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

• Published in: Psychological medicine Vol. 52; no. 14; pp. 3260 - 3266
• Main Authors: Wu, Guo-Rong, Baeken, Chris
• Format: Journal Article
• Language: English
• Published: Cambridge, UK Cambridge University Press 01.10.2022
• Subjects: Antidepressants; Brain research; Brain stem; Brain Stem - diagnostic imaging; Character; Depression; Depressive Disorder, Major - diagnostic imaging; Depressive Disorder, Major - drug therapy; Dopamine receptors; Fluorodeoxyglucose F18; Gender; Glucose; Glucose metabolism; Humans; Individual differences; Locus coeruleus; Medical imaging; Mental depression; Metabolism; Neurobiology; Neuroimaging; Neurosciences; Novelty seeking; Original; Original Article; Personality; Personality Inventory; Personality tests; Personality traits; Pontine nuclei; Positron emission tomography; Refractory depression; Reinforcement; Reticular formation; Reward; Temperament; Temperament - physiology; Tomography; Treatment resistance; Ventral tegmentum; 18FDG PET; reward dependence; depression; TCI; treatment resistance
• DOI: 10.1017/S0033291720005425
• ISSN: 0033-2917; 1469-8978

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. 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 fluorodeoxyglucose positron emission tomography ( 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. 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. The MKL algorithm identified a likely metabolic marker in the brainstem for RD in major depression. Although 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.