Brain structural connectivity and neuroticism in healthy adults

Recent research shows that congruent outcomes are more rapidly (and incongruent less rapidly) detected when individuals receive optimistic rather than pessimistic cues, an effect that was termed optimism robustness. In the current voxel-based morphometry study, we examined whether optimism robustness has a counterpart in brain structure. The participants’ task was to detect two different letters (symbolizing monetary gain or loss) in a visual search matrix. Prior to each onset of the search matrix, two different verbal cues informed our participants about a high probability to gain (optimistic expectancy) or lose (pessimistic expectancy) money. The target presented was either congruent or incongruent with these induced expectancies. Optimism robustness revealed in the participants’ reaction times correlated positively with gray matter volume (GMV) in brain regions involved in selective attention (medial visual association area, intraparietal sulcus), emphasizing the strong intertwinement of optimistic expectancies and attention deployment. In addition, GMV in the primary visual cortex diminished with increasing optimism robustness, in line with the interpretation of optimism robustness arising from a global, context-oriented perception. Future studies should address the malleability of these structural correlates of optimism robustness. Our results may assist in the identification of treatment targets in depression.

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“…Accordingly, future investigations may apply graph theory ( Kaiser, 2011 ; Korgaonkar et al. , 2014 ; Ueda et al. , 2018 ; Pang et al.…”

Section: Discussionmentioning

confidence: 99%

Enhanced sensitivity to optimistic cues is manifested in brain structure: a voxel-based morphometry study

Aue

Dricu

Singh

et al. 2021

Social Cognitive and Affective Neuroscience

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“…The results are thus rarely consistent (see Dima, Friston, Stephan, & Frangou, 2015;Gentili et al, 2017;B. J. Li et al, 2018;Ueda et al, 2018).…”

Section: N/ne In a Predictive-processing Modelmentioning

confidence: 96%

Better Safe Than Sorry: A Common Signature of General Vulnerability for Psychopathology

Bergh

Brosschot

Critchley

et al. 2020

Perspect Psychol Sci

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Several labels, such as neuroticism, negative emotionality, and dispositional negativity, indicate a broad dimension of psychopathology. However, largely separate, often disorder-specific research lines have developed that focus on different cognitive and affective characteristics that are associated with this dimension, such as perseverative cognition (worry, rumination), reduced autobiographical memory specificity, compromised fear learning, and enhanced somatic-symptom reporting. In this article, we present a theoretical perspective within a predictive-processing framework in which we trace these phenotypically different characteristics back to a common underlying “better-safe-than-sorry” processing strategy. This implies information processing that tends to be low in sensory-perceptual detail, which allows threat-related categorical priors to dominate conscious experience and for chronic uncertainty/surprise because of a stagnated error-reduction process. This common information-processing strategy has beneficial effects in the short term but important costs in the long term. From this perspective, we suggest that the phenomenally distinct cognitive and affective psychopathological characteristics mentioned above represent the same basic processing heuristic of the brain and are only different in relation to the particular type of information involved (e.g., in working memory, in autobiographical memory, in the external and internal world). Clinical implications of this view are discussed.

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“…Comparing the cumulative number of shortest paths going through each brain region during the transition from a resting state to cognitive task evaluation, they found the gradual transition from a sharp decrease of betweenness across the parietal and frontal lobes in the delta range to a pronounced increase in the gamma range [168]. By constructing whole-brain inter-regional interactions for each participant, Ueda et al [169] found that the correlation between neuroticism and betweenness are very different for various brain regions, implying that the neuroticism trait is likely formed as a result of interactions among the brain regions related to neuroticism.…”

Section: Betweenness and Bottlenecksmentioning

confidence: 99%

Computational network biology: Data, models, and applications

et al. 2020

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Biological entities are involved in intricate and complex interactions, in which uncovering the biological information from the network concepts are of great significance. Benefiting from the advances of network science and high-throughput biomedical technologies, studying the biological systems from network biology has attracted much attention in recent years, and networks have long been central to our understanding of biological systems, in the form of linkage maps among genotypes, phenotypes, and the corresponding environmental factors. In this review, we summarize the recent developments of computational network biology, first introducing various types of biological networks and network structural properties. We then review the network-based approaches, ranging from some network metrics to the complicated machine-learning methods, and emphasize how to use these algorithms to gain new biological insights. Furthermore, we highlight the application in neuroscience, human disease, and drug developments from the perspectives of network science, and we discuss some major challenges and future directions. We hope that this review will draw increasing

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Brain structural connectivity and neuroticism in healthy adults

Cited by 21 publications

References 60 publications

Enhanced sensitivity to optimistic cues is manifested in brain structure: a voxel-based morphometry study

Enhanced sensitivity to optimistic cues is manifested in brain structure: a voxel-based morphometry study

Better Safe Than Sorry: A Common Signature of General Vulnerability for Psychopathology

Computational network biology: Data, models, and applications

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