The effect of external stimulation on functional networks in the aging healthy human brain

Understanding the brain changes occurring during aging can provide new insights for developing treatments that alleviate or reverse cognitive decline. Neurostimulation techniques have emerged as potential treatments for brain disorders and to improve cognitive functions. Nevertheless, given the ethical restrictions of neurostimulation approaches, in silico perturbation protocols based on causal whole-brain models are fundamental to gaining a mechanistic understanding of brain dynamics. Furthermore, this strategy could serve as a more specific biomarker relating local activity with global brain dynamics. Here, we used a large resting-state fMRI dataset divided into middle-aged (N=310, aged < 65 years) and older adults (N=310, aged >= 65) to characterize brain states in each group as a probabilistic metastable substate (PMS) space, each with a probabilistic occurrence and frequency. Then, we fitted the PMS to a whole-brain model and applied in silico stimulations with different intensities in each node to force transitions from the brain states of the older group to the middle-age group. We found that the precuneus, a brain area belonging to the default mode network and the rich club, was the best stimulation target. These findings might have important implications for designing neurostimulation interventions to revert the effects of aging on whole-brain dynamics.

Trajectory of rich club properties in structural brain networks

Many organizational principles of structural brain networks are established before birth and undergo considerable developmental changes afterwards. These include the topologically central hub regions and a densely connected rich club. While several studies have mapped developmental trajectories of brain connectivity and brain network organization across childhood and adolescence, comparatively little is known about subsequent development over the course of the lifespan. Here, we present a cross-sectional analysis of structural brain network development in N = 8,066 participants aged 5 to 80 years. Across all brain regions, structural connectivity strength followed an ′inverted-U′-shaped trajectory with vertex in the early 30s. Connectivity strength of hub regions showed a similar trajectory and the identity of hub regions remained stable across all age groups. While connectivity strength declined with advancing age, the organization of hub regions into a rich club did not only remain intact but became more pronounced, presumingly through a selected sparing of relevant connections from age-related connectivity loss. The stability of rich club organization in the face of overall age-related decline is consistent with a ′first come, last served′ model of neurodevelopment, where the first principles to develop are the last to decline with age. Rich club organization has been shown to be highly beneficial for communicability and higher cognition. A resilient rich club might thus be protective of a functional loss in late adulthood and represent a neural reserve to sustain cognitive functioning in the aging brain.

Adaptive Rewiring in Non-Uniform Coupled Oscillators

Structural plasticity of the brain can be represented in a highly simplified form as adaptive rewiring, the relay of connections according to the spontaneous dynamic synchronization in network activity. Adaptive rewiring, over time, leads from initial random networks to brain-like complex networks, i.e., networks with modular small-world structures and a rich-club effect. Adaptive rewiring has only been studied, however, in networks of identical oscillators with uniform or random coupling strengths. To implement information processing functions (e.g., stimulus selection or memory storage), it is necessary to consider symmetry-breaking perturbations of oscillator amplitudes and coupling strengths. We studied whether non-uniformities in amplitude or connection strength could operate in tandem with adaptive rewiring. Throughout network evolution, either amplitude or connection strength of a subset of oscillators was kept different from the rest. In these extreme conditions, subsets might become isolated from the rest of the network or otherwise interfere with the development of network complexity. However, whereas these subsets form distinctive structural and functional communities, they generally maintain connectivity with the rest of the network and allow the development of network complexity. Pathological development was observed only in a small proportion of the models. These results suggest that adaptive rewiring can robustly operate alongside information processing in biological and artificial neural networks.

Finding landmarks – An investigation of viewing behavior during spatial navigation in VR using a graph-theoretical analysis approach

