The role of Brain Connectivity 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 neurodegenerative diseases. As the field of neuroscience is constantly evolving, with multimodal imaging now considered the preferable method of evaluating different diseases and interventions, we have now expanded the remit of Brain Connectivity to ensure that we are able to include articles of a translational nature while maintaining the core of Brain Connectivity with outstanding methodological contributions.
With the view toward expanding the scope of Brain Connectivity, I would like to invite original articles and reviews describing:
The underlying mechanisms and influences on structural and functional connectivity behind neurological disease;
How different pathological substrates influence structural and functional connectivity in brain disorders;
Multimodal imaging in brain disorders in both human and animal models; and
Experimental techniques combining magnetic resonance imaging (MRI; connectivity), electroencephalography, magnetoencephalography, positron emission tomography, single photon emission computed tomography, and other new and evolving methods.
Original manuscripts, communications, and review articles are welcomed. Several subcategories under original articles and communications will be considered, including reports of original experimental data, methodological studies, novel data analysis schemes, theoretical data modeling, and descriptions of changes in brain connectivity in health and disease. We also welcome reports of original investigations in the areas of neuroscience, neurology, physics, biophysics, computer science, neuroinformatics, developmental biology, genetics, molecular biology, psychiatry, pharmacology, and anesthesiology, all of which influence brain connectivity.
In this issue, you will find several high-quality articles by experts in their fields:
Atypical Relationships Between Spontaneous EEG and fMRI Activity in Autism. Autism spectrum disorders (ASDs) have been linked to atypical communication among distributed brain networks. However, despite decades of research, the exact nature of differences between typically developing (TD) individuals and those with ASDs remains unclear. In the current study, Lisa E. Mash, Ralph-Axel Müller, and colleagues studied two cohorts of children and adolescents who underwent resting-state electroencephalogram (EEG) or functional magnetic resonance imaging (fMRI) with a subset of individuals in both the EEG and fMRI cohorts. In the EEG cohort, occipitoparietal EEG alpha power was found to be reduced in those with ASD. In the fMRI cohort, blood-oxygen-level-dependent (BOLD) power was regionally increased in right temporal regions, and there was widespread overconnectivity between the thalamus and cortical regions in the ASD group relative to the TD group. Finally, multimodal analyses found that while TD children showed consistently positive relationships between EEG alpha power and regional BOLD power, these associations were weak or negative in those with ASD. These findings suggest atypical links between alpha rhythms and regional BOLD activity in those with ASD, possibly implicating neural substrates and processes that coordinate thalamocortical regulation of the alpha rhythm.
Potential for Resting-State fMRI of the Amygdala in Elucidating Neurological Mechanisms of Adaptive Self- Regulatory Strategies: A Systematic Review. Shannon M. Warren and Horst Dieter Steklisc along with their colleagues explore the potential of resting state fMRI (RS-fMRI) of the amygdala for advancing research on the neural mechanism underlying adaptive strategies developed in an early adverse environment. The authors highlight how developmental theories consider the evolved mechanisms underlying adaptive behavioral strategies in response to environmental cues. Identifying neural mechanisms mediating processes of conditional adaptation in humans is an active area of research. RS-fMRI captures functional connectivity theorized to represent the underlying functional architecture of the brain. This allows how underlying functional brain connections are related to early experiences during development, as well as current traits and behaviors, to be investigated. This review explores the potential of RS-fMRI of the amygdala for advancing research on the neural mechanisms underlying adaptive strategies developed in early adverse environments. RS-fMRI studies of early life stress and amygdala functional connectivity within the frame of evolutionary theories are reviewed, specifically regarding the development of self-regulatory strategies. The potential of RS-fMRI for investigating the effects of early life stress on developmental trajectories of self-regulation is discussed.
Graph Theory Analysