The instability of functional connectivity in patients with schizophrenia and their siblings: A dynamic connectivity study

Neuropeptide Y (NPY) is a neurotransmitter that has been implicated in the development of anxiety and mood disorders. Low levels of NPY have been associated with risk for these disorders, and high levels with resilience. Anxiety and depression are associated with altered intrinsic functional connectivity of brain networks, but the effect of NPY on functional connectivity is not known. Here, we test the hypothesis that individual differences in NPY expression affect resting functional connectivity of the default mode and salience networks. We evaluated static connectivity using graph theoretical techniques and dynamic connectivity with Leading Eigenvector Dynamics Analysis (LEiDA). To increase our power of detecting NPY effects, we genotyped 221 individuals and identified 29 healthy subjects at the extremes of genetically predicted NPY expression (12 high, 17 low). Static connectivity analysis revealed that lower levels of NPY were associated with shorter path lengths, higher global efficiency, higher clustering, higher small-worldness, and average higher node strength within the salience network, whereas subjects with high NPY expression displayed higher modularity and node eccentricity within the salience network. Dynamic connectivity analysis showed that the salience network of low-NPY subjects spent more time in a highly coordinated state relative to high-NPY subjects, and the salience network of high-NPY subjects switched between states more frequently. No group differences were found for static or dynamic connectivity of the default mode network. These findings suggest that genetically driven individual differences in NPY expression influence risk of mood and anxiety disorders by altering the intrinsic functional connectivity of the salience network.

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P.1.i.011 Effect of delta-9-tetrahydrocannabinol on the whole-brain resting state functional connectivity: a dynamic connectivity approach

European Neuropsychopharmacology ◽

10.1016/s0924-977x(15)30370-9 ◽

2015 ◽

Vol 25 ◽

pp. S305

Author(s):

Y. Zaytseva ◽

J. Horacek ◽

J. Hlinka ◽

I. Fajnerova ◽

R. Androvicova ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Resting State Functional Connectivity ◽

Whole Brain ◽

Dynamic Connectivity

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Functional connectivity in mild cognitive impairment with Lewy bodies

Journal of Neurology ◽

10.1007/s00415-021-10580-z ◽

2021 ◽

Author(s):

Julia Schumacher ◽

John-Paul Taylor ◽

Calum A. Hamilton ◽

Michael Firbank ◽

Paul C. Donaghy ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Functional Connectivity ◽

Resting State ◽

Brain Function ◽

Lewy Bodies ◽

Sliding Window ◽

Resting State Fmri ◽

Approaches To Study ◽

Dynamic Connectivity

AbstractPrevious resting-state fMRI studies in dementia with Lewy bodies have described changes in functional connectivity in networks related to cognition, motor function, and attention as well as alterations in connectivity dynamics. However, whether these changes occur early in the course of the disease and are already evident at the stage of mild cognitive impairment is not clear. We studied resting-state fMRI data from 31 patients with mild cognitive impairment with Lewy bodies compared to 28 patients with mild cognitive impairment due to Alzheimer’s disease and 24 age-matched controls. We compared the groups with respect to within- and between-network functional connectivity. Additionally, we applied two different approaches to study dynamic functional connectivity (sliding-window analysis and leading eigenvector dynamic analysis). We did not find any significant changes in the mild cognitive impairment groups compared to controls and no differences between the two mild cognitive impairment groups, using static as well as dynamic connectivity measures. While patients with mild cognitive impairment with Lewy bodies already show clear functional abnormalities on EEG measures, the fMRI analyses presented here do not appear to be sensitive enough to detect such early and subtle changes in brain function in these patients.

