Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
[Preprint]. 2023 Oct 3:2023.10.03.23296454.
doi: 10.1101/2023.10.03.23296454.

Network state dynamics underpin craving in a transdiagnostic population

Jean Ye  1 , Kathleen A Garrison  2 , Cheryl Lacadie  3 , Marc N Potenza  1   2   4   5   6   7   8 , Rajita Sinha  2   4   5 , Elizabeth V Goldfarb  1   2   8   9   10 , Dustin Scheinost  1   3   4   8   11
Affiliations

Network state dynamics underpin craving in a transdiagnostic population

Jean Ye et al. medRxiv. .

Update in

Abstract

Emerging fMRI brain dynamic methods present a unique opportunity to capture how brain region interactions across time give rise to evolving affective and motivational states. As the unfolding experience and regulation of affective states affect psychopathology and well-being, it is important to elucidate their underlying time-varying brain responses. Here, we developed a novel framework to identify network states specific to an affective state of interest and examine how their instantaneous engagement contributed to its experience. This framework investigated network state dynamics underlying craving, a clinically meaningful and changeable state. In a transdiagnostic sample of healthy controls and individuals diagnosed with or at risk for craving-related disorders (N=252), we utilized connectome-based predictive modeling (CPM) to identify craving-predictive edges. An edge-centric timeseries approach was leveraged to quantify the instantaneous engagement of the craving-positive and craving-negative networks during independent scan runs. Individuals with higher craving persisted longer in a craving-positive network state while dwelling less in a craving-negative network state. We replicated the latter results externally in an independent group of healthy controls and individuals with alcohol use disorder exposed to different stimuli during the scan (N=173). The associations between craving and network state dynamics can still be consistently observed even when craving-predictive edges were instead identified in the replication dataset. These robust findings suggest that variations in craving-specific network state recruitment underpin individual differences in craving. Our framework additionally presents a new avenue to explore how the moment-to-moment engagement of behaviorally meaningful network states supports our changing affective experiences.

PubMed Disclaimer

Conflict of interest statement

Disclosures: The authors report no conflicts of interest with respect to the content of this manuscript. Dr. Potenza discloses that he has consulted for and advised Game Day Data, Addiction Policy Forum, AXA, Idorsia, Baria-Tek, and Opiant Therapeutics; been involved in a patent application with Yale University and Novartis; received research support from the Mohegan Sun Casino and the Connecticut Council on Problem Gambling; consulted for or advised legal and gambling entities on issues related to impulse control and addictive behaviors; provided clinical care related to impulse-control and addictive behaviors; performed grant reviews; edited journals/journal sections; given academic lectures in grand rounds, CME events, and other clinical/scientific venues; and generated books or chapters for publishers of mental health texts. Dr. Sinha discloses her consultation with Embera Neurotherapeutics and also receiving research materials and support from Aelis Farma, CT Pharma and Aptinyx Inc.

Figures

Figure 1.
Figure 1.. Datasets and analysis schematic.
A) illustrates the two datasets analyzed in this study (imagery dataset in purple and visual stimuli dataset in green). Self-reported craving was collected at the end of each baseline or task run, as indicated by the gray diamonds. B) represents the analysis pipeline. Craving network was identified using the baseline condition of either the imagery or the visual stimuli dataset. Network state dynamics were examined in the task condition. For both primary and validation analysis, we first investigated brain dynamics in the task condition from the same dataset (main analysis) before extending the network to an independent dataset (external analysis).
Figure 2.
Figure 2.. Brain dynamic measures.
A) Methods overview. We first used CPM to identify a craving network and separated it into a positive and negative subnetwork. Edge timeseries tracked the moment-to-moment engagement of these two subnetworks. We additionally created a state timeseries by taking the difference between the positive and negative subnetworks. B) An example state timeseries (PND = red, NND = blue). C) Annotation of brain dynamic measures extracted from this state timeseries, including PND time, PND dwell time, PND state amplitude, NND dwell time, and NND state amplitude.
Figure 3.
Figure 3.. Primary Analysis.
A) Imagery craving network predicted craving correlated significantly with self-reported craving. B) showed the node degrees (i.e., the number of edges each brain region contributed to the craving network) of the positive and negative subnetworks. C) During imagery, network state dynamics correlated with craving. Notably, some of the results were replicated during external validation in the visual stimuli dataset using the same imagery craving network D). Baselineimg, baseline condition from the imagery dataset; Taskimg, task condition from the imagery dataset; Baselinevis, baseline condition from the visual stimuli dataset; Taskvis, task condition from the visual stimuli dataset.
Figure 4.
Figure 4.. Validation analysis.
A) Craving predicted by the visual stimuli craving network correlated significantly with self-reported craving. B) shows the node degree of the positive and negative sub-networks. C) During the task condition from the visual stimuli dataset, network state dynamics were associated with craving. When the visual stimuli craving network was extended to the imagery condition, we found that individuals with higher craving also dwelled more in the NND state and demonstrated a shallower NND state D). Baselineimg, baseline condition from the imagery dataset; Taskimg, task condition from the imagery dataset; Baselinevis, baseline condition from the visual stimuli dataset; Taskvis, task condition from the visual stimuli dataset.
See this image and copyright information in PMC

References

    1. Hutchison R. M., Womelsdorf T., Allen E. A., Bandettini P. A., Calhoun V. D., Corbetta M., Della Penna S., Duyn J. H., Glover G. H., Gonzalez-Castillo J., Handwerker D. A., Keilholz S., Kiviniemi V., Leopold D. A., de Pasquale F., Sporns O., Walter M., & Chang C. (2013). Dynamic functional connectivity: Promise, issues, and interpretations. NeuroImage, 80, 360–378. - PMC - PubMed
    1. Preti M. G., Bolton T. A., & Van De Ville D. (2017). The dynamic functional connectome: State-of-the-art and perspectives. NeuroImage, 160, 41–54. - PubMed
    1. Lurie D. J., Kessler D., Bassett D. S., Betzel R. F., Breakspear M., Kheilholz S., Kucyi A., Liégeois R., Lindquist M. A., McIntosh A. R., Poldrack R. A., Shine J. M., Thompson W. H., Bielczyk N. Z., Douw L., Kraft D., Miller R. L., Muthuraman M., Pasquini L., … Calhoun V. D. (2020). Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Network Neuroscience, 4(1), 30–69. - PMC - PubMed
    1. Tobia M. J., Hayashi K., Ballard G., Gotlib I. H., & Waugh C. E. (2017). Dynamic functional connectivity and individual differences in emotions during social stress. Human Brain Mapping, 38(12), 6185–6205. - PMC - PubMed
    1. Gaviria J., Rey G., Bolton T., Delgado J., Van De Ville D., & Vuilleumier P. (2021). Brain functional connectivity dynamics at rest in the aftermath of affective and cognitive challenges. Human Brain Mapping, 42(4), 1054–1069. - PMC - PubMed

Publication types