Time-resolved connectome of the five-factor model of personality.

The human brain is characterized by highly dynamic patterns of functional connectivity. However, it is unknown whether this time-variant 'connectome' is related to the individual differences in the behavioural and cognitive traits described in the five-factor model of personality. To answer this question, inter-network time-variant connectivity was computed in n = 818 healthy people via a dynamical conditional correlation model. Next, network dynamicity was quantified throughout an ad-hoc measure (T-index) and the generalizability of the multi-variate associations between personality traits and network dynamicity was assessed using a train/test split approach. Conscientiousness, reflecting enhanced cognitive and emotional control, was the sole trait linked to stationary connectivity across several circuits such as the default mode and prefronto-parietal network. The stationarity in the 'communication' across large-scale networks offers a mechanistic description of the capacity of conscientious people to 'protect' non-immediate goals against interference over-time. This study informs future research aiming at developing more realistic models of the brain dynamics mediating personality differences.

Reduced betweenness centrality of a sensory-motor vestibular network in subclinical agoraphobia.

Agoraphobic patients feel dizzy in crowded open spaces and respond to this symptom with excessive fear and avoidance. These clinical features show great similitude with the newly defined syndrome of persistent postural perceptual dizziness (PPPD). Patients with PPPD show decreased activity and connectivity in regions of the vestibular cortex. Due to the great overlap between these two conditions, we hypothesized that individuals with sub-clinical agoraphobia would show reduction in the connectivity features of these regions. We selected a group of healthy individuals from the Human Connectome Project that self-reported agoraphobia episodes, and compared it with a control group. We accurately matched the two groups for psychological measures and personality traits in order to study the neural correlates of vestibular symptoms independently of possible psychiatric vulnerabilities. We found that the agoraphobia group showed reduced betweenness centrality of a network encompassing key regions of the vestibular cortex. Dysfunctions of the vestibular cortex may explain the dizziness symptom for a disorder previously labelled as psychogenic.

Structural connectome of the human vestibular, pre-motor, and navigation network.

The aim of this study is to characterize modules and hubs within the multimodal vestibular system and, particularly, to test the centrality of posterior peri-sylvian regions. Structural connectivity matrices from 50 unrelated healthy right-handed subjects from the Human Connectome Project (HCP) database were analyzed using multishell diffusion-weighted data, probabilistic tractography (constrained spherical-deconvolution informed filtering of tractograms) in combination with subject-specific grey matter parcellations. Network nodes included parcellated regions within the vestibular, pre-motor and navigation system. Module calculation produced two and three modules in the right and left hemisphere, respectively. On the right, regions were grouped into a vestibular and pre-motor module, and into a visual-navigation module. On the left this last module was split into an inferior and superior component. In the thalamus, a region comprising the mediodorsal and anterior complex, and lateral and inferior pulvinar, was included in the ipsilateral navigation module, while the remaining thalamus was clustered with the ipsilateral vestibular pre-motor module. Hubs were located bilaterally in regions encompassing the inferior parietal cortex and the precuneus. This analysis revealed a dorso-lateral path within the multi-modal vestibular system related to vestibular / motor control, and a ventro-medial path related to spatial orientation / navigation. Posterior peri-sylvian regions may represent the main hubs of the whole modular network.

Functional connectome of the five-factor model of personality.

A key objective of the emerging field of personality neuroscience is to link the great variety of the enduring dispositions of human behaviour with reliable markers of brain function. This can be achieved by analyzing large sets of data with methods that model whole-brain connectivity patterns. To meet these expectations, we exploited a large repository of personality and neuroimaging measures made publicly available via the Human Connectome Project. Using connectomic analyses based on graph theory, we computed global and local indices of functional connectivity (e.g., nodal strength, efficiency, clustering, betweenness centrality) and related these metrics to the five-factor-model (FFM) personality traits (i.e., neuroticism, extraversion, openness, agreeableness, and conscientiousness). The maximal information coefficient was used to assess for linear and non-linear statistical dependencies across the graph 'nodes', which were defined as distinct brain circuits identified via independent component analysis. Multi-variate regression models and 'train/test' machine-learning approaches were also used to examine the associations between FFM traits and connectomic indices as well as to test for the generalizability of the main findings, whilst accounting for age and sex differences. Conscientiousness was the sole FFM trait linked to measures of higher functional connectivity in the fronto-parietal and default mode networks. This might provide a mechanistic explanation of the behavioural observation that conscientious people are reliable and efficient in goal-setting or planning. Our study provides new inputs to understanding the neurological basis of personality and contributes to the development of more realistic models of the brain dynamics that mediate personality differences.

Dynamical brain connectivity estimation using GARCH models: An application to personality neuroscience.

