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.

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 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.

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