scholarly journals Graph Signal Processing of Low and High-Order Dynamic Functional Connectivity Networks Using EEG Resting-State for Schizophrenia: A Whole Brain Breakdown

2019 ◽  
Author(s):  
Stavros I. Dimitriadis
Keyword(s):  
Time Series ◽  
Functional Connectivity ◽  
Resting State ◽  
High Order ◽  
Phase Lag ◽  
Research Knowledge ◽  
Brain Areas ◽  
Dynamic Functional Connectivity ◽  
Connectivity Graph ◽  
Low Order

AbstractConventional static or dynamic functional connectivity graph (FCG/DFCG) referred to as low-order FCG focusing on temporal correlation estimates of the resting-state electroencephalography (rs-EEG) time series between any potential pair of brain areas. A DFCG is first constructed from multichannel recordings by adopting the methodology of sliding-window and a proper functional connectivity estimator. However, low-order FC ignores the high-level inter-relationship of brain areas. Recently, a high-order version of FCG has emerged by estimating the correlations of the time series that describe the fluctuations of the functional strength of every pair of ROIs across experimental time.In the present study, a dynamic functional connectivity graph (DFCG) has been estimated using the imaginary part of phase lag value (iPLV). We analyzed DFCG profiles of electroencephalographic resting state (eyes-closed) recordings of healthy controls subjects (n=39) and subjects with symptoms of schizophrenia (n=45) in basic frequency bands {δ,θ,α1,α2,β1,β2,γ}. In our analysis, we incorporated both intra and cross-frequency coupling modes. Adopting our recent Dominant Intrinsic Coupling Mode (DICM) model leads to the construction of an integrated DFCG (iDFCG) that encapsulates both the functional strength but also the DICM of every pair of brain areas. Based on the LO - IDFCG, we constructed the HO- IDFCG by adopting the cosine similarity between the time-series derived from the LO-DIFCG. At a second level, we estimated the laplacian transformations of both LO and HO-IDFCG and by calculating the temporal evolution of Synchronizability (Syn), four network metric time series (NMTSSyn) were produced. Following, a machine learning approach based on multi-kernel SVM with the four NMTSSynused as potential features and appropriate kernels, we succeeded a superior classification accuracy (∼98%). DICM and flexibility index (FI) achieved a classification with absolute performance (100 %)Schizophrenic subjects demonstrated a hypo-synchronization compared to healthy control group which can be interpreted as a low global synchronization of co-fluctuate functional patterns. Our analytic pathway could be helpful both for the design of reliable biomarkers and also for evaluating non-intervention treatments tailored to schizophrenia. EEG offers a low-cost environment for applied neuroscience and the transfer of research knowledge from neuroimaging labs to daily clinical practice.

High-Order Functional Connectivity Network for Differential Diagnosis of Depression

2019 ◽  
Vol 9 (6) ◽  
pp. 1095-1102
Author(s):  
Jian Yang ◽  
Xu Mao ◽  
Ning Liu ◽  
Ning Zhong
Keyword(s):  
Time Series ◽  
Differential Diagnosis ◽  
Functional Connectivity ◽  
Resting State ◽  
Correlation Time ◽  
Time Window ◽  
Resting State Fmri ◽  
High Order ◽  
Healthy Controls ◽  
Resting State Functional Connectivity

Resting-state functional connectivity (FC) changes dynamically and major depressive disorder (MDD) has abnormality in functional connectivity networks (FCNs), but few existing resting-state fMRI study on MDD utilizes the dynamics, especially for identifying depressive individuals from healthy controls. In this paper, we propose a methodological procedure for differential diagnosis of depression, called HN3D, which is based on high-order functional connectivity networks (HFCN). Firstly, HN3D extracts time series by independent component analysis, and partitions them into overlapped short series by sliding time window. Secondly, it constructs a FCN for each time window and concatenates correlation matrices of all FCNs to generate correlation time series. Then, correlation time series are grouped into different clusters and high-order correlations for HFCN is calculated based on their means. Finally, graph based features of HFCNs are extracted and selected for a linear discriminative classifier. Tested on 21 healthy controls and 20 MDD patients, HN3D achieved its best 100% classification accuracy, which is much higher than results based on stationary FCNs. In addition, most discriminative components of HN3D locate in default mode network and visual network, which are consistent with existing stationary-based results on depression. Though HN3D needs to be studied further, it is helpful for the differential diagnosis of depression and might have potentiality in identifying relevant biomarkers.

