Hossein Dini , Luis E. Bruni , Thomas Z. Ramsøy , Vince D. Calhoun , Mohammad S.E. Sendi
{"title":"The overlap across psychotic disorders: A functional network connectivity analysis","authors":"Hossein Dini , Luis E. Bruni , Thomas Z. Ramsøy , Vince D. Calhoun , Mohammad S.E. Sendi","doi":"10.1016/j.ijpsycho.2024.112354","DOIUrl":null,"url":null,"abstract":"<div><p>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, <em>N</em> = 181), bipolar (BP, <em>N</em> = 163), and schizoaffective (SAD, <em>N</em> = 130) and HC (<em>N</em> = 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 <em>p</em> < 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 <em>R</em> = 0.245, <em>p</em> < 0.01) and combined distance travelled from all disorders was predictive of the PANSS symptoms scores (average <em>R</em> = 0.147, p < 0.01).</p></div>","PeriodicalId":54945,"journal":{"name":"International Journal of Psychophysiology","volume":"201 ","pages":"Article 112354"},"PeriodicalIF":2.5000,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Psychophysiology","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167876024000588","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
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).
期刊介绍:
The International Journal of Psychophysiology is the official journal of the International Organization of Psychophysiology, and provides a respected forum for the publication of high quality original contributions on all aspects of psychophysiology. The journal is interdisciplinary and aims to integrate the neurosciences and behavioral sciences. Empirical, theoretical, and review articles are encouraged in the following areas:
• Cerebral psychophysiology: including functional brain mapping and neuroimaging with Event-Related Potentials (ERPs), Positron Emission Tomography (PET), Functional Magnetic Resonance Imaging (fMRI) and Electroencephalographic studies.
• Autonomic functions: including bilateral electrodermal activity, pupillometry and blood volume changes.
• Cardiovascular Psychophysiology:including studies of blood pressure, cardiac functioning and respiration.
• Somatic psychophysiology: including muscle activity, eye movements and eye blinks.