The role of Brain Connectivity in evaluating neuronal integrity and its relationship with pathological substrates is going to be crucial and influential in the field of neuroscience and medicine over the coming years. As impairment of structural and functional connectivity, as either a primary or secondary event, is implicated in neuronal damage in most brain disorders, Brain Connectivity plays a major role in research into normal brain function and a range of neurological disorders. As the field of neuroscience is constantly evolving, with multimodal imaging now considered as the preferable method of evaluating different diseases and interventions, we have expanded the breadth of research published in Brain Connectivity to ensure that we are able to include articles of a translational nature in the field of neuroscience.
With the intention of expanding the scope of our journal, I would like to invite leaders in the field such as the readers of Brain Connectivity to submit original articles and reviews describing:
Advances in neuroimaging using positron emission tomography (PET) and magnetic resonance imaging (MRI) in Alzheimer's disease, Parkinson's disease, and other neurodegenerative diseases;
Clinical translation of novel PET and MRI biomarkers in neurodegenerative diseases and stroke;
How different pathological substrates influence structural and functional connectivity in brain disorders;
Multimodal imaging in brain disorders in both human subjects and animal models; and
Experimental techniques combining MRI (connectivity), electroencephalography (EEG), magnetoencephalography (MEG), PET, single photon emission computed tomography, and other new and evolving methods.
For more information about the journal, including scope and instructions for authors, please visit our website at https://mc.manuscriptcentral.com/brainconnectivity
In this current issue, you will find several high-quality articles by experts in their fields:
Brain Network Dynamics Correlates with Personality Traits. In this article, Aya Kabbara, Mahmoud Hassan, and their colleagues from University Rennes and Aix Marseille University France investigate the relationship between brain network dynamics and personality traits. The current understanding of personality-related differences in functional connectivity has been obtained through the stationary analysis, which does not capture the complex dynamical properties of brain networks. In this study, the authors aim to evaluate the feasibility of using dynamic network measures to predict personality traits. Using the EEG/MEG source connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from two data sets: (1) resting-state EEG data acquired from 56 subjects and (2) resting-state MEG data provided from the Human Connectome Project. Then, several dynamic functional connectivity metrics were evaluated. Interestingly, a significant relationship between this personality trait and the dynamic variability of the temporal lobe regions was observed. Results also revealed that extraversion and openness are positively correlated with the dynamics of the brain networks. These findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding of the neural substrates of personality.
Effect of Fixed-Density Thresholding on Structural Brain Networks: A Demonstration in Cerebral Small Vessel Disease. Bruno M. de Brito Robalo, Yael D. Reijmer, and colleagues from University Medical Center Utrecht, on behalf of the Utrecht VCI study group, evaluated how the popular solution to control for edge density variability in structural brain network analysis influences the basic network architecture in terms of edge weights, hub-location, and hub-connectivity and, in particular, how it affects the sensitivity to detect disease-related abnormalities. The authors investigated two questions in a cohort of patients with cerebral small vessel disease and in age-matched controls.
The authors found that fixed-density thresholding disproportionally removes edges composed by long streamlines, but is independent of fractional anisotropy. The edges removed were not preferentially connected to hub or non-hub nodes. More than half of the original hubs were reproducible when networks were thresholded to a density of ≥10%. Furthermore, the between-group differences in graph measures observed in the unthresholded network remained present after thresholding, irrespective of the chosen density. The authors therefore conclude that moderate fixed-density thresholds can successfully be applied to control for the effects of density in structural brain network analysis.
Resting-State Functional Connectivity Dynamics in Healthy Aging: An Approach through Network Change Point Detection. In this study, Núria Mancho-Fora, Joan Guàrdia-Olmos, and their colleagues from the University of Barcelona aim to assess the impact of age on the short-term temporal dynamics of the topological properties of the undirected and weighted whole-brain functional connectivity networks. They investigated the association between the participant's age and the number of significant change points detected through the Network Change Point Detection algorithm. They investigated the resting-state functional MRI sequences of 114 healthy individuals combined from three different studies conducted at the University of Barcelona. Participants were healthy people in the absence of any pathology that could interfere with the scanning procedures, as well as any chronic illness that implied a short-lived situation. The topological properties of all subjects' functional connectivity networks were characterized by their network strength, transitivity, characteristic path length, and small-worldness, analyzing the effect of age in those observed distributions. The authors constructed a mixed linear model for each network topological property and demonstrated several statistically significant relationships between the indicators of the functional connectivity networks that show a certain regular pattern of change in the network. These dynamic changes were related to the age of each group studied.
Brain Structural–Functional Connectivity Relationship Underlying the Information Processing Speed. In this article, Pedro Henrique Rodrigues da Silva, Renata Ferranti Leoni, and colleagues from the University of São Paulo, Brazil, highlight the relationship between information processing speed (IPS) and structural–functional connectivity. Human cognition and behavior emerge from neuronal interactions on a brain structural architecture. The convergence (or divergence) between functional dynamics and structural connectivity and their relationship with cognition is still a pivotal question regarding the brain. The authors hypothesized that the structural connectivity constrains, but does not determine, functional connectivity, and such a relationship is related to cognitive performance. IPS was assessed by the Symbol Digit Modalities Test (SDMT), while blood oxygenation level–dependent and diffusion tensor images of young healthy volunteers were acquired in a 3T MRI machine. The authors suggest that the IPS functional network is related to the highest SDMT scores when its effective endogenous connections are suppressed to the detriment of modulation caused by the experimental conditions, with the underlying structure providing low diffusion environments.
Finally, I would like to thank all the researchers and all the staff at Mary Ann Liebert, Inc., publishers, editors, and reviewers of Brain Connectivity working during this extremely difficult time of the COVID-19 pandemic to advance research in every corner of the world to improve our lives. I sincerely hope that all our contributors and readers of the journal are remaining in good health.
