Abstract
The integrity of structural and functional connectivity is the basis of normal functioning of the brain and is impaired in different disease states. The role of Brain Connectivity in evaluating neuronal integrity and its relationship with pathological substrates and genetic and epigenetic influence 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, either as 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.
Different pathological substrates such as amyloid deposition, tau deposition, microglial activation, synuclein pathology, astrocyte activation, mitochondrial function and other changes occurring in the brain in different neurodegenerative diseases could influence the structural and functional integrity.
To highlight the recent advances in brain connectivity and the pathology of Alzheimer's disease, we are organizing a special issue on the influence of pathological substrates on structural and/or functional connectivity in the Alzheimer's trajectory (subjective cognitive impairment, mild cognitive impairment, and Alzheimer's disease).
We are specifically looking for Original research investigating the role of amyloid, tau, neuroinflammation/microglial activation and astrocytes on structural and/or functional connectivity, along with influence on glucose metabolism, and genetic and epigenetic factors Review articles and perspectives.
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 also like to invite authors to submit original articles and reviews describing: Artificial intelligence in neuroimaging Advances in neuroimaging using PET and MRI in Alzheimer's disease, Parkinson's disease and other neurodegenerative diseases Novel PET and MRI biomarkers in neurodegenerative diseases and stroke Influence of genetic and epigenetic factors on structural and functional connectivity in brain disorders Multimodal imaging in brain disorders in both human subjects and animal models Experimental techniques combining magnetic resonance imaging (MRI) (connectivity), electroencephalography (EEG), magnetoencephalography (MEG), positron emission tomography (PET), single photon emission computed tomography (SPECT), and other new and evolving methods.
For more information about the Journal, including scope and instructions for authors, please visit our website
In this current issue, you will find several high-quality articles by experts in their fields:
Indirect Structural Connectivity Identifies Changes in Brain Networks After Stroke
Miguel Sotelo and Brian Schmit along with their colleagues set out to evaluate changes in structural connectivity after stroke and evaluated changes in indirect connectivity to post-stroke impairment. They implemented a novel measure of indirect connectivity to assess the impact of stroke on brain connectivity. They performed probabilistic tractography and direct and indirect connectivity in post-stroke patients. These measures were then linearly combined with measures of white matter integrity to predict motor impairment. They found significantly reduced indirect connectivity in the frontal and parietal lobes, ipsilesional subcortical regions and bilateral cerebellum after stroke. This study provides evidence of changes in indirect connectivity and how it relates with motor impairment. The authors highlight the importance of measuring indirect connectivity to identify the effect of stroke on brain networks
In Amyotrophic Lateral Sclerosis Blood Cytokines Are Altered, but Do Not Correlate with Changes in Brain Topology
Arianna Polverino and Pierpaolo Sorrentino and colleagues aimed at investigating the possible relationship between peripheral markers of inflammation and brain networks in amyotrophic lateral sclerosis. Based on magnetoencephalography data, the authors estimated topological properties of the brain networks in ALS patients and healthy controls along with evaluating cytokines, and modeled the brain topological features in function of the cytokines levels. They found significant differences in the levels of the cytokines IL-4, IL-1β and IFN-γ between patients and controls. They also detected modifications in brain global topological parameters in terms of hyper-connectedness. Despite both blood cytokines and brain topology being altered, such changes do not appear have a direct relationship.
BrainNET: Inference of Brain Network Topology Using Machine Learning
Gowtham Krishnan Murugesan and Joseph Maldjian along with their colleagues set out to develop a new fMRI network inference method, BrainNET, that utilizes an efficient machine learning algorithm to quantify contributions of various regions in the brain to a specific region. BrainNET is based on Extremely Randomized Trees to estimate network topology from fMRI data and is modified to generate an adjacency matrix representing brain network topology, without reliance on arbitrary thresholds. Open source simulated fMRI data in twenty-eight different simulations under various confounding conditions with known ground truth were used to validate the method. Performance was compared with correlation and partial correlation (PC). The real-world performance was then evaluated.
Effects of White Matter Hyperintensities on Brain Connectivity and Hippocampal Volume in Healthy Subjects According to Their Localization
Michele Porcu and Luca Saba along with their colleagues the relationships between white matter hyperintensities and hippocampal volume and their influence on brain networks by using resting-state functional connectivity according to their localization.
In this exploratory cross-sectional study, morphometric analyses of both WMH burden and the total hippocampal relative volume was performed for each subject. The authors demonstrated that deep white matter intensity influenced network properties of several cerebral regions, in particular, global and local efficiency of both the hippocampi.
Real-Time Resting-State fMRI Using Averaged Sliding Windows with Partial Correlations and Regression of Confounding Signals
Vakamudi Kishore and Stefan Posse along with their colleagues describe a computationally efficient real-time seed-based resting-state fMRI analysis pipeline using moving averaged sliding-windows with partial correlations and regression of motion parameters and signals from white matter and cerebrospinal fluid. They found that analytical and numerical analyses of averaged sliding-window correlation and sliding-window regression as a function of window width show selectable bandpass filter characteristics and effective suppression of artifactual correlations resulting from signal drifts and transients. The authors conclude that computational performance and confound tolerance make this seed-based resting-state fMRI approach suitable for real-time monitoring of data quality and resting-state connectivity dynamics in neuroscience and clinical research studies.
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.