It has recently become evident that the functional connectome of the human brain is a dynamical entity whose time evolution carries important information underpinning physiological brain function as well as its disease-related aberrations. While simple sliding window approaches have had some success in estimating dynamical brain connectivity in a functional MRI (fMRI) context, these methods suffer from limitations related to the arbitrary choice of window length and limited time resolution. Recently, Generalized autoregressive conditional heteroscedastic (GARCH) models have been employed to generate dynamical covariance models which can be applied to fMRI. Here, we employ a GARCH-based method (dynamic conditional correlation - DCC) to estimate dynamical brain connectivity in the Human Connectome Project (HCP) dataset and study how the dynamic functional connectivity behaviors related to personality as described by the five-factor model. Openness, a trait related to curiosity and creativity, is the only trait associated with significant differences in the amount of time-variability (but not in absolute median connectivity) of several inter-network functional connections in the human brain. The DCC method offers a novel window to extract dynamical information which can aid in elucidating the neurophysiological underpinning of phenomena to which conventional static brain connectivity estimates are insensitive.

Dynamic inter-network connectivity in the human brain.

Recently, the field of functional brain connectivity has shifted its attention on studying how functional connectivity (FC) between remote regions changes over time. It is becoming increasingly evident that the human "connectome" is a dynamical entity whose variations are effected over very short timescales and reflect crucial mechanisms which underline the physiological functioning of the brain. In this study, we employ ad-hoc statistical and surrogate data generation methods to quantify whether and which brain networks displayed dynamic behaviors in a very large sample of healthy subjects provided by the Human Connectome Project (HCP). Our findings provided evidences that there are specific pairs of networks and specific networks within the healthy brain that are more likely to display dynamic behaviors. This new set of findings supports the notion that studying the time-variant connectivity in the brain could reveal useful and important properties about brain functioning in health and disease.

The brain network underlying serial visual search: comparing overt and covert spatial orienting, for activations and for effective connectivity.

We used functional magnetic resonance imaging (fMRI) to investigate the brain basis of overt and covert forms of attention during search, while employing stringent control of both eye movements and attentional shifts. A factorial design compared overt and covert forms of goal-directed serial search versus stimulus-driven tracking. To match ocular changes and the number and magnitude of attention shifts across cells in the design, stimulus-driven tracking involved trial-specific "replay" of previous goal-directed eye movements. We found that, in terms of cortical activations, engagement of the dorsal fronto-parietal network by goal-directed attention did not depend on oculomotor requirements, being found similarly for covert attention, in accord with other work. However, analyses of effective connectivity (or "functional coupling") revealed that information flow within this network changed significantly as a function of both the task (goal-directed or stimulus-driven) and the overt versus covert form of attention. Additionally, we observed a distinct set of subcortical regions (pulvinar and caudate nucleus) engaged primarily during the covert form of goal-directed search. We conclude that dynamics within the dorsal fronto-parietal attentional system flexibly reorganize to integrate task demands and oculomotor requirements.

Intermolecular double quantum coherences (iDQc) and diffusion-weighted imaging (DWI) imaging of the human brain at 1.5 T.

To study the sensitivity of intermolecular double quantum coherences (iDQc) imaging contrast to brain microstructure and brain anisotropy, we investigated the iDQC contrast between differently structured areas of the brain according to the strength and the direction of the applied correlation gradient. Thus diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) maps have been obtained. This procedure, which consists of analyzing both iDQc and DWI images at different gradient strength and gradient direction, could be a promising tool for clinical brain investigations performed with higher than 1.5 T magnetic fields.

In vivo multiple spin echoes imaging of trabecular bone on a clinical 1.5 T MR scanner.

In vivo multiple spin echoes (MSE) images of bone marrow in trabecular bone were obtained for the first time on a clinical 1.5 T scanner. Despite of a reduced sensitivity of the MSE trabecular bone images with respect to the cerebral matter ones, it is possible to observe some features in the MSE trabecular bone images that may be useful in the diagnosis of osteopenic states. Two different CRAZED-type MSE imaging sequences based on spin-echo and EPI imaging modalities were applied in phantom and in vivo. Preliminary experimental results indicate that EPI imaging readout seems to conceal the MSE contrast correlated with pore dimension in porous media. However it is still possible to detect anisotropy effects related to the bone structure in MSE-EPI images. Some strategies are suggested to optimize the quality of MSE trabecular bone images.

Real-time quantification of T(2)(*) changes using multiecho planar imaging and numerical methods.

Conventional approaches to quantify whole brain T(2)(*) maps use nonlinear regression with intensive computational requirements that therefore likely limit quantitative T(2)(*) mapping for real-time applications. To overcome these limitations an alternative method, NumART(2)(*) (NUMerical Algorithm for Real-time T(2)(*) mapping) that directly calculates T(2)(*) by a linear combination of images obtained at three or more different echo times was developed. NumART(2)(*), linear least-squares, and nonlinear regression techniques were applied to multiecho planar images of the human brain and to simulated data. Although NumART(2)(*) may overestimate T(2)(*), it yields comparable values to regression techniques in cortical and subcortical areas, with only moderate deviations for echo spacings between 18 and 40 ms. NumART(2)(*), like linear regression, requires 2% of the computational time needed for nonlinear regression and compares favorably with linear regression due to its higher precision. The use of NumART(2)(*) for continuous on-line T(2)(*) mapping in real time fMRI studies is shown.