Dataset Information

Relationship between MEG global dynamic functional network connectivity measures and symptoms in schizophrenia.

ABSTRACT: An investigation of differences in dynamic functional network connectivity (dFNC) of healthy controls (HC) versus that of schizophrenia patients (SP) was completed, using eyes-open resting state MEG data. The MEG analysis utilized a source-space activity estimate (MNE/dSPM) whose result was the input to a group spatial independent component analysis (ICA), on which the networks of our MEG dFNC analysis were based. We have previously reported that our MEG dFNC revealed that SP change between brain meta-states (repeating patterns of network correlations which are allowed to overlap in time) significantly more often and to states which are more different, relative to HC. Here, we extend our previous work to investigate the relationship between symptomology in SP and four meta-state metrics. We found a significant correlation between positive symptoms and the two meta-state metrics which showed significant differences between HC and SP. These two statistics quantified 1) how often individuals change state and 2) the total distance traveled within the state-space. We additionally found that a clustering of the meta-state metrics divides SP into groups which vary in symptomology. These results indicate specific relationships between symptomology and brain function for SP.

SUBMITTER: Sanfratello L

PROVIDER: S-EPMC6661190 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Relationship between MEG global dynamic functional network connectivity measures and symptoms in schizophrenia.

Sanfratello L L Houck J M JM Calhoun V D VD

Schizophrenia research 20190524

An investigation of differences in dynamic functional network connectivity (dFNC) of healthy controls (HC) versus that of schizophrenia patients (SP) was completed, using eyes-open resting state MEG data. The MEG analysis utilized a source-space activity estimate (MNE/dSPM) whose result was the input to a group spatial independent component analysis (ICA), on which the networks of our MEG dFNC analysis were based. We have previously reported that our MEG dFNC revealed that SP change between brai ...[more]

PMID: 31130399

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Project description:Aim: In this work, we propose the novel use of adaptively constrained independent vector analysis (acIVA) to effectively capture the temporal and spatial properties of dynamic blood-oxygen-level-dependent (BOLD) activity (dBA), and we efficiently quantify the spatial property of dBA (sdBA). We also propose to incorporate dBA into the study of brain dynamics to gain insight into activity-connectivity co-evolution patterns. Introduction: Studies of the dynamics of the human brain using functional magnetic resonance imaging (fMRI) have enabled the identification of unique functional network connectivity (FNC) states and provided new insights into mental disorders. There is evidence showing that both BOLD activity, which is captured by fMRI, and FNC are related to mental and cognitive processes. However, a few studies have evaluated the inter-relationships of these two domains of function. Moreover, the identification of subgroups of schizophrenia has gained significant clinical importance due to a need to study the heterogeneity of schizophrenia. Methods: We design a simulation study to verify the effectiveness of acIVA and apply acIVA to the dynamic study of resting-state fMRI data collected from individuals with schizophrenia and healthy controls (HCs) to investigate the relationship between dBA and dynamic FNC (dFNC). Results: The simulation study demonstrates that acIVA accurately captures the spatial variability and provides an efficient quantification of sdBA. The fMRI analysis yields synchronized sdBA-temporal property of dBA (tdBA) patterns and shows that the dBA and dFNC are significantly correlated in the spatial domain. Using these dynamic features, we identify schizophrenia subgroups with significant differences in terms of their clinical symptoms. Conclusion: We find that brain function is abnormally organized in schizophrenia compared with HCs since there are less synchronized sdBA-tdBA patterns in schizophrenia and schizophrenia prefers a component that merges multiple brain regions. Identification of schizophrenia subgroups using dynamic features inspires the use of neuroimaging in studying the heterogeneity of disorders. Impact statement This work introduces the use of joint blind source separation for the study of brain dynamics to enable efficient quantification of the spatial property of dynamic blood-oxygen-level-dependent (BOLD) activity to provide insight into the relationship of dynamic BOLD activity and dynamic functional network connectivity. The identification of subgroups of schizophrenia using dynamic features allows the study of heterogeneity of schizophrenia, emphasizing the importance of functional magnetic resonance imaging analysis in the study of brain activity and functional connectivity to gain a better understanding of the human brain, especially the brain with a mental disorder.

