scholarly journals Decentralized Analysis of Brain Imaging Data: Voxel-Based Morphometry and Dynamic Functional Network Connectivity

2018 ◽  
Vol 12 ◽  
Author(s):  
Harshvardhan Gazula ◽  
Bradley T. Baker ◽  
Eswar Damaraju ◽  
Sergey M. Plis ◽  
Sandeep R. Panta ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Network Connectivity ◽  
Functional Network ◽  
Imaging Data ◽  
Voxel Based Morphometry ◽  
Functional Network Connectivity ◽  
Brain Imaging Data ◽  
Dynamic Functional Network Connectivity

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 connectivity analysis reveals transiently increased segregation in patients with severe stroke

2020 ◽  
Author(s):  
Anna K. Bonkhoff ◽  
Markus D. Schirmer ◽  
Martin Bretzner ◽  
Mark Etherton ◽  
Kathleen Donahue ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Stroke ◽  
Acute Ischemic Stroke ◽  
Network Connectivity ◽  
Functional Network ◽  
Angular Gyrus ◽  
Stroke Severity ◽  
Functional Network Connectivity ◽  
Dynamic Connectivity ◽  
Dynamic Functional Network Connectivity

AbstractBackground and PurposeTo explore the whole-brain dynamic functional network connectivity patterns in acute ischemic stroke (AIS) patients and their relation to stroke severity in the short and long term.MethodsWe investigated large-scale dynamic functional network connectivity of 41 AIS patients two to five days after symptom onset. Re-occurring dynamic connectivity configurations were obtained using a sliding window approach and k-means clustering. We evaluated differences in dynamic patterns between three NIHSS-stroke severity defined groups (mildly, moderately, and severely affected patients). Furthermore, we established correlation analyses between dynamic connectivity estimates and AIS severity as well as neurological recovery within the first 90 days after stroke (DNIHSS). Finally, we built Bayesian hierarchical models to predict acute ischemic stroke severity and examine the inter-relation of dynamic connectivity and clinical measures, with an emphasis on white matter hyperintensity lesion load.ResultsWe identified three distinct dynamic connectivity configurations in the early post-acute stroke phase. More severely affected patients (NIHSS 10–21) spent significantly more time in a highly segregated dynamic connectivity configuration that was characterized by particularly strong connectivity (three-level ANOVA: p<0.05, post hoc t-tests: p<0.05, FDR-corrected for multiple comparisons). Recovery, as indexed by the realized change of the NIHSS over time, was significantly linked to the acute dynamic connectivity between bilateral intraparietal lobule and left angular gyrus (Pearson’s r = –0.68, p<0.05, FDR-corrected). Increasing dwell times, particularly those in a very segregated connectivity configuration, predicted higher acute stroke severity in our Bayesian modelling framework.ConclusionsOur findings demonstrate transiently increased segregation between multiple functional domains in case of severe AIS. Dynamic connectivity involving default mode network components significantly correlated with recovery in the first three months post-stroke.

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

2018 ◽  
Author(s):  
L. Sanfratello ◽  
J.M. Houck ◽  
V.D. Calhoun
Keyword(s):  
Brain Function ◽  
Network Connectivity ◽  
Functional Network ◽  
Positive Symptoms ◽  
Functional Network Connectivity ◽  
Global Dynamic ◽  
Eyes Open ◽  
Change State ◽  
The Relationship ◽  
Dynamic Functional Network Connectivity

AbstractAn 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 cognitive 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 statistics 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.

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