Vision provides the most important sensory information for spatial navigation. Recent technical advances allow new options to conduct more naturalistic experiments in virtual reality (VR) while additionally gather data of the viewing behavior with eye tracking investigations. Here, we propose a method that allows to quantify characteristics of visual behavior by using graph-theoretical measures to abstract eye tracking data recorded in a 3D virtual urban environment. The analysis is based on eye tracking data of 20 participants, who freely explored the virtual city Seahaven for 90 minutes with an immersive VR headset with an inbuild eye tracker. To extract what participants looked at, we defined “gaze” events, from which we created gaze graphs. On these, we applied graph-theoretical measures to reveal the underlying structure of visual attention. Applying graph partitioning, we found that our virtual environment could be treated as one coherent city. To investigate the importance of houses in the city, we applied the node degree centrality measure. Our results revealed that 10 houses had a node degree that exceeded consistently two-sigma distance from the mean node degree of all other houses. The importance of these houses was supported by the hierarchy index, which showed a clear hierarchical structure of the gaze graphs. As these high node degree houses fulfilled several characteristics of landmarks, we named them “gaze-graph-defined landmarks”. Applying the rich club coefficient, we found that these gaze-graph-defined landmarks were preferentially connected to each other and that participants spend the majority of their experiment time in areas where at least two of those houses were visible. Our findings do not only provide new experimental evidence for the development of spatial knowledge, but also establish a new methodology to identify and assess the function of landmarks in spatial navigation based on eye tracking data.

The impact of genetic risk for Alzheimers disease on the structural brain networks of young adults

To facilitate pre-symptomatic diagnosis of late-onset Alzheimers disease, non-invasive imaging biomarkers could be combined with genetic risk information. In this work, we investigated the structural brain networks of young adults in relation to polygenic risk for Alzheimers disease, using magnetic resonance imaging (MRI) and genotype data for 564 19-year-old participants from the Avon Longitudinal Study of Parents and Children. Diffusion MRI was acquired on a 3T scanner, and the data were used to perform whole-brain tractography. The resulting tractograms were used to generate structural brain networks, using the number of streamlines and the diffusion tensor fractional anisotropy as edge weights. This was done for the whole-brain connectome, and for the default mode, limbic and visual subnetworks. Graph theoretical metrics were calculated for these networks, for each participant. The hubs of the networks were also identified, and the connectivity of the rich-club, feeder and local connections was also calculated. Polygenic risk scores (PRS), estimating the burden of genetic risk carried by an individual, were calculated both at genome-wide level and for nine specific disease pathways. The correlation coefficients were calculated between the PRSs and a) the graph theoretical metrics of the structural networks and b) the rich-club, feeder and local connectivity of the whole brain networks. In the visual subnetwork, the mean nodal strength exhibited a negative correlation with the genome-wide PRS including the APOE locus, while the mean betweenness centrality showed a positive correlation with the pathway-specific PRS for plasma lipoprotein particle assembly including the APOE locus. The rich-club connectivity was reduced in participants with higher genome-wide PRS including the APOE locus. Our results indicate small changes in the brain connectome of young adults at risk of developing Alzheimers disease in later life

Rich-Club Analysis of the Structural Brain Network in Cases with Cerebral Small Vessel Disease and Depression Symptoms

<b><i>Background:</i></b> Altered white matter brain networks have been extensively studied in cerebral small vessel disease (SVD). However, there exists currently a deficiency of comprehending the performance of changes within the structural networks of the brain in cases with cerebral SVD and depression symptoms. The main aim of the present research is to study the network topology behaviors and features of rich-club organization in SVD patients using graph theory and diffusion tensor imaging (DTI) to characterize changes in the microstructure of the brain. <b><i>Methods:</i></b> DTI datasets were acquired from 26 SVD patients with symptoms of depression (SVD + D) and 26 SVD patients without symptoms of depression (SVD − D), and a series of neuropsychological assessments were completed. A structural network was created using a deterministic fiber tracking method. The analysis of rich-club was performed in company with analysis of the global network features of the network to characterize the topological properties of all subjects. <b><i>Results:</i></b> DTI data were obtained from SVD patients who manifested symptoms of depression (SVD + D) and from control SVD patients (SVD − D). In comparison with SVD − D patients, SVD + D cases demonstrated a diminished coefficient of clustering along with lower global efficiencies and longer path length characteristics. Rich-club analysis showed SVD + D patients had decreased feeder connectivity and local connectivity strengths compared to SVD − D patients. Our data also showed that the feeder connections in the brain correlated significantly with the severity of depression in SVD + D patients. <b><i>Conclusions:</i></b> Our study revealed that SVD patients with depressive symptoms have disrupted white matter networks that characteristically have reduced network efficiency compared to the networks in other SVD patients. Disrupted information interactions among the regions of nonrich-club and rich-club in SVD cases are related to the severity of depression. Our data suggest that DTI may be utilized as an appropriate biomarker for the diagnosis of depression in comorbid SVD patients.