The overlap across psychotic disorders: A functional network connectivity analysis

被引:0
|
作者
Dini, Hossein [1 ]
Bruni, Luis E. [1 ]
Ramsoy, Thomas Z. [2 ,3 ]
Calhoun, Vince D. [4 ,5 ,6 ,7 ]
Sendi, Mohammad S. E. [7 ,8 ,9 ]
机构
[1] Aalborg Univ, Dept Architecture Design & Media Technol, Augmented Cognit Lab, Copenhagen, Denmark
[2] Neurons Inc, Dept Appl Neurosci, Taastrup, Denmark
[3] Singular Univ, Fac Neurosci, Santa Clara, CA USA
[4] Georgia Inst Technol, Wallace H Coulter Dept Biomed Engn, Atlanta, GA USA
[5] Emory Univ, Atlanta, GA USA
[6] Georgia Inst Technol, Dept Elect & Comp Engn, Atlanta, GA USA
[7] Emory Univ, Georgia State Univ, Georgia Inst Technol, Triinst Ctr Translat Res Neuroimaging & Data Sci, Atlanta, GA USA
[8] McLean Hosp, Boston, MA 02478 USA
[9] Harvard Med Sch, Boston, MA 02115 USA
基金
美国国家卫生研究院; 美国国家科学基金会;
关键词
Schizophrenia disorder; Bipolar disorder; Schizoaffective disorder; Dynamic functional network connectivity; Static functional network connectivity; Resting-state functional MRI; BIPOLAR-SCHIZOPHRENIA NETWORK; DEFAULT MODE NETWORK; GIG-ICA APPLICATION; INTERMEDIATE PHENOTYPES; SCHIZOAFFECTIVE DISORDER; TREATMENT RESPONSE; CLASSIFICATION; MISDIAGNOSIS; BIOMARKERS; STATES;
D O I
10.1016/j.ijpsycho.2024.112354
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
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).
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页数:11
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