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

2019 ◽  
Vol 209 ◽  
pp. 129-134 ◽  
Author(s):  
L. Sanfratello ◽  
J.M. Houck ◽  
V.D. Calhoun
Keyword(s):  
Network Connectivity ◽  
Functional Network ◽  
Functional Network Connectivity ◽  
Global Dynamic ◽  
Dynamic Functional Network Connectivity

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 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 Spatial Connectivity and Temporal Dynamic Functional Network Connectivity of Musical Emotions Evoked by Dynamically Changing Tempo

2021 ◽  
Vol 15 ◽  
Author(s):  
Ying Liu ◽  
Weili Lian ◽  
Xingcong Zhao ◽  
Qingting Tang ◽  
Guangyuan Liu
Keyword(s):  
Time Window ◽  
Network Connectivity ◽  
Functional Network ◽  
Temporal Dynamic ◽  
Whole Brain ◽  
Strong Connectivity ◽  
Spatial Connectivity ◽  
Functional Network Connectivity ◽  
Musical Emotion ◽  
Dynamic Functional Network Connectivity

Music tempo is closely connected to listeners’ musical emotion and multifunctional neural activities. Music with increasing tempo evokes higher emotional responses and music with decreasing tempo enhances relaxation. However, the neural substrate of emotion evoked by dynamically changing tempo is still unclear. To investigate the spatial connectivity and temporal dynamic functional network connectivity (dFNC) of musical emotion evoked by dynamically changing tempo, we collected dynamic emotional ratings and conducted group independent component analysis (ICA), sliding time window correlations, and k-means clustering to assess the FNC of emotion evoked by music with decreasing tempo (180–65 bpm) and increasing tempo (60–180 bpm). Music with decreasing tempo (with more stable dynamic valences) evoked higher valence than increasing tempo both with stronger independent components (ICs) in the default mode network (DMN) and sensorimotor network (SMN). The dFNC analysis showed that with time-decreasing FNC across the whole brain, emotion evoked by decreasing music was associated with strong spatial connectivity within the DMN and SMN. Meanwhile, it was associated with strong FNC between the DMN–frontoparietal network (FPN) and DMN–cingulate-opercular network (CON). The paired t-test showed that music with a decreasing tempo evokes stronger activation of ICs within DMN and SMN than that with an increasing tempo, which indicated that faster music is more likely to enhance listeners’ emotions with multifunctional brain activities even when the tempo is slowing down. With increasing FNC across the whole brain, music with an increasing tempo was associated with strong connectivity within FPN; time-decreasing connectivity was found within CON, SMN, VIS, and between CON and SMN, which explained its unstable valence during the dynamic valence rating. Overall, the FNC can help uncover the spatial and temporal neural substrates of musical emotions evoked by dynamically changing tempi.

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