据(生物型)进行的新分类的缺陷。
我们评估了 1125 名受试者,包括健康对照者、先证者(精神分裂症、分裂情感障碍、精神病性双相障碍)和先证者的亲属。先证者和亲属也分为生物型组(使用以前报道的方法分为 B1-B3、B1R-B3R)。采用独立成分分析得出经验性静息态功能磁共振成像网络。首先确定与全局精神病相关的异常。随后对各种诊断类别进行事后 t 检验。后续线性混合模型比较了跨类别分类的个体内差异的显著性。二级分析评估了与生物学特征评分的相关性。
先证者与对照者之间的体素检验显示了九个异常网络。事后分析显示,所有先证者亚组(DSM 和生物型)的大多数网络连接性较低。与 DSM 相比,生物型的分类判别个体内效应大小略好。在精神分裂症患者的亲属中,两个网络和精神病性双相障碍患者的亲属中,一个网络的连接性降低。在两个网络中发现了精神分裂症患者亲属的连接性缺陷,在一个网络中发现了精神病性双相障碍患者亲属的连接性缺陷。生物型亲属在一个网络中也表现出类似的缺陷。四个网络的连接性缺陷与认知控制特征评分显著相关。
总体而言,我们在九个大规模网络中发现了与精神病相关的连接性缺陷。这些网络中的缺陷与认知控制因素更为密切相关,这表明对疾病特征分析和治疗干预有潜在的影响。与传统的 DSM 或精神科诊断相比,生物型在区分精神病亚组方面表现略好。