scholarly journals Altered dynamic functional network connectivity in levodopa-induced dyskinesia of Parkinson's disease

2020 ◽  
Author(s):  
Qianqian Si ◽  
Yongsheng Yuan ◽  
Caiting Gan ◽  
Min Wang ◽  
Lina Wang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Large Scale ◽  
Network Connectivity ◽  
Functional Network ◽  
Sensorimotor Network ◽  
Functional Network Connectivity ◽  
Strongly Connected ◽  
Window Approach ◽  
Dynamic Functional Network Connectivity

Abstract Background Traditional measures of static functional connectivity may not completely reflect the dynamic neural activity of levodopa-induced dyskinesia (LID) in Parkinson's disease (PD). This study was aimed to investigate the dynamic changes of large-scale functional network connectivity in the temporal domain in PD patients with and without LID. Methods Using dynamic functional network connectivity (dFNC) analysis, we evaluated 41 PD patients with LID (LID group) and 34 PD patients without LID (No-LID group), on and off their levodopa medications. Group spatial independent component analysis, sliding-window approach and k-means clusters were employed. Results In OFF phase, we found no differences between PD subgroups in temporal properties. In ON phase, compared than No-LID group, LID group occurred more frequently and dwelled longer in strongly connected State 1, characterized by strong connections between visual network (VIS) and other networks. When switching from OFF to ON phase, LID group occurred more frequently and dwelled longer in State 2 and occurred less frequently and dwelled shorter in State 3 (both states were strongly connected), while No-LID group occurred more frequently and dwelled longer in State 5 (weakly connected). Additionally, correlation analysis further demonstrated that the severity of dyskinesia was only associated with frequency of occurrence and dwell time in State 2, dominated by inferior frontal cortex in cognitive executive network (CEN), strongly connecting with sensorimotor network (SMN) and VIS. Conclusions Using dFNC analysis, we found that compared to those without LID, PD patients with LID may be involved in the superexcitation of VIS, as well as interconnections between CEN and SMN, VIS, having impact on inhibition of motor circuits. The dFNC analysis might provide new insights into the neural mechanisms of LID in PD.

scholarly journals Altered Brain Functional Network in Subtypes of Parkinson's Disease: A Dynamic Perspective

2021 ◽  
Vol 13 ◽  
Author(s):  
Junlan Zhu ◽  
Qiaoling Zeng ◽  
Qiao Shi ◽  
Jiao Li ◽  
Shuwen Dong ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Functional Connectivity ◽  
Dynamic Properties ◽  
Network Connectivity ◽  
Functional Network ◽  
Global Network ◽  
Network Efficiency ◽  
Dominant Phenotype ◽  
Window Approach

Background: Parkinson's disease (PD) is a highly heterogeneous disease, especially in the clinical characteristics and prognosis. The PD is divided into two subgroups: tremor-dominant phenotype and non-tremor-dominant phenotype. Previous studies reported abnormal changes between the two PD phenotypes by using the static functional connectivity analysis. However, the dynamic properties of brain networks between the two PD phenotypes are not yet clear. Therefore, we aimed to uncover the dynamic functional network connectivity (dFNC) between the two PD phenotypes at the subnetwork level, focusing on the temporal properties of dFNC and the variability of network efficiency.Methods: We investigated the resting-state functional MRI (fMRI) data from 29 tremor-dominant PD patients (PDTD), 25 non-tremor-dominant PD patients (PDNTD), and 20 healthy controls (HCs). Sliding window approach, k-means clustering, independent component analysis (ICA), and graph theory analysis were applied to analyze the dFNC. Furthermore, the relationship between alterations in the dynamic properties and clinical features was assessed.Results: The dFNC analyses identified four reoccurring states, one of them showing sparse connections (state I). PDTD patients stayed longer time in state I and showed increased FNC between BG and vSMN in state IV. Both PD phenotypes exhibited higher FNC between dSMN and FPN in state II and state III compared with the controls. PDNTD patients showed decreased FNC between BG and FPN but increased FNC in the bilateral FPN compared with both PDTD patients and controls. In addition, PDNTD patients exhibited greater variability in global network efficiency. Tremor scores were positively correlated with dwell time in state I along with increased FNC between BG and vSMN in state IV.Conclusions: This study explores the dFNC between the PDTD and PDNTD patients, which offers new evidence on the abnormal time-varying brain functional connectivity and their network destruction of the two PD phenotypes, and may help better understand the neural substrates underlying different types of PD.

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 Alzheimer’s Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study

2021 ◽  
Vol 14 ◽  
Author(s):  
Mohammad S. E. Sendi ◽  
Elaheh Zendehrouh ◽  
Robyn L. Miller ◽  
Zening Fu ◽  
Yuhui Du ◽  
...  
Keyword(s):  
Default Mode Network ◽  
Early Stage ◽  
Network Connectivity ◽  
Functional Network ◽  
Normal Brain ◽  
Whole Brain ◽  
Mild Dementia ◽  
Sensorimotor Network ◽  
Functional Network Connectivity ◽  
The Brain

BackgroundAlzheimer’s disease (AD) is the most common age-related problem and progresses in different stages, including mild cognitive impairment (early stage), mild dementia (middle-stage), and severe dementia (late-stage). Recent studies showed changes in functional network connectivity obtained from resting-state functional magnetic resonance imaging (rs-fMRI) during the transition from healthy aging to AD. By assuming that the brain interaction is static during the scanning time, most prior studies are focused on static functional or functional network connectivity (sFNC). Dynamic functional network connectivity (dFNC) explores temporal patterns of functional connectivity and provides additional information to its static counterpart.MethodWe used longitudinal rs-fMRI from 1385 scans (from 910 subjects) at different stages of AD (from normal to very mild AD or vmAD). We used group-independent component analysis (group-ICA) and extracted 53 maximally independent components (ICs) for the whole brain. Next, we used a sliding-window approach to estimate dFNC from the extracted 53 ICs, then group them into 3 different brain states using a clustering method. Then, we estimated a hidden Markov model (HMM) and the occupancy rate (OCR) for each subject. Finally, we investigated the link between the clinical rate of each subject with state-specific FNC, OCR, and HMM.ResultsAll states showed significant disruption during progression normal brain to vmAD one. Specifically, we found that subcortical network, auditory network, visual network, sensorimotor network, and cerebellar network connectivity decrease in vmAD compared with those of a healthy brain. We also found reorganized patterns (i.e., both increases and decreases) in the cognitive control network and default mode network connectivity by progression from normal to mild dementia. Similarly, we found a reorganized pattern of between-network connectivity when the brain transits from normal to mild dementia. However, the connectivity between visual and sensorimotor network connectivity decreases in vmAD compared with that of a healthy brain. Finally, we found a normal brain spends more time in a state with higher connectivity between visual and sensorimotor networks.ConclusionOur results showed the temporal and spatial pattern of whole-brain FNC differentiates AD form healthy control and suggested substantial disruptions across multiple dynamic states. In more detail, our results suggested that the sensory network is affected more than other brain network, and default mode network is one of the last brain networks get affected by AD In addition, abnormal patterns of whole-brain dFNC were identified in the early stage of AD, and some abnormalities were correlated with the clinical score.

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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