Project description:The human brain is a highly dynamic system with non-stationary neural activity and rapidly-changing neural interaction. Resting-state dynamic functional connectivity (dFC) has been widely studied during recent years, and the emerging aberrant dFC patterns have been identified as important features of many mental disorders such as schizophrenia (SZ). However, only focusing on the time-varying patterns in FC is not enough, since the local neural activity itself (in contrast to the inter-connectivity) is also found to be highly fluctuating from research using high-temporal-resolution imaging techniques. Exploring the time-varying patterns in brain activity and their relationships with time-varying brain connectivity is important for advancing our understanding of the co-evolutionary property of brain network and the underlying mechanism of brain dynamics. In this study, we introduced a framework for characterizing time-varying brain activity and exploring its associations with time-varying brain connectivity, and applied this framework to a resting-state fMRI dataset including 151 SZ patients and 163 age- and gender matched healthy controls (HCs). In this framework, 48 brain regions were first identified as intrinsic connectivity networks (ICNs) using group independent component analysis (GICA). A sliding window approach was then adopted for the estimation of dynamic amplitude of low-frequency fluctuation (dALFF) and dFC, which were used to measure time-varying brain activity and time-varying brain connectivity respectively. The dALFF was further clustered into six reoccurring states by the k-means clustering method and the group difference in occurrences of dALFF states was explored. Lastly, correlation coefficients between dALFF and dFC were calculated and the group difference in these dALFF-dFC correlations was explored. Our results suggested that 1) ALFF of brain regions was highly fluctuating during the resting-state and such dynamic patterns are altered in SZ, 2) dALFF and dFC were correlated in time and their correlations are altered in SZ. The overall results support and expand prior work on abnormalities of brain activity, static FC (sFC) and dFC in SZ, and provide new evidence on aberrant time-varying brain activity and its associations with brain connectivity in SZ, which might underscore the disrupted brain cognitive functions in this mental disorder.

Project description:Despite advances in the field of dynamic connectivity, fixed sliding window approaches for the detection of fluctuations in functional connectivity are still widely used. The use of conventional connectivity metrics in conjunction with a fixed sliding window comes with the arbitrariness of the chosen window lengths. In this paper we use multivariate autoregressive and neural mass models with a priori defined ground truths to systematically analyze the sensitivity of conventional metrics in combination with different window lengths to detect genuine fluctuations in connectivity for various underlying state durations. Metrics of interest are the coherence, imaginary coherence, phase lag index, phase locking value and the amplitude envelope correlation. We performed analysis for two nodes and at the network level. We demonstrate that these metrics show indeed higher variability for genuine temporal fluctuations in connectivity compared to a static connectivity state superimposed by noise. Overall, the error of the connectivity estimates themselves decreases for longer state durations (order of seconds), while correlations of the connectivity fluctuations with the ground truth was higher for longer state durations. In general, metrics, in combination with a sliding window, perform poorly for very short state durations. Increasing the SNR of the system only leads to a moderate improvement. In addition, at the network level, only longer window widths were sufficient to detect plausible resting state networks that matched the underlying ground truth, especially for the phase locking value, amplitude envelope correlation and coherence. The length of these longer window widths did not necessarily correspond to the underlying state durations. For short window widths resting state network connectivity patterns could not be retrieved. We conclude that fixed sliding window approaches for connectivity can detect modulations of connectivity, but mostly if the underlying dynamics operate on moderate to slow timescales. In practice, this can be a drawback, as state durations can vary significantly in empirical data.

Project description: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.

Dataset Information

Relationship between MEG global dynamic functional network connectivity measures and symptoms in schizophrenia.

Publications

Relationship between MEG global dynamic functional network connectivity measures and symptoms in schizophrenia.

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