A Classification-Based Approach to Estimate the Number of Resting Functional Magnetic Resonance Imaging Dynamic Functional Connectivity States

Saha, Debbrata K.; Damaraju, Eswar; Rashid, Barnaly; Abrol, Anees; Plis, Sergey M.; Calhoun, Vince D.

doi:10.1089/brain.2020.0794

Cited by 24 publications

(20 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The dFNC provides information on time-varying connectivity variations throughout time (Allen et al, 2014). Unlike static functional network connectivity (sFNC), dFNC captures the local connectivity of each window rather than providing mean connectivity (Saha et al, 2021). Recent studies have shown that rs-fMRI brain functional connectivity is hugely dynamic and can reveal the underlying mechanisms of brain connection disparities in psychotic disorders (Garrity et al, 2007; Damaraju et al, 2014; Miller et al, 2016; Zhi et al, 2018; Dong et al, 2019; Sun et al, 2019; Rey et al, 2021; Tang et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…They proved that dFNC could capture decreased connectivity between the inferior parietal lobule and the rest of CCN regions, while sFNC cannot. Saha et al studied the dynamic versus static brain connectivity on rs-fMRI data and reported that the classification accuracy is maximum when they use either dFNC or combined dFNC and sFNC data implying that dFNC provides more information to classify HC vs. patient groups (Saha et al, 2021). In the current study, we first estimated whole-brain sFNC and then averaged the resulting connectivities within the networks to capture network-specific sFNC of each subject [Figure 1B and Figure 6A].…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Overlap across psychotic disorders: A functional network connectivity analysis

Dini

Bruni

Ramsøy

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Overlap across psychotic disorders: A functional network connectivity analysis

Dini

Bruni

Ramsøy

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The SVM has been widely applied in numerous neuroimaging classification studies and has achieved remarkable results due to its excellent generalization performance. Our motivation of using SVM over other approaches was due to its sensitivity, resilience to overfitting, ability to extract and interpret features, and superior performance in fMRI data classification (Wang et al 2019;De Martino et al 2008;Pereira, Mitchell, and Botvinick 2009;Ecker et al 2010;Liu et al 2013;Vergun et al 2013;Saha et al 2021) Feature selection was performed on the training set (50% were selected randomly every time). In order to select the most predictive features, we repeated the feature selection process for ten rounds and retained those features with a high average weight (top 70%) among all the rounds.…”

Section: Support Vector Machine-based Classification (Svm)mentioning

confidence: 99%

Multi-model Order ICA: A Data-driven Method for Evaluating Brain Functional Network Connectivity Within and Between Multiple Spatial Scales

Xing

Iraji

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

BackgroundWhile functional connectivity is widely studied, there has been little work studying functional connectivity at different spatial scales. Likewise, the relationship of functional connectivity between spatial scales is unknown.MethodsWe proposed an independent component analysis (ICA) - based approach to capture information at multiple model orders (component numbers) and to evaluate functional network connectivity (FNC) both within and between model orders. We evaluated the approach by studying group differences in the context of a study of resting fMRI (rsfMRI) data collected from schizophrenia (SZ) individuals and healthy controls (HC). The predictive ability of FNC at multiple spatial scales was assessed using support vector machine (SVM)-based classification.ResultsIn addition to consistent predictive patterns at both multiple-model orders and single model orders, unique predictive information was seen at multiple-model orders and in the interaction between model orders. We observed that the FNC between model order 25 and 50 maintained the highest predictive information between HC and SZ. Results highlighted the predictive ability of the somatomotor and visual domains both within and between model orders compared to other functional domains. Also, subcortical-somatomotor, temporal-somatomotor, and temporal-subcortical FNCs had relatively high weights in predicting SZ.ConclusionsIn sum, multi-model order ICA provides a more comprehensive way to study FNC, produces meaningful and interesting results which are applicable to future studies. We shared the spatial templates from this work at different model orders to provide a reference for the community, which can be leveraged in regression-based or fully automated (spatially constrained) ICA approaches.Impact StatementMulti-model order ICA provides a comprehensive way to study brain functional network connectivity within and between multiple spatial scales, highlighting findings that would have been ignored in single model order analysis. This work expands upon and adds to the relatively new literature on resting fMRI-based classification and prediction. Results highlighted the differentiating power of specific intrinsic connectivity networks on classifying brain disorders of schizophrenia patients and healthy participants, at different spatial scales. The spatial templates from this work provide a reference for the community, which can be leveraged in regression-based or fully automated ICA approaches.

show abstract

“…dFNC has shown promise as a biomarker for schizophrenia (Sendi et al, 2021a(Sendi et al, , 2021b), Alzheimer's disease (Sendi et al, 2021c), major depressive disorder (Sendi et al, 2021e), and autism spectrum disorder (Harlalka et al, 2019). It has been shown that dFNC improves classification between disordered and healthy conditions (Rashid et al, 2015;Saha et al, 2021) and provides more information about the pathology of neurological and neuropsychiatric disorders than its static counterpart (Menon and Krishnamurthy, 2019).…”

Section: Introductionmentioning

confidence: 99%

iSparse kmeans: a two-step clustering approach for big dynamic functional network connectivity data

Sendi

Salat

Miller

et al. 2022

Preprint

View full text Add to dashboard Cite

Background: Dynamic functional network connectivity (dFNC) estimated from resting-state functional magnetic imaging (rs-fMRI) studies the temporally varying of functional integration between brain networks. In a typical 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 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, called iterative sparse kmeans or iSparse kmeans, to analyze large dFNC data without having access to huge computational power. Method: In iSparse kmeans, 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 iSparse kmeans, we analyzed four dFNC datasets from the human connectome project (HCP). Results: We found that both conventional kmeans and iSparse kmeans generate similar brain dFNC states while iSparse kmeans is 27 times faster than the traditional method in finding the optimum number of clusters. We show that the results are replicated across four different datasets from HCP. Conclusion: We developed a new analytic pipeline which facilitates analysis of large dFNC datasets without having access to a huge computational power source. We validated the reproducibility of the result across multiple datasets.

show abstract

A Classification-Based Approach to Estimate the Number of Resting Functional Magnetic Resonance Imaging Dynamic Functional Connectivity States

Cited by 24 publications

References 38 publications

Overlap across psychotic disorders: A functional network connectivity analysis

Overlap across psychotic disorders: A functional network connectivity analysis

Multi-model Order ICA: A Data-driven Method for Evaluating Brain Functional Network Connectivity Within and Between Multiple Spatial Scales

iSparse kmeans: a two-step clustering approach for big dynamic functional network connectivity data

Contact Info

Product

Resources

About