Evaluating state-based network dynamics in anhedonia

Anhedonia is a transdiagnostic clinical syndrome associated with significant clinical impairment. In spite of this, a clear network-level characterization of anhedonia does not yet exist. The present study addressed this gap in the literature by taking a graph theoretical approach to characterizing...

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Main Authors: Angela Pisoni, Jeffrey Browndyke, Simon W. Davis, Moria Smoski
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Neuroimage: Reports
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Online Access:http://www.sciencedirect.com/science/article/pii/S266695602400031X
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author Angela Pisoni
Jeffrey Browndyke
Simon W. Davis
Moria Smoski
author_facet Angela Pisoni
Jeffrey Browndyke
Simon W. Davis
Moria Smoski
author_sort Angela Pisoni
collection DOAJ
description Anhedonia is a transdiagnostic clinical syndrome associated with significant clinical impairment. In spite of this, a clear network-level characterization of anhedonia does not yet exist. The present study addressed this gap in the literature by taking a graph theoretical approach to characterizing state-based (i.e., reward anticipation, rest) network dynamics in a transdiagnostic sample of adults with clinically significant anhedonia (n = 77). Analyses focused on three canonical brain networks: the Salience Network (SN), the Default Mode Network (DMN) and the Central Executive Network (CEN), with hypotheses focusing on the role of saliency-mapping in anhedonia. Contrary to hypotheses, no significant relation was found between the SN and anhedonia symptom severity. Exploratory results revealed a significant association between anhedonia severity and DMN reorganization from rest to reward anticipation. Specifically, greater anhedonia severity was associated with less reward-related reorganization. This finding suggests that anhedonia severity may be associated with DMN hyposensitivity, such that individuals with more severe anhedonia may have a difficult time disengaging from their internal world in the context of potentially rewarding experiences. Although preliminary, this finding challenges the centrality of the SN in anhedonia severity and suggests the importance of the DMN. Clinical implications and future directions are explored.
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spelling doaj-art-9a1b8d90ae2743e0bac6a5efa5198cf42024-12-12T05:23:51ZengElsevierNeuroimage: Reports2666-95602024-12-0144100225Evaluating state-based network dynamics in anhedoniaAngela Pisoni0Jeffrey Browndyke1Simon W. Davis2Moria Smoski3Department of Psychology and Neuroscience, Duke University, Durham, NC, USA; Durham Veterans Affairs Health Care System, Durham, NC, USA; Corresponding author. DUMC Box 102505, Durham, NC, 27710, USA.Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA; Duke Institute for Brain Sciences, Duke University, Durham, NC, USA; Durham Veterans Affairs Health Care System, Durham, NC, USADepartment of Psychology and Neuroscience, Duke University, Durham, NC, USA; Department of Neurology, Duke University Medical Center, Durham, NC, USADepartment of Psychology and Neuroscience, Duke University, Durham, NC, USA; Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USAAnhedonia is a transdiagnostic clinical syndrome associated with significant clinical impairment. In spite of this, a clear network-level characterization of anhedonia does not yet exist. The present study addressed this gap in the literature by taking a graph theoretical approach to characterizing state-based (i.e., reward anticipation, rest) network dynamics in a transdiagnostic sample of adults with clinically significant anhedonia (n = 77). Analyses focused on three canonical brain networks: the Salience Network (SN), the Default Mode Network (DMN) and the Central Executive Network (CEN), with hypotheses focusing on the role of saliency-mapping in anhedonia. Contrary to hypotheses, no significant relation was found between the SN and anhedonia symptom severity. Exploratory results revealed a significant association between anhedonia severity and DMN reorganization from rest to reward anticipation. Specifically, greater anhedonia severity was associated with less reward-related reorganization. This finding suggests that anhedonia severity may be associated with DMN hyposensitivity, such that individuals with more severe anhedonia may have a difficult time disengaging from their internal world in the context of potentially rewarding experiences. Although preliminary, this finding challenges the centrality of the SN in anhedonia severity and suggests the importance of the DMN. Clinical implications and future directions are explored.http://www.sciencedirect.com/science/article/pii/S266695602400031XAnhedoniaNetwork dynamicsDefault mode networkSalience networkRewardGraph theory
spellingShingle Angela Pisoni
Jeffrey Browndyke
Simon W. Davis
Moria Smoski
Evaluating state-based network dynamics in anhedonia
Neuroimage: Reports
Anhedonia
Network dynamics
Default mode network
Salience network
Reward
Graph theory
title Evaluating state-based network dynamics in anhedonia
title_full Evaluating state-based network dynamics in anhedonia
title_fullStr Evaluating state-based network dynamics in anhedonia
title_full_unstemmed Evaluating state-based network dynamics in anhedonia
title_short Evaluating state-based network dynamics in anhedonia
title_sort evaluating state based network dynamics in anhedonia
topic Anhedonia
Network dynamics
Default mode network
Salience network
Reward
Graph theory
url http://www.sciencedirect.com/science/article/pii/S266695602400031X
work_keys_str_mv AT angelapisoni evaluatingstatebasednetworkdynamicsinanhedonia
AT jeffreybrowndyke evaluatingstatebasednetworkdynamicsinanhedonia
AT simonwdavis evaluatingstatebasednetworkdynamicsinanhedonia
AT moriasmoski evaluatingstatebasednetworkdynamicsinanhedonia