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Dynamic connectivity detection: an algorithm for determining functional connectivity change points in fMRI data

Frontiers in Neuroscience ◽

10.3389/fnins.2015.00285 ◽

2015 ◽

Vol 9 ◽

Cited By ~ 42

Author(s):

Yuting Xu ◽

Martin A. Lindquist

Keyword(s):

Functional Connectivity ◽

Change Points ◽

Fmri Data ◽

Dynamic Connectivity

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0143 Vigilance Declines Following Sleep Deprivation are Associated with Two Previously Identified Dynamic Connectivity States

SLEEP ◽

10.1093/sleep/zsaa056.141 ◽

2020 ◽

Vol 43 (Supplement_1) ◽

pp. A56-A57

Author(s):

J Teng ◽

J Ong ◽

A Patanaik ◽

J Zhou ◽

M Chee ◽

...

Keyword(s):

Functional Connectivity ◽

Sleep Deprivation ◽

Sustained Attention ◽

Resting State ◽

Resting State Fmri ◽

Fmri Data ◽

Dynamic Functional Connectivity ◽

Arousal State ◽

Total Sleep Deprivation ◽

Dynamic Connectivity

Abstract Introduction Dynamic functional connectivity (DFC) analysis of resting-state fMRI data has been successfully used to track fluctuations in arousal in the human brain. Changes in DFC have also been reported with acute sleep deprivation. Here, we demonstrate that dynamic connectivity states (DCS) previously related to arousal are reproducible, and are associated with individual differences in sustained attention declines after one night of total sleep deprivation. Methods 32 participants underwent two counterbalanced resting-state fMRI scans: during rested wakefulness (RW) and following total sleep deprivation (SD). They also completed the Psychomotor Vigilance Test (PVT), a sustained attention task that is highly sensitive to the effects of sleep loss. SD vulnerability was computed as the decrease in response speed (∆RS) and increase in lapses (∆lapse) in SD compared with RW. Dynamic functional connectivity analysis was conducted on rs-fMRI data. Connectivity matrices were clustered to obtain 5 prototypical DCS. We calculated the proportion of time participants spent in each of these DCS, as well as how often participants transitioned between DCSs. Relationships between SD vulnerability and connectivity metrics were then correlated. Results We recovered two DCS that were highly similar (ρ = .89-.91) to arousal-related DCS observed in previous work (high arousal state (HAS); low arousal state (LAS)). After sleep deprivation, the proportion of time spent in the LAS increased significantly (t29=3.16, p=.0039), while there was no significant change in HAS (t29=-1.43, p=.16). We observed significantly more state transitions in RW compared with SD. Change in LAS and HAS across sleep conditions correlated significantly with SD vulnerability (ΔLASxΔRS: r=-0.64, p<.0001; ΔLASxΔlapse: r=0.43, p=.018; ΔHASxΔRS; r=0.43, p=.019; ΔHASxΔlapse; r=-0.39, p=.033). Finally, Δ%transitions was correlated with ΔRS but not Δlapse. Conclusion This study adds to the evidence that two specific reproducible DCS are robust markers of arousal and attention, and may be useful indicators of SD vulnerability. Support This work was supported by the National Medical Research Council, Singapore (STaR/0015/2013), and the National Research Foundation Science of Learning (NRF2016-SOL002-001).

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Dynamic functional connectivity changes in dementia with Lewy bodies and Alzheimer’s disease

10.1101/374538 ◽

2018 ◽

Author(s):

Julia Schumacher ◽

Luis R. Peraza ◽

Michael Firbank ◽

Alan J. Thomas ◽

Marcus Kaiser ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Functional Connectivity ◽

