scholarly journals A novel method for tri-clustering dynamic functional network connectivity (dFNC) identifies significant schizophrenia effects across multiple states in distinct subgroups of individuals

2020 ◽  
Author(s):  
Md Abdur Rahaman ◽  
Eswar Damaraju ◽  
Jessica A. Turner ◽  
Theo G.M. van Erp ◽  
Daniel Mathalon ◽  
...  
Keyword(s):  
Time Course ◽  
Network Connectivity ◽  
Brain Regions ◽  
Functional Network ◽  
Imaging Data ◽  
Experimental Conditions ◽  
Functional Network Connectivity ◽  
Time Space ◽  
Significant Difference ◽  
Dynamic Functional Network Connectivity

AbstractBackgroundBrain imaging data collected from individuals are highly complex with unique variation; however, such variation is typically ignored in approaches that focus on group averages or even supervised prediction. State-of-the-art methods for analyzing dynamic functional network connectivity (dFNC) subdivide the entire time course into several (possibly overlapping) connectivity states (i.e., sliding window clusters). Though, such an approach does not factor in the homogeneity of underlying data and may end up with a less meaningful subgrouping of the dataset.MethodsDynamic-N-way tri-clustering (dNTiC) incorporates a homogeneity benchmark to approximate clusters that provide a more apples-to-apples comparison between groups within analogous subsets of time-space and subjects. dNTiC sorts the dFNC states by maximizing similarity across individuals and minimizing variance among the pairs of components within a state.ResultsResulting tri-clusters show significant differences between schizophrenia (SZ) and healthy control (HC) in distinct brain regions. Compared to HC, SZ in most tri-clusters show hypoconnectivity (low positive) among subcortical, default mode, cognitive control but hyper-connectivity (high positive) between sensory networks. In tri-cluster 3, HC subjects show significantly stronger connectivity among sensory networks and anticorrelation between subcortical and sensory networks compared to SZ. Results also provide statistically significant difference in reoccurrence time between SZ and HC subjects for two distinct dFNC states.ConclusionsOutcomes emphasize the utility of the proposed method for characterizing and leveraging variance within high-dimensional data to enhance the interpretability and sensitivity of measurements in the study of a heterogeneous disorder like schizophrenia and in unconstrained experimental conditions such as resting fMRI.

scholarly journals Dynamic Functional Network Connectivity In Schizophrenia With MEG And fMRI, Do Different Time Scales Tell A Different Story?

2018 ◽  
Author(s):  
Lori Sanfratello ◽  
Jon Houck ◽  
Vince Calhoun
Keyword(s):  
Imaging Modality ◽  
Network Connectivity ◽  
Individual Variability ◽  
Functional Network ◽  
Functional Network Connectivity ◽  
Significant Difference ◽  
Eyes Open ◽  
Brain States ◽  
Global Statistics ◽  
Dynamic Functional Network Connectivity

AbstractThe importance of how brain networks function together to create brain states has become increasingly recognized. Therefore, an investigation of eyes-open resting state dynamic functional network connectivity (dFNC) of healthy controls (HC) versus that of schizophrenia patients (SP) via both fMRI and a novel MEG pipeline was completed. The fMRI analysis used a spatial independent component analysis (ICA) to determine the networks on which the dFNC was based. The MEG analysis utilized a source-space activity estimate (MNE/dSPM) whose result was the input to a spatial ICA, on which the networks of the MEG dFNC was based. We found that dFNC measures reveal significant differences between HC and SP, which depended upon the imaging modality. Consistent with previous findings, a dFNC analysis predicated on fMRI data revealed HC and SP remain in different overall brain states (defined by a k-means clustering of network correlations) for significantly different periods of time, with SP spending less time in a highly-connected state. The MEG dFNC, in contrast, revealed group differences in more global statistics: SP changed between meta-states (k-means cluster states that are allowed to overlap in time) significantly more often and to states which were more different, relative to HC. MEG dFNC also revealed a highly connected state where a significant difference was observed in inter-individual variability, with greater variability among SP. Overall, our results show that fMRI and MEG reveal between-group functional connectivity differences in distinct ways, highlighting the utility of using each of the modalities individually, or potentially a combination of modalities, to better inform our understanding of disorders such as schizophrenia.

scholarly journals The relationship between dynamic functional network connectivity and spatial orientation in healthy young adults

2021 ◽  
Author(s):  
Mohammad S. E. Sendi ◽  
Charles A. Ellis ◽  
Robyn L. Miller ◽  
David H. Salat ◽  
Vince D. Calhoun
Keyword(s):  
Young Adults ◽  
Spatial Orientation ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Brain Regions ◽  
Functional Network ◽  
Sensorimotor Network ◽  
Functional Network Connectivity ◽  
The Relationship ◽  
Dynamic Functional Network Connectivity

ABSTRACTSpatial orientation is essential to interacting with a physical environment, and better understanding it could contribute to a better understanding of a variety of diseases and disorders that are characterized by deficits in spatial orientation. Many previous studies have focused on the relationship between spatial orientation and individual brain regions, though in recent years studies have begun to examine spatial orientation from a network perspective. This study analyzes dynamic functional network connectivity (dFNC) values extracted from over 800 resting-state fMRI recordings of healthy young adults (age 22-37 years) and applies unsupervised machine learning methods to identify neural brain states that occur across all subjects. We estimated the occupancy rate (OCR) for each subject, which was proportional to the amount of time that they spent in each state, and investigated the link between the OCR and spatial orientation and the state-specific FNC values and spatial orientation controlling for age and sex. Our findings showed that the amount of time subjects spent in a state characterized by increased connectivity within and between visual, auditory, and sensorimotor networks and within the default mode network while at rest corresponded to their performance on tests of spatial orientation. We also found that increased sensorimotor network connectivity in two of the identified states negatively correlated with decreased spatial orientation, further highlighting the relationship between the sensorimotor network and spatial orientation. This study provides insight into how the temporal properties of the functional brain connectivity within and between key brain networks may influence spatial orientation.

