| Literature DB >> 30367740 |
Benedetta Conio1,2, Paola Magioncalda1,2, Matteo Martino1,2, Shankar Tumati3, Laura Capobianco1,2, Andrea Escelsior1,2, Giulia Adavastro1,2, Daniel Russo1,2, Mario Amore1,2, Matilde Inglese2,4,5, Georg Northoff3,6,7,8.
Abstract
Affective temperaments have been described since the early 20th century and may play a central role in psychiatric illnesses, such as bipolar disorder (BD). However, the neuronal basis of temperament is still unclear. We investigated the relationship of temperament with neuronal variability in the resting state signal-measured by fractional standard deviation (fSD) of Blood-Oxygen-Level Dependent signal-of the different large-scale networks, that is, sensorimotor network (SMN), along with default-mode, salience and central executive networks, in standard frequency band (SFB) and its sub-frequencies slow4 and slow5, in a large sample of healthy subject (HC, n = 109), as well as in the various temperamental subgroups (i.e., cyclothymic, hyperthymic, depressive, and irritable). A replication study on an independent dataset of 121 HC was then performed. SMN fSD positively correlated with cyclothymic z-score and was significantly increased in the cyclothymic temperament compared to the depressive temperament subgroups, in both SFB and slow4. We replicated our findings in the independent dataset. A relationship between cyclothymic temperament and neuronal variability, an index of intrinsic neuronal activity, in the SMN was found. Cyclothymic and depressive temperaments were associated with opposite changes in the SMN variability, resembling changes previously described in manic and depressive phases of BD. These findings shed a novel light on the neural basis of affective temperament and also carry important implications for the understanding of a potential dimensional continuum between affective temperaments and BD, on both psychological and neuronal levels.Entities:
Keywords: bipolar disorder; neuronal variability; resting state fMRI; sensorimotor network; temperament
Mesh:
Year: 2018 PMID: 30367740 PMCID: PMC6865768 DOI: 10.1002/hbm.24453
Source DB: PubMed Journal: Hum Brain Mapp ISSN: 1065-9471 Impact factor: 5.038