scholarly journals Modeling the Switching behavior of Functional Connectivity Microstates (FCμstates) as a Novel Biomarker for Mild Cognitive Impairment

2019 ◽  
Author(s):  
SI Dimitriadis ◽  
María Eugenia López ◽  
Fernando Maestu ◽  
Ernesto Pereda
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Functional Connectivity ◽  
Resting State ◽  
External Validation ◽  
Clinical Importance ◽  
Switching Behavior ◽  
Phase Lag ◽  
Dynamic Functional Connectivity ◽  
Frequency Coupling

AbstractIt is evident the need for designing and validating novel biomarkers for the detection of mild cognitive impairment (MCI). MCI patients have a high risk of developing Alzheimer’s disease (AD), and for that reason the introduction of novel and reliable biomarkers is of significant clinical importance. Motivated by recent findings about the rich information of dynamic functional connectivity graphs (DFCGs) about brain (dys)function, we introduced a novel approach of identifying MCI based on magnetoencephalographic (MEG) resting state recordings.The activity of different brain rhythms {δ, θ, α1, α2, β1, β2, γ1, γ2} was first beamformed with linear constrained minimum norm variance in the MEG data to determine ninety anatomical regions of interest (ROIs). A dynamic functional connectivity graph (DFCG) was then estimated using the imaginary part of phase lag value (iPLV) for both intra-frequency coupling (8) and also cross-frequency coupling pairs (28). We analyzed DFCG profiles of neuromagnetic resting state recordings of 18 Mild Cognitive Impairment (MCI) patients and 20 healthy controls. We followed our model of identifying the dominant intrinsic coupling mode (DICM) across MEG sources and temporal segments that further leads to the construction of an integrated DFCG (iDFCG). We then filtered statistically and topologically every snapshot of the iDFCG with data-driven approaches. Estimation of the normalized Laplacian transformation for every temporal segment of the iDFCG and the related eigenvalues created a 2D map based on the network metric time series of the eigenvalues (NMTSeigs). NMTSeigs preserves the non-stationarity of the fluctuated synchronizability of iDCFG for each subject. Employing the initial set of 20 healthy elders and 20 MCI patients, as training set, we built an overcomplete dictionary set of network microstates (nμstates). Afterward, we tested the whole procedure in an extra blind set of 20 subjects for external validation.We succeeded a high classification accuracy on the blind dataset (85 %) which further supports the proposed Markovian modeling of the evolution of brain states. The adaptation of appropriate neuroinformatic tools that combine advanced signal processing and network neuroscience tools could manipulate properly the non-stationarity of time-resolved FC patterns revealing a robust biomarker for MCI.

Dynamic Functional Connectivity of Resting-State Spinal Cord fMRI Reveals Fine-Grained Intrinsic Architecture

2020 ◽  
Author(s):  
Nawal Kinany ◽  
Elvira Pirondini ◽  
Silvestro Micera ◽  
Dimitri Van De Ville
Keyword(s):  
Spinal Cord ◽  
Functional Connectivity ◽  
Resting State ◽  
Dynamic Functional Connectivity ◽  
Fine Grained

scholarly journals Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times

2021 ◽  
Author(s):  
Yusi Chen ◽  
Qasim Bukhari ◽  
Tiger Wutu Lin ◽  
Terrence J Sejnowski
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Resting State ◽  
Structural Connectivity ◽  
Reaction Times ◽  
Resonance Imaging ◽  
Brain Areas ◽  
Human Connectome Project ◽  
Wide Range