Resting State ◽

Dementia With Lewy Bodies ◽

Lewy Bodies ◽

Brain Network ◽

Visual Hallucinations ◽

Dynamic Functional Connectivity ◽

Dynamic Connectivity ◽

Cognitive Fluctuations

AbstractWe studied the dynamic functional connectivity profile of dementia with Lewy bodies (DLB) and Alzheimer’s disease (AD) and the relationship between dynamic connectivity and the temporally transient symptoms of cognitive fluctuations and visual hallucinations in DLB.Resting state fMRI data from 31 DLB, 29 AD, and 31 healthy control participants were analysed using dual regression to determine between-network functional connectivity. We used a sliding window approach followed by k-means clustering and dynamic network analyses to study dynamic functional connectivity changes associated with AD and DLB. Network measures that showed significant group differences were tested for correlations with clinical symptom severity.AD and DLB patients spent more time than controls in sparse connectivity configurations with absence of strong positive and negative connections and a relative isolation of motor networks from other networks. Additionally, DLB patients spent less time in a more strongly connected state and the variability of global brain network efficiency was reduced in DLB compared to controls. However, there were no significant correlations between dynamic connectivity measures and clinical scores.The loss of global efficiency variability in DLB might indicate the presence of an abnormally rigid brain network and the lack of economical dynamics, factors which could contribute to an inability to respond appropriately to situational demands. However, the absence of significant clinical correlations indicates that the severity of transient cognitive symptoms such as cognitive fluctuations and visual hallucinations might not be directly related to these dynamic connectivity changes observed during a short resting state scan.

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Unsupervised clustering of track-weighted dynamic functional connectivity reveals white matter substrates of functional connectivity dynamics

10.1101/2021.12.04.471233 ◽

2021 ◽

Author(s):

Gianpaolo Antonio Basile ◽

Salvatore Bertino ◽

Victor Nozais ◽

Alessia Bramanti ◽

Rosella Ciurleo ◽

...

Keyword(s):

White Matter ◽

Functional Connectivity ◽

Resting State ◽

Time Course ◽

Brain Connectivity ◽

Structural Connectivity ◽

Resting State Fmri ◽

Dynamic Changes ◽

Dynamic Functional Connectivity ◽

Dynamic Connectivity

AbstractThe contribution of structural connectivity to functional connectivity dynamics is still far from being fully elucidated. Herein, we applied track-weighted dynamic functional connectivity (tw-dFC), a model integrating structural, functional, and dynamic connectivity, on high quality diffusion weighted imaging and resting-state fMRI data from two independent repositories. The tw-dFC maps were analyzed using independent component analysis, aiming at identifying spatially independent white matter components which support dynamic changes in functional connectivity. Each component consisted of a spatial map of white matter bundles that show consistent fluctuations in functional connectivity at their endpoints, and a time course representative of such functional activity. These components show high intra-subject, inter-subject, and inter-cohort reproducibility. We provided also converging evidence that functional information about white matter activity derived by this method can capture biologically meaningful features of brain connectivity organization, as well as predict higher-order cognitive performance.

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Forecasting Seasonal Habitat Connectivity in a Developing Landscape

Land ◽

10.3390/land9070233 ◽

2020 ◽

Vol 9 (7) ◽

pp. 233

Author(s):

Katherine Zeller ◽

David Wattles ◽

Javan Bauder ◽

Stephen DeStefano

Keyword(s):

Functional Connectivity ◽

Habitat Connectivity ◽

Black Bears ◽

Movement Model ◽

Management Plans ◽

Education Campaigns ◽

Four Levels ◽

Human Wildlife Conflict ◽

Dynamic Connectivity

Connectivity and wildlife corridors are often key components to successful conservation and management plans. Connectivity for wildlife is typically modeled in a static environment that reflects a single snapshot in time. However, it has been shown that, when compared with dynamic connectivity models, static models can underestimate connectivity and mask important population processes. Therefore, including dynamism in connectivity models is important if the goal is to predict functional connectivity. We incorporated four levels of dynamism (individual, daily, seasonal, and interannual) into an individual-based movement model for black bears (Ursus americanus) in Massachusetts, USA. We used future development projections to model movement into the year 2050. We summarized habitat connectivity over the 32-year simulation period as the number of simulated movement paths crossing each pixel in our study area. Our results predict black bears will further colonize the expanding part of their range in the state and move beyond this range towards the greater Boston metropolitan area. This information is useful to managers for predicting and addressing human–wildlife conflict and in targeting public education campaigns on bear awareness. Including dynamism in connectivity models can produce more realistic models and, when future projections are incorporated, can ensure the identification of areas that offer long-term functional connectivity for wildlife.

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