Dataset Information

Stratification of MDD and GAD patients by resting state brain connectivity predicts cognitive bias.

ABSTRACT: Patients with Generalized Anxiety Disorder (GAD) and Major Depressive Disorder (MDD) show between-group comorbidity and symptom overlap, and within-group heterogeneity. Resting state functional connectivity might provide an alternate, biologically informed means by which to stratify patients with GAD or MDD. Resting state functional magnetic resonance imaging data were acquired from 23 adults with GAD, 21 adults with MDD, and 27 healthy adult control participants. We investigated whether within- or between-network connectivity indices from five resting state networks predicted scores on continuous measures of depression and anxiety. Successful predictors were used to stratify participants into two new groups. We examined whether this stratification predicted attentional bias towards threat and whether this varied between patients and controls. Depression scores were linked to elevated connectivity within a limbic network including the amygdala, hippocampus, VMPFC and subgenual ACC. Patients with GAD or MDD with high limbic connectivity showed poorer performance on an attention-to-threat task than patients with low limbic connectivity. No parallel effect was observed for control participants, resulting in an interaction of clinical status by resting state group. Our findings provide initial evidence for the external validity of stratification of MDD and GAD patients by functional connectivity markers. This stratification cuts across diagnostic boundaries and might valuably inform future intervention studies. Our findings also highlight that biomarkers of interest can have different cognitive correlates in individuals with versus without clinically significant symptomatology. This might reflect protective influences leading to resilience in some individuals but not others.

SUBMITTER: Bijsterbosch JD

PROVIDER: S-EPMC6051497 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Stratification of MDD and GAD patients by resting state brain connectivity predicts cognitive bias.

Bijsterbosch Janine D JD Ansari Tahereh L TL Smith Stephen S Gauld Oliver O Zika Ondrej O Boessenkool Sirius S Browning Michael M Reinecke Andrea A Bishop Sonia J SJ

NeuroImage. Clinical 20180430

Patients with Generalized Anxiety Disorder (GAD) and Major Depressive Disorder (MDD) show between-group comorbidity and symptom overlap, and within-group heterogeneity. Resting state functional connectivity might provide an alternate, biologically informed means by which to stratify patients with GAD or MDD. Resting state functional magnetic resonance imaging data were acquired from 23 adults with GAD, 21 adults with MDD, and 27 healthy adult control participants. We investigated whether within- ...[more]

PMID: 30035026

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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.

Dataset Information

Stratification of MDD and GAD patients by resting state brain connectivity predicts cognitive bias.

Publications

Stratification of MDD and GAD patients by resting state brain connectivity predicts cognitive bias.

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