Connectomics and neuroticism:an altered functional network organization

The personality trait neuroticism is a potent risk marker for psychopathology. Although the neurobiological basis remains unclear, studies have suggested that alterations in connectivity may underlie it. Therefore, the aim of the current study was to shed more light on the functional network organization in neuroticism. To this end, we applied graph theory on resting-state functional magnetic resonance imaging (fMRI) data in 120 women selected based on their neuroticism score. Binary and weighted brain-wide graphs were constructed to examine changes in the functional network structure and functional connectivity strength. Furthermore, graphs were partitioned into modules to specifically investigate connectivity within and between functional subnetworks related to emotion processing and cognitive control. Subsequently, complex network measures (ie, efficiency and modularity) were calculated on the brain-wide graphs and modules, and correlated with neuroticism scores. Compared with low neurotic individuals, high neurotic individuals exhibited a whole-brain network structure resembling more that of a random network and had overall weaker functional connections. Furthermore, in these high neurotic individuals, functional subnetworks could be delineated less clearly and the majority of these subnetworks showed lower efficiency, while the affective subnetwork showed higher efficiency. In addition, the cingulo-operculum subnetwork demonstrated more ties with other functional subnetworks in association with neuroticism. In conclusion, the ‘neurotic brain' has a less than optimal functional network organization and shows signs of functional disconnectivity. Moreover, in high compared with low neurotic individuals, emotion and salience subnetworks have a more prominent role in the information exchange, while sensory(-motor) and cognitive control subnetworks have a less prominent role

Associations Between Daily Affective Instability and Connectomics in Functional Subnetworks in Remitted Patients with Recurrent Major Depressive Disorder

Remitted patients with major depressive disorder (rMDD) often report more fluctuations in mood as residual symptomatology. It is unclear how this affective instability is associated with information processing related to the default mode (DMS), salience/reward (SRS), and frontoparietal (FPS) subnetworks in rMDD patients at high risk of recurrence (rrMDD). Sixty-two unipolar, drug-free rrMDD patients (>= 2 MDD episodes) and 41 healthy controls (HCs) were recruited. We used experience sampling methodology to monitor mood/cognitions (10 times a day for 6 days) and calculated affective instability using the mean adjusted absolute successive difference. Subsequently, we collected resting-state functional magnetic resonance imaging data and performed graph theory to obtain network metrics of integration within (local efficiency) the DMS, SRS, and FPS, and between (participation coefficient) these subnetworks and others. In rrMDD patients compared with HCs, we found that affective instability was increased in most negative mood/cognition variables and that the DMS had less connections with other subnetworks. Furthermore, we found that rrMDD patients, who showed more instability in feeling down and irritated, had less connections between the SRS and other subnetworks and higher local efficiency coefficients in the FPS, respectively. In conclusion, rrMDD patients, compared with HCs, are less stable in their negative mood and these dynamics are related to differences in information processing within-and between-specific functional subnetworks. These results are a first step to gain a better understanding of how mood fluctuations in real life are represented in the brain and provide insights into the vulnerability profile of MDD