Augmented Cognition Lab, Department of Architecture, Design and Media Technology, Aalborg University, Copenhagen, Denmark.
Department of Applied Neuroscience, Neurons Inc., Taastrup, Denmark; Faculty of Neuroscience, Singularity University, Santa Clara, CA, United States.
Int J Psychophysiol. 2024 Jul;201:112354. doi: 10.1016/j.ijpsycho.2024.112354. Epub 2024 Apr 24.
Functional network connectivity (FNC) has previously been shown to distinguish patient groups from healthy controls (HC). However, the overlap across psychiatric disorders such as schizophrenia (SZ), bipolar (BP), and schizoaffective disorder (SAD) is not evident yet. This study focuses on studying the overlap across these three psychotic disorders in both dynamic and static FNC (dFNC/sFNC). We used resting-state fMRI, demographics, and clinical information from the Bipolar-Schizophrenia Network on Intermediate Phenotypes cohort (BSNIP). The data includes three groups of patients with schizophrenia (SZ, N = 181), bipolar (BP, N = 163), and schizoaffective (SAD, N = 130) and HC (N = 238) groups. After estimating each individual's dFNC, we group them into three distinct states. We evaluated two dFNC features, including occupancy rate (OCR) and distance travelled over time. Finally, the extracted features, including both sFNC and dFNC, are tested statistically across patients and HC groups. In addition, we explored the link between the clinical scores and the extracted features. We evaluated the connectivity patterns and their overlap among SZ, BP, and SAD disorders (false discovery rate or FDR corrected p < 0.05). Results showed dFNC captured unique information about overlap across disorders where all disorder groups showed similar pattern of activity in state 2. Moreover, the results showed similar patterns between SZ and SAD in state 1 which was different than BP. Finally, the distance travelled feature of SZ (average R = 0.245, p < 0.01) and combined distance travelled from all disorders was predictive of the PANSS symptoms scores (average R = 0.147, p < 0.01).
功能网络连接 (FNC) 先前已被证明可以区分患者组和健康对照组 (HC)。然而,精神障碍(如精神分裂症 (SZ)、双相 (BP) 和分裂情感障碍 (SAD))之间的重叠尚未显现。本研究专注于研究这三种精神病性障碍在动态和静态功能网络连接 (dFNC/sFNC) 中的重叠。我们使用静息态 fMRI、人口统计学数据和来自双相-精神分裂症网络中间表型队列 (BSNIP) 的临床信息。该数据包括三组患者:精神分裂症 (SZ,N=181)、双相 (BP,N=163)、分裂情感障碍 (SAD,N=130) 和 HC (N=238)。在估计每个人的 dFNC 后,我们将他们分为三个不同的状态。我们评估了两个 dFNC 特征,包括占有率 (OCR) 和随时间移动的距离。最后,提取的特征包括 sFNC 和 dFNC,在患者和 HC 组之间进行统计学测试。此外,我们还探讨了临床评分与提取特征之间的联系。我们评估了 SZ、BP 和 SAD 障碍之间的连接模式及其重叠(经错误发现率或 FDR 校正 p<0.05)。结果表明,dFNC 捕获了疾病之间重叠的独特信息,所有疾病组在状态 2 中表现出相似的活动模式。此外,结果表明 SZ 和 SAD 在状态 1 之间存在相似的模式,而与 BP 不同。最后,SZ 的移动距离特征 (平均 R=0.245,p<0.01) 和所有疾病的组合移动距离特征可以预测 PANSS 症状评分 (平均 R=0.147,p<0.01)。