Reduced network integration in default mode and executive networks is associated with social and personal optimism biases

Abstract

An optimism bias refers to the belief in good things happening to oneself in the future with a higher likelihood than is justified. Social optimism biases extend this concept to groups that one identifies with. Previous literature has found that both personal and social optimism biases are linked to brain structure and task‐related brain function. Less is known about whether optimism biases are also expressed in resting‐state functional connectivity (RSFC). Forty‐two participants completed questionnaires on dispositional personal optimism (which is not necessarily unjustified) and comparative optimism (i.e., whether we see our own future as being rosier than a comparison person's future) and underwent a resting‐state functional magnetic resonance imaging scan. They further undertook an imaginative soccer task in order to assess both their personal and social optimism bias. We tested associations of these data with RSFC within and between 13 networks, using sparse canonical correlation analyses (sCCAs). We found that the primary sCCA component was positively connected to personal and social optimism bias and negatively connected to dispositional personal pessimism. This component was associated with (a) reduced integration of the default mode network, (b) reduced integration of the central executive and salience networks, and (c) reduced segregation between the default mode network and the central executive network. Our finding that optimism biases are linked to RSFC indicates that they may be rooted in neurobiology that exists outside of concurrent tasks. This poses questions as to what the limits of the malleability of such biases may be.

Multivariate patterns of brain-behavior associations across the adult lifespan

“…Affective cognition has been shown to change throughout the lifespan, with older adults reporting improvement in emotional experience and an increased frequency of positive feelings, relative to younger adults [ 53 ], which is also what we report (see Supplementary Table 4 ). Other neuroimaging studies have independently reported association between affective cognition and working memory network activation [ 7 ], functional network connectivity [ 8 , 13 , 54 , 55 ] or structural morphometry [ 9 ]. It is also known from psychiatric studies that the activation of the working memory network is modulated by the level of depression and anxiety [ 56 ].…”

“…To achieve this, the algorithm groups variables from either side into component pairs/dimensions, which are referred to as modes in the present paper. Instead of a classic (non-sparse) canonical correlation analysis, we conducted sCCA because this analysis (a) permits the inclusion of more features than observations and (b) allows stronger inferences regarding the contribution of individual variables (for similar approaches, see [ 7 , 9 , 55 ]; also see reviews by Zhuang et al [ 36 ], Wang, Smallwood [ 94 ]).…”

Predictive modeling of optimism bias using gray matter cortical thickness

“…Participants: Our study takes leverage from Moser et al [3], who investigated functional brain correlates of optimism biases. We have used the same dataset in terms of participants, experimental setup, and deriving the optimism biases.…”

“…Optimism bias (OB) is a well-established behavioral dimension, where individuals associate themselves with more positive outcome scenarios than negative [1,2]. More specifically, optimism bias can further be branched into the deeper concepts of personal and social optimism biases [3]. Personal optimism bias (POB) stems from a bias towards oneself where an individual expects a more favorable outcome compared to a peer or competitor, given the same scenario.…”

“…Gray matter cortical thickness remains the feature set for the predictive modeling, and POB and SOB serve as the target variables. Both, POB and SOB were calculated from a previously established soccer-based experimental paradigm [3,4,23]. In brief, each participant recruited for our experiment was asked to estimate the likelihood for successful outcomes given different soccer situations (figure 1).…”