scholarly journals S161. DYNAMIC FUNCTIONAL NETWORK CONNECTIVITY COMPARING AUDITORY VERBAL HALLUCINATIONS IN PSYCHOTIC AND NON-PSYCHOTIC SUBJECTS

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S97-S98
Author(s):  
Theresa Marschall ◽  
Branislava Curcic-Blake ◽  
Sanne Brederoo ◽  
Iris Sommer
Keyword(s):  
Brain Networks ◽  
Network Connectivity ◽  
Functional Network ◽  
Salience Network ◽  
Auditory Verbal Hallucinations ◽  
Functional Network Connectivity ◽  
Significant Difference ◽  
Time Courses ◽  
Dynamic Functional Network Connectivity ◽  
Clinical Populations

Abstract Background Auditory verbal hallucinations (AVH) are often seen as a hallmark of schizophrenia, but can also occur in the general healthy population. While AVH in non-clinical populations might offer an opportunity to study them in isolation, it remains debatable whether the mechanisms underlying AVH are the same in clinical and non-clinical populations. For example, non-clinical populations are reported to attribute lower emotional valence to their AVH. Such differences in phenomenology are hypothesized to arise from differences on the neurobiological level. With the current study, we employ a data-driven approach to define brain networks involved in AVH in clinical and non-clinical subjects, and test whether dynamic differences in network connectivity exist between these groups. Methods Functional magnetic resonance imaging data of 21 non-psychotic individuals and 21 matched psychotic patients with frequent AVH were obtained. During scanning, subjects manually indicated the on- and offset of their AVH. Using independent component (IC) analysis, the data were split into 72 statistically independent spatial maps and their time courses. These time courses were regressed with the AVH time courses. With a one sample t-test on the beta weights, we selected those ICs that related to AVH in both groups for further dynamic functional network connectivity analysis. To identify functional connectivity states, k-means clustering was implemented on correlation matrices acquired using sliding windows. Group differences between these states were determined with two-sample t-tests. Results Both groups experienced AVH during scanning, with a mean number of 24.71 AVH episodes in the clinical and 17.14 episodes in the non-clinical group. We identified seven ICs with time courses significantly related to the occurrence of AVH in both groups. The auditory, sensorimotor, and posterior salience network were positively related to AVH occurrence. The ventral default mode network (DMN), anterior salience network and a network consisting of (para-)hippocampal areas were negatively related to AVH. While in general, networks related to AVH were similar in both groups, a significant difference between the two groups was found in the mean dwell time in states characterized by varying connectivity between these networks. Psychotic patients spent more time in a state of low connectivity (r < 0.055) between all AVH-related networks. Non-psychotic patients dwelled longer in a different state, where some weak correlations between networks were present (.15 > r ≥ .10). Specifically, networks positively related to AVH showed small negative correlations with each other, and a small negative relationship with the DMN. At the same time, the anterior salience network displayed a small positive relationship with the sensorimotor, auditory and posterior salience networks. Discussion Our findings suggest that similar brain networks underlie AVH in non-psychotic and psychotic individuals, but that the groups differ in terms of connectivity between those networks. Among the involved networks are those typically associated with AVH in psychotic patients, such as the DMN and auditory network. During the experience of AVH, psychotic individuals are more likely to show a state defined by segregation of the AVH-related networks. On the contrary, during AVH non-psychotic individuals are in a state defined by more connectivity between the networks. This suggests that a distinction between clinical and non-clinical AVH may have its neurobiological basis in the extend of disruption of involved network connectivity.

scholarly journals Statelets: High dimensional predominant shapes in dynamic functional network connectivity

2020 ◽  
Author(s):  
Md Abdur Rahaman ◽  
Eswar Damaraju ◽  
Debbrata Kumar Saha ◽  
Sergey M. Plis ◽  
Vince D. Calhoun
Keyword(s):  
Time Course ◽  
Temporal Dynamics ◽  
Network Connectivity ◽  
Brain Network ◽  
Functional Network ◽  
High Dimensional ◽  
Network Organization ◽  
Functional Network Connectivity ◽  
Space Data ◽  
Dynamic Functional Network Connectivity

AbstractDynamic functional network connectivity (dFNC) analysis is a widely used approach for capturing brain activation patterns, connectivity states, and network organization. However, a typical sliding window plus clustering (SWC) approaches for analyzing dFNC continuously models the system through a fixed set of connectivity patterns or states. It assumes these patterns are span throughout the brain, but in practice, they are more spatially constrained and temporally short-lived. Thus, SWC is not designed to capture transient dynamic changes nor heterogeneity across subjects/time. Here, we adapt time series motifs to model the temporal dynamics of functional connectivity. We propose a state-space data mining approach that combines a probabilistic pattern summarization framework called ‘Statelets’ — a subset of high dimensional state-shape prototypes capturing the dynamics. We handle scale differences using the earth mover distance and utilize kernel density estimation to build a probability density profile for local motifs. We apply the framework to study dFNC collected from patients with schizophrenia (SZ) and healthy control (HC). Results demonstrate SZ subjects exhibit reduced modularity in their brain network organization relative to HC. These statelets in the HC group show more recurrence across the dFNC time-course compared to the SZ. An analysis of the consistency of the connections across time reveals significant differences within visual, sensorimotor, and default mode regions where HC subjects show higher consistency than SZ. The introduced statelet-approach also enables the handling of dynamic information in cross-modal applications to study healthy and disordered brains and multi-modal fusion within a single dataset.

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