Dataset Information

Time-Varying Functional Connectivity Decreases as a Function of Acute Nicotine Abstinence.

ABSTRACT:

Background

The nicotine withdrawal syndrome (NWS) includes affective and cognitive disruptions whose incidence and severity vary across time during acute abstinence. However, most network-level neuroimaging uses static measures of resting-state functional connectivity and assumes time-invariance and is thus unable to capture dynamic brain-behavior relationships. Recent advances in resting-state functional connectivity signal processing allow characterization of time-varying functional connectivity (TVFC), which characterizes network communication between networks that reconfigure over the course of data collection. Therefore, TVFC may more fully describe network dysfunction related to the NWS.

Methods

To isolate alterations in the frequency and diversity of communication across network boundaries during acute nicotine abstinence, we scanned 25 cigarette smokers in the nicotine-sated and abstinent states and applied a previously validated method to characterize TVFC at a network and a nodal level within the brain.

Results

During abstinence, we found brain-wide decreases in the frequency of interactions between network nodes in different modular communities (i.e., temporal flexibility). In addition, within a subset of the networks examined, the variability of these interactions across community boundaries (i.e., spatiotemporal diversity) also decreased. Finally, within 2 of these networks, the decrease in spatiotemporal diversity was significantly related to NWS clinical symptoms.

Conclusions

Using multiple measures of TVFC in a within-subjects design, we characterized a novel set of changes in network communication and linked these changes to specific behavioral symptoms of the NWS. These reductions in TVFC provide a meso-scale network description of the relative inflexibility of specific large-scale brain networks during acute abstinence.

SUBMITTER: Fedota JR

PROVIDER: S-EPMC8035238 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Publications

Time-Varying Functional Connectivity Decreases as a Function of Acute Nicotine Abstinence.

Fedota John R JR Ross Thomas J TJ Castillo Juan J McKenna Michael R MR Matous Allison L AL Salmeron Betty Jo BJ Menon Vinod V Stein Elliot A EA

Biological psychiatry. Cognitive neuroscience and neuroimaging 20201019 4

<h4>Background</h4>The nicotine withdrawal syndrome (NWS) includes affective and cognitive disruptions whose incidence and severity vary across time during acute abstinence. However, most network-level neuroimaging uses static measures of resting-state functional connectivity and assumes time-invariance and is thus unable to capture dynamic brain-behavior relationships. Recent advances in resting-state functional connectivity signal processing allow characterization of time-varying functional co ...[more]

PMID: 33436331

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Project description:Whole-brain functional connectivity (FC) measured with functional MRI (fMRI) evolves over time in meaningful ways at temporal scales going from years (e.g., development) to seconds [e.g., within-scan time-varying FC (tvFC)]. Yet, our ability to explore tvFC is severely constrained by its large dimensionality (several thousands). To overcome this difficulty, researchers often seek to generate low dimensional representations (e.g., 2D and 3D scatter plots) hoping those will retain important aspects of the data (e.g., relationships to behavior and disease progression). Limited prior empirical work suggests that manifold learning techniques (MLTs)-namely those seeking to infer a low dimensional non-linear surface (i.e., the manifold) where most of the data lies-are good candidates for accomplishing this task. Here we explore this possibility in detail. First, we discuss why one should expect tvFC data to lie on a low dimensional manifold. Second, we estimate what is the intrinsic dimension (ID; i.e., minimum number of latent dimensions) of tvFC data manifolds. Third, we describe the inner workings of three state-of-the-art MLTs: Laplacian Eigenmaps (LEs), T-distributed Stochastic Neighbor Embedding (T-SNE), and Uniform Manifold Approximation and Projection (UMAP). For each method, we empirically evaluate its ability to generate neuro-biologically meaningful representations of tvFC data, as well as their robustness against hyper-parameter selection. Our results show that tvFC data has an ID that ranges between 4 and 26, and that ID varies significantly between rest and task states. We also show how all three methods can effectively capture subject identity and task being performed: UMAP and T-SNE can capture these two levels of detail concurrently, but LE could only capture one at a time. We observed substantial variability in embedding quality across MLTs, and within-MLT as a function of hyper-parameter selection. To help alleviate this issue, we provide heuristics that can inform future studies. Finally, we also demonstrate the importance of feature normalization when combining data across subjects and the role that temporal autocorrelation plays in the application of MLTs to tvFC data. Overall, we conclude that while MLTs can be useful to generate summary views of labeled tvFC data, their application to unlabeled data such as resting-state remains challenging.

Dataset Information

Time-Varying Functional Connectivity Decreases as a Function of Acute Nicotine Abstinence.

Background

Methods

Results

Conclusions

Publications

Time-Varying Functional Connectivity Decreases as a Function of Acute Nicotine Abstinence.

Similar Datasets

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