Dataset Information

Personality and complex brain networks: The role of openness to experience in default network efficiency.

ABSTRACT: The brain's default network (DN) has been a topic of considerable empirical interest. In fMRI research, DN activity is associated with spontaneous and self-generated cognition, such as mind-wandering, episodic memory retrieval, future thinking, mental simulation, theory of mind reasoning, and creative cognition. Despite large literatures on developmental and disease-related influences on the DN, surprisingly little is known about the factors that impact normal variation in DN functioning. Using structural equation modeling and graph theoretical analysis of resting-state fMRI data, we provide evidence that Openness to Experience-a normally distributed personality trait reflecting a tendency to engage in imaginative, creative, and abstract cognitive processes-underlies efficiency of information processing within the DN. Across two studies, Openness predicted the global efficiency of a functional network comprised of DN nodes and corresponding edges. In Study 2, Openness remained a robust predictor-even after controlling for intelligence, age, gender, and other personality variables-explaining 18% of the variance in DN functioning. These findings point to a biological basis of Openness to Experience, and suggest that normally distributed personality traits affect the intrinsic architecture of large-scale brain systems. Hum Brain Mapp 37:773-779, 2016. © 2015 Wiley Periodicals, Inc.

SUBMITTER: Beaty RE

PROVIDER: S-EPMC4738373 | biostudies-other | 2016 Feb

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:Personality neuroscience aims to find associations between brain measures and personality traits. Findings to date have been severely limited by a number of factors, including small sample size and omission of out-of-sample prediction. We capitalized on the recent availability of a large database, together with the emergence of specific criteria for best practices in neuroimaging studies of individual differences. We analyzed resting-state functional magnetic resonance imaging data from 884 young healthy adults in the Human Connectome Project (HCP) database. We attempted to predict personality traits from the "Big Five", as assessed with the NEO-FFI test, using individual functional connectivity matrices. After regressing out potential confounds (such as age, sex, handedness and fluid intelligence), we used a cross-validated framework, together with test-retest replication (across two sessions of resting-state fMRI for each subject), to quantify how well the neuroimaging data could predict each of the five personality factors. We tested three different (published) denoising strategies for the fMRI data, two inter-subject alignment and brain parcellation schemes, and three different linear models for prediction. As measurement noise is known to moderate statistical relationships, we performed final prediction analyses using average connectivity across both imaging sessions (1 h of data), with the analysis pipeline that yielded the highest predictability overall. Across all results (test/retest; 3 denoising strategies; 2 alignment schemes; 3 models), Openness to experience emerged as the only reliably predicted personality factor. Using the full hour of resting-state data and the best pipeline, we could predict Openness to experience (NEOFAC_O: r=0.24, R2=0.024) almost as well as we could predict the score on a 24-item intelligence test (PMAT24_A_CR: r=0.26, R2=0.044). Other factors (Extraversion, Neuroticism, Agreeableness and Conscientiousness) yielded weaker predictions across results that were not statistically significant under permutation testing. We also derived two superordinate personality factors ("α" and "β") from a principal components analysis of the NEO-FFI factor scores, thereby reducing noise and enhancing the precision of these measures of personality. We could account for 5% of the variance in the β superordinate factor (r=0.27, R2=0.050), which loads highly on Openness to experience. We conclude with a discussion of the potential for predicting personality from neuroimaging data and make specific recommendations for the field.

Project description:Recent neuroimaging studies have associated activity in the default mode network (DMN) with self-referential and pain processing, both of which are altered in borderline personality disorder (BPD). In patients with BPD, antinociception has been linked to altered activity in brain regions involved in the cognitive and affective evaluation of pain. Findings in healthy subjects indicate that painful stimulation leads to blood oxygenation level-dependent signal decreases and changes in the functional architecture of the DMN.To connect the previously separate research areas of DMN connectivity and altered pain perception in BPD and to explore DMN connectivity during pain processing in patients with BPD.Case-control study.University hospital.Twenty-five women with BPD, including 23 (92%) with a history of self-harm, and 22 age-matched control subjects.Psychophysical assessment and functional magnetic resonance imaging during painful heat vs neutral temperature stimulation.Connectivity of DMN as assessed via independent component analysis and psychophysiological interaction analysis.Compared with control subjects, patients with BPD showed less integration of the left retrosplenial cortex and left superior frontal gyrus into the DMN. Higher BPD symptom severity and trait dissociation were associated with an attenuated signal decrease of the DMN in response to painful stimulation. During pain vs neutral, patients with BPD exhibited less posterior cingulate cortex seed region connectivity with the left dorsolateral prefrontal cortex.Patients with BPD showed significant alterations in DMN connectivity, with differences in spatial integrity and temporal characteristics. These alterations may reflect a different cognitive and affective appraisal of pain as less self-relevant and aversive as well as a deficiency in the switching between baseline and task-related processing. This deficiency may be related to everyday difficulties of patients with BPD in regulating their emotions, focusing mindfully on 1 task at a time, and efficiently shifting their attention from one task to another.

Dataset Information

Personality and complex brain networks: The role of openness to experience in default network efficiency.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets