Brain network localization of anhedonia

Translational Psychiatry ARTICLE www.nature.com/tp OPEN Brain network localization of anhedonia Chenglong Liu 1,2,3,4 , Yu Song 1,2,3,4 , Xufeng Zhao1,2,3, Zhili Wang1,2,3, Liping Yan1,2,3, Yongqiang Yu 1,2,3 ✉ and Jiajia Zhu 1,2,3 ✉ 1234567890();,: © The Author(s) 2026 Anhedonia, encompassing a broad spectrum of deficits in reward processing, is highly prevalent in major depressive disorder (MDD) and constitutes one of its core symptoms. While substantial progress has recently been made in mapping neuropsychiatric symptoms to specific brain networks, focused efforts to examine network localization of anhedonia are limited. We initially synthesized extant neuroimaging literature to identify brain locations with structural or functional alterations related to anhedonia. By integrating these affected brain locations with large-scale discovery (1113 healthy individuals) and validation (1093 healthy individuals and 255 MDD patients) resting-state functional magnetic resonance imaging datasets, we then applied novel functional connectivity network mapping to construct an anhedonia network. The anhedonia network was composed of the dorsal anterior cingulate cortex, insula, lateral prefrontal cortex, and striatum, principally implicating the canonical ventral attention and subcortical networks. Further analyses revealed that the trait and state anhedonia networks preferentially involved the default and limbic networks respectively, in addition to the commonly affected ventral attention and subcortical networks. Our findings may not only advance the understanding of the neurobiology underlying anhedonia from a network perspective, but also potentially contribute to more targeted and effective intervention strategies for anhedonia. Translational Psychiatry (2026)16:214 ; https://doi.org/10.1038/s41398-026-04005-6 INTRODUCTION Anhedonia is traditionally defined as a decrease in an individual’s ability to experience pleasure or interest, which has been extended by the modern framework to encompass a broader spectrum of deficits in reward processing [1, 2]. Anhedonia is highly prevalent in major depressive disorder (MDD) and constitutes one of its core symptoms [3]. Conventional antidepressants, such as selective serotonin reuptake inhibitors, have shown a limited clinical benefit on anhedonia, which is prominently associated with treatment-resistant depression [4]. Moreover, there is strong evidence that anhedonia could serve as an independent risk factor for suicide [5–7]. These findings suggest that anhedonia requires careful assessment and targeted treatment, highlighting the need to elucidate its neurobiology to improve therapy development. Although substantial preclinical and clinical research indicates that dysfunction in neural reward circuitry along with alterations in multiple relevant neurotransmitter systems are implicated in anhedonia [4, 8–11], a complete picture of its brain substrates is yet to be unveiled. Continuing improvements in in vivo neuroimaging techniques and analytic approaches have enabled a more precise examination of brain structure and function in health and disease [12–19]. Taking advantage of neuroimaging tools, numerous studies have documented brain structural and functional alterations linked to anhedonia, with the anterior cingulate cortex, orbitofrontal cortex, insula, striatum, thalamus, and amygdala being preferentially affected [4, 20–24]. However, the extent and nature of such changes have varied significantly across studies. The marked heterogeneity in previous results may be reconciled by an increasingly recognized notion that abnormalities in distinct brain locations that cause the same symptom can map to a common brain network [25]. Motivated by this perspective, brain localization of a neuropsychiatric symptom has recently shifted from a dominant region-based approach to an updated network-based paradigm. In this instance, a novel and well-validated functional connectivity network mapping (FCNM) approach has been developed to achieve network localization of a disease, a symptom or a psychological process, by integrating brain locations of interest (e.g., lesion, structural damage, functional abnormality, and neural activation) with large-scale functional brain connectome data [26–33]. The FCNM approach has enjoyed considerable success in mapping a variety of neurological and psychiatric symptoms to common symptom-specific brain networks [34–44]. Despite these myriad points of interest, focused efforts to examine brain network localization of anhedonia with the FCNM approach have been limited. To address this missing gap, this exploratory study initially synthesized extant neuroimaging literature to identify brain locations with structural or functional alterations related to anhedonia. By integrating these affected brain locations with large-scale discovery and validation resting-state functional magnetic resonance imaging (fMRI) datasets, we then applied the FCNM approach to construct an anhedonia network. A flowchart of the study procedure and data analysis is shown in Fig. 1. 1 Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China. 2Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China. 3Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei 230032, China. 4These authors contributed equally: Chenglong Liu, Yu Song. ✉email: ; Received: 11 December 2025 Revised: 18 February 2026 Accepted: 16 March 2026 C. Liu et al. 2 Fig. 1 Study procedure and data analysis. We initially synthesized extant neuroimaging literature to identify brain locations with structural or functional alterations related to anhedonia. By integrating these affected brain locations with large-scale discovery (AMUD) and validation (HCP and MDD) resting-state fMRI datasets, we then applied the FCNM approach to construct an anhedonia network. Specifically, spheres centered at each coordinate of a contrast were first created and merged together to generate a contrast-specific combined seed mask. Second, based on the resting-state BOLD fMRI data, we computed a contrast seed-to-whole brain rsFC map for each subject. Third, the subject-level rsFC maps were entered into a voxel-wise one-sample t test to identify brain regions functionally connected to each contrast seed. Fourth, the resulting group-level t maps were thresholded and binarized. Finally, the binarized maps were overlaid to produce a network probability map, which was thresholded at 50% to yield the anhedonia network. AMUD Anhui Medical University Dataset, BOLD bloodoxygen-level-dependent, FCNM functional connectivity network mapping, fMRI functional magnetic resonance imaging, HCP Human Connectome Project, MDD major depressive disorder, rsFC resting-state functional connectivity. MATERIALS AND METHODS Study search and selection Following the Preferred Reporting Items for Systematic Review (...truncated)