Recordings from resting state functional magnetic resonance imaging (rs-fMRI) reflect the influence of pathways between brain areas. A wide range of methods have been proposed to measure this functional connectivity (FC), but the lack of ''ground truth'' has made it difficult to systematically validate them. Most measures of FC produce connectivity estimates that are symmetrical between brain areas. Differential covariance (dCov) is an algorithm for analyzing FC with directed graph edges. Applied to synthetic datasets, dCov-FC was more effective than covariance and partial correlation in reducing false positive connections and more accurately matching the underlying structural connectivity. When we applied dCov-FC to resting state fMRI recordings from the human connectome project (HCP) and anesthetized mice, dCov-FC accurately identified strong cortical connections from diffusion Magnetic Resonance Imaging (dMRI) in individual humans and viral tract tracing in mice. In addition, those HCP subjects whose rs-fMRI were more integrated, as assessed by a graph-theoretic measure, tended to have shorter reaction times in several behavioral tests. Thus, dCov-FC was able to identify anatomically verified connectivity that yielded measures of brain integration causally related to behavior.

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

2021 ◽  
Vol 15 ◽  
Author(s):  
Mohammad S. E. Sendi ◽  
Elaheh Zendehrouh ◽  
Charles A. Ellis ◽  
Zhijia Liang ◽  
Zening Fu ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Default Mode Network ◽  
Resting State ◽  
Symptom Severity ◽  
Transition Probability ◽  
Feature Selection Method ◽  
Cingulate Cortex ◽  
Anterior Cingulate ◽  
Dynamic Functional Connectivity ◽  
Default Mode

Background: Schizophrenia affects around 1% of the global population. Functional connectivity extracted from resting-state functional magnetic resonance imaging (rs-fMRI) has previously been used to study schizophrenia and has great potential to provide novel insights into the disorder. Some studies have shown abnormal functional connectivity in the default mode network (DMN) of individuals with schizophrenia, and more recent studies have shown abnormal dynamic functional connectivity (dFC) in individuals with schizophrenia. However, DMN dFC and the link between abnormal DMN dFC and symptom severity have not been well-characterized.Method: Resting-state fMRI data from subjects with schizophrenia (SZ) and healthy controls (HC) across two datasets were analyzed independently. We captured seven maximally independent subnodes in the DMN by applying group independent component analysis and estimated dFC between subnode time courses using a sliding window approach. A clustering method separated the dFCs into five reoccurring brain states. A feature selection method modeled the difference between SZs and HCs using the state-specific FC features. Finally, we used the transition probability of a hidden Markov model to characterize the link between symptom severity and dFC in SZ subjects.Results: We found decreases in the connectivity of the anterior cingulate cortex (ACC) and increases in the connectivity between the precuneus (PCu) and the posterior cingulate cortex (PCC) (i.e., PCu/PCC) of SZ subjects. In SZ, the transition probability from a state with weaker PCu/PCC and stronger ACC connectivity to a state with stronger PCu/PCC and weaker ACC connectivity increased with symptom severity.Conclusions: To our knowledge, this was the first study to investigate DMN dFC and its link to schizophrenia symptom severity. We identified reproducible neural states in a data-driven manner and demonstrated that the strength of connectivity within those states differed between SZs and HCs. Additionally, we identified a relationship between SZ symptom severity and the dynamics of DMN functional connectivity. We validated our results across two datasets. These results support the potential of dFC for use as a biomarker of schizophrenia and shed new light upon the relationship between schizophrenia and DMN dynamics.

scholarly journals Dynamic Functional Connectivity of Resting-State Spinal Cord fMRI Reveals Fine-Grained Intrinsic Architecture

Neuron ◽  
2020 ◽  
Vol 108 (3) ◽  
pp. 424-435.e4 ◽  
Author(s):  
Nawal Kinany ◽  
Elvira Pirondini ◽  
Silvestro Micera ◽  
Dimitri Van De Ville
Keyword(s):  
Spinal Cord ◽  
Functional Connectivity ◽  
Resting State ◽  
Dynamic Functional Connectivity ◽  
Fine Grained
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