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Brainstem glucose metabolism predicts reward dependence scores in treatment-resistant major depression

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Abstract
Background It has been suggested that individual differences in temperament could be involved in the (non-)response to antidepressant (AD) treatment. However, how neurobiological processes such as brain glucose metabolism may relate to personality features in the treatment-resistant depressed (TRD) state remains largely unclear. Methods To examine how brainstem metabolism in the TRD state may predict Cloninger's temperament dimensions Harm Avoidance (HA), Novelty Seeking (NS), and Reward Dependence (RD), we collected (18)fluorodeoxyglucose positron emission tomography ((18)FDG PET) scans in 40 AD-free TRD patients. All participants were assessed with the Temperament and Character Inventory (TCI). We applied a multiple kernel learning (MKL) regression to predict the HA, NS, and RD from brainstem metabolic activity, the origin of respectively serotonergic, dopaminergic, and noradrenergic neurotransmitter (NT) systems. Results The MKL model was able to significantly predict RD but not HA and NS from the brainstem metabolic activity. The MKL pattern regression model identified increased metabolic activity in the pontine nuclei and locus coeruleus, the medial reticular formation, the dorsal/median raphe, and the ventral tegmental area that contributed to the predictions of RD. Conclusions The MKL algorithm identified a likely metabolic marker in the brainstem for RD in major depression. Although (18)FDG PET does not investigate specific NT systems, the predictive value of brainstem glucose metabolism on RD scores however indicates that this temperament dimension in the TRD state could be mediated by different monoaminergic systems, all involved in higher order reward-related behavior.
Keywords
Applied Psychology, Psychiatry and Mental health, (18)FDG PET, depression, reward dependence, TCI, treatment resistance, CHARACTER INVENTORY, PERSONALITY-TRAIT, MOOD DISORDERS, HF-RTMS, TEMPERAMENT, DIMENSIONS, SYSTEM, CORTEX, STATE, CONNECTIVITY

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MLA
Wu, Guo-Rong, and Chris Baeken. “Brainstem Glucose Metabolism Predicts Reward Dependence Scores in Treatment-Resistant Major Depression.” PSYCHOLOGICAL MEDICINE, 2022, doi:10.1017/s0033291720005425.
APA
Wu, G.-R., & Baeken, C. (2022). Brainstem glucose metabolism predicts reward dependence scores in treatment-resistant major depression. PSYCHOLOGICAL MEDICINE. https://doi.org/10.1017/s0033291720005425
Chicago author-date
Wu, Guo-Rong, and Chris Baeken. 2022. “Brainstem Glucose Metabolism Predicts Reward Dependence Scores in Treatment-Resistant Major Depression.” PSYCHOLOGICAL MEDICINE. https://doi.org/10.1017/s0033291720005425.
Chicago author-date (all authors)
Wu, Guo-Rong, and Chris Baeken. 2022. “Brainstem Glucose Metabolism Predicts Reward Dependence Scores in Treatment-Resistant Major Depression.” PSYCHOLOGICAL MEDICINE. doi:10.1017/s0033291720005425.
Vancouver
1.
Wu G-R, Baeken C. Brainstem glucose metabolism predicts reward dependence scores in treatment-resistant major depression. PSYCHOLOGICAL MEDICINE. 2022;
IEEE
[1]
G.-R. Wu and C. Baeken, “Brainstem glucose metabolism predicts reward dependence scores in treatment-resistant major depression,” PSYCHOLOGICAL MEDICINE, 2022.
@article{8696162,
  abstract     = {{Background It has been suggested that individual differences in temperament could be involved in the (non-)response to antidepressant (AD) treatment. However, how neurobiological processes such as brain glucose metabolism may relate to personality features in the treatment-resistant depressed (TRD) state remains largely unclear. Methods To examine how brainstem metabolism in the TRD state may predict Cloninger's temperament dimensions Harm Avoidance (HA), Novelty Seeking (NS), and Reward Dependence (RD), we collected (18)fluorodeoxyglucose positron emission tomography ((18)FDG PET) scans in 40 AD-free TRD patients. All participants were assessed with the Temperament and Character Inventory (TCI). We applied a multiple kernel learning (MKL) regression to predict the HA, NS, and RD from brainstem metabolic activity, the origin of respectively serotonergic, dopaminergic, and noradrenergic neurotransmitter (NT) systems. Results The MKL model was able to significantly predict RD but not HA and NS from the brainstem metabolic activity. The MKL pattern regression model identified increased metabolic activity in the pontine nuclei and locus coeruleus, the medial reticular formation, the dorsal/median raphe, and the ventral tegmental area that contributed to the predictions of RD. Conclusions The MKL algorithm identified a likely metabolic marker in the brainstem for RD in major depression. Although (18)FDG PET does not investigate specific NT systems, the predictive value of brainstem glucose metabolism on RD scores however indicates that this temperament dimension in the TRD state could be mediated by different monoaminergic systems, all involved in higher order reward-related behavior.}},
  author       = {{Wu, Guo-Rong and Baeken, Chris}},
  issn         = {{0033-2917}},
  journal      = {{PSYCHOLOGICAL MEDICINE}},
  keywords     = {{Applied Psychology,Psychiatry and Mental health,(18)FDG PET,depression,reward dependence,TCI,treatment resistance,CHARACTER INVENTORY,PERSONALITY-TRAIT,MOOD DISORDERS,HF-RTMS,TEMPERAMENT,DIMENSIONS,SYSTEM,CORTEX,STATE,CONNECTIVITY}},
  language     = {{eng}},
  pages        = {{7}},
  title        = {{Brainstem glucose metabolism predicts reward dependence scores in treatment-resistant major depression}},
  url          = {{http://dx.doi.org/10.1017/s0033291720005425}},
  year         = {{2022}},
}

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