Aberrant Dynamic Functional Connectivity of Default Mode Network in Schizophrenia and Links to Symptom Severity

Sendi, Mohammad S.E.; Zendehrouh, Elaheh; Ellis, Charles A.; Liang, Zhijia; Fu, Zening; Mathalon, Daniel H.; Ford, Judith M.; Preda, Adrian; Erp, Theo G.M. van; Miller, Robyn L.; Pearlson, Godfrey D.; Turner, Jessica A.; Calhoun, Vince D.

doi:10.1101/2021.01.03.425152

Cited by 6 publications

(8 citation statements)

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“…Past studies show that DMN activities alter in schizophrenia (Sendi et al, 2021). Decreased modulations of DMN in schizophrenia compared to healthy controls in our study might indicate such alterations.…”

Section: Discussionsupporting

confidence: 55%

Disrupted controlling mechanism of salience network on default-mode network and central-executive network in schizophrenia

Chand

Thakuri

Soni

2021

Preprint

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Neuroimaging studies suggest that the human brain consists of intrinsically organized large-scale neural networks. Among those networks, the interplay among default-mode network (DMN), salience network (SN), and central-executive network (CEN) has been widely employed to understand the functional interaction patterns in health and diseases. This triple network model suggests that SN causally controls DMN and CEN in healthy individuals. This interaction is often referred to as the dynamic controlling mechanism of SN. However, such interactions are not well understood in individuals with schizophrenia. In this study, we leveraged resting state functional magnetic resonance imaging (fMRI) data of schizophrenia (n = 67) and healthy controls (n = 81) to evaluate the functional interactions among DMN, SN, and CEN using dynamical causal modeling. In healthy controls, our analyses replicated previous findings that SN regulates DMN and CEN activities (Mann-Whitney U test; p < 10-8). In schizophrenia, however, our analyses revealed the disrupted SN-based controlling mechanism on DMN and CEN (Mann-Whitney U test; p < 10-16). These results indicate that the disrupted controlling mechanism of SN on two other neural networks may be a candidate neuroimaging phenotype in schizophrenia.

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Section: Discussionsupporting

confidence: 55%

Disrupted controlling mechanism of salience network on default-mode network and central-executive network in schizophrenia

Chand

Thakuri

Soni

2021

Preprint

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“…Then we concatenated extracted features of participants in a 𝐶 × 𝐶 × 𝑇 array (where C=53 denotes the number of ICs, and T= 178 denotes the number of windows). Finally, we concatenated the participant's calculated arrays to show brain connectivity changes between defined regions as a function of time as shown in Figure1C (Allen et al, 2014;Dini et al, 2021;Sendi et al, 2021dSendi et al, , 2021b. Moreover, we calculated static FNC of each subject by calculating the Pearson correlation across all 53 regions for the entire rs-fMRI session (Figure1B).…”

Section: Functional Network Connectivitymentioning

confidence: 99%

Overlap across psychotic disorders: A functional network connectivity analysis

Dini

Bruni

Ramsøy

et al. 2022

Preprint

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Functional network connectivity has previously been shown to distinguish patient groups from healthy controls (HC). However, the overlap across schizophrenia (SZ), bipolar (BP), and schizoaffective disorder (SAD) is not clear yet. This study focuses on finding overlap across these three psychotic disorders using dynamic FNC (dFNC) and compares it with static FNC (sFNC). We used resting-state fMRI, demographics, and clinical information from the Bipolar Schizophrenia Network on Intermediate Phenotypes cohort. The data includes three groups of patients with schizophrenia (SZP, N=102), bipolar (BPP, N=102), and schizoaffective (SADP, N=102), their relatives SZR (N=102), BPR (N=102), SADR (N=102), and HC (N=118) groups. After estimating each individual's dFNC, we put them into three identical states. We estimated five different features, including occupancy rate (OCR), number of transitions, the total number of transitions, and the total distance traveled. Finally, the extracted features are tested statistically across patients and HC groups. In addition, we explored the link between the clinical scores and the extracted features. We found the OCR difference between SZP and SZR in state2, between BPP and HC in state1, and between SADP and HC in state2. Also, state2 OCR separates SZP from BPP, state3 OCR separates BPP from SZP and SADP. Moreover, the OCR and traveled distance feature extracted from SZ and BP could significantly predict PANSS Total and PANSS General scores. Finally, combined distance features of all disorders showed a significant relationship to PANSS Total and PANSS General scores.

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“…For example, the UK Biobank dataset released neuroimaging data from more than 40,000 participants (Alfaro-Almagro et al, 2021) and has targeted acquiring data from 100,000 individuals (Alfaro- Almagro et al, 2018). Also, it has been discussed that many neuroimaging analytic pipelines are not scalable for massive data sets, including possibly tens, if not hundreds of thousands of participants (van Horn and Toga, 2014). The conventional dFNC pipeline.…”

Section: Introductionmentioning

confidence: 99%

Two-step clustering-based pipeline for big dynamic functional network connectivity data

et al. 2022

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BackgroundDynamic functional network connectivity (dFNC) estimated from resting-state functional magnetic imaging (rs-fMRI) studies the temporally varying functional integration between brain networks. In a conventional dFNC pipeline, a clustering stage to summarize the connectivity patterns that are transiently but reliably realized over the course of a scanning session. However, identifying the right number of clusters (or states) through a conventional clustering criterion computed by running the algorithm repeatedly over a large range of cluster numbers is time-consuming and requires substantial computational power even for typical dFNC datasets, and the computational demands become prohibitive as datasets become larger and scans longer. Here we developed a new dFNC pipeline based on a two-step clustering approach to analyze large dFNC data without having access to huge computational power.MethodsIn the proposed dFNC pipeline, we implement two-step clustering. In the first step, we randomly use a sub-sample dFNC data and identify several sets of states at different model orders. In the second step, we aggregate all dFNC states estimated from all iterations in the first step and use this to identify the optimum number of clusters using the elbow criteria. Additionally, we use this new reduced dataset and estimate a final set of states by performing a second kmeans clustering on the aggregated dFNC states from the first k-means clustering. To validate the reproducibility of results in the new pipeline, we analyzed four dFNC datasets from the human connectome project (HCP).ResultsWe found that both conventional and proposed dFNC pipelines generate similar brain dFNC states across all four sessions with more than 99% similarity. We found that the conventional dFNC pipeline evaluates the clustering order and finds the final dFNC state in 275 min, while this process takes only 11 min for the proposed dFNC pipeline. In other words, the new pipeline is 25 times faster than the traditional method in finding the optimum number of clusters and finding the final dFNC states. We also found that the new method results in better clustering quality than the conventional approach (p < 0.001). We show that the results are replicated across four different datasets from HCP.ConclusionWe developed a new analytic pipeline that facilitates the analysis of large dFNC datasets without having access to a huge computational power source. We validated the reproducibility of the result across multiple datasets.

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Aberrant Dynamic Functional Connectivity of Default Mode Network in Schizophrenia and Links to Symptom Severity

Cited by 6 publications

References 63 publications

Disrupted controlling mechanism of salience network on default-mode network and central-executive network in schizophrenia

Disrupted controlling mechanism of salience network on default-mode network and central-executive network in schizophrenia

Overlap across psychotic disorders: A functional network connectivity analysis

Two-step clustering-based pipeline for big dynamic functional network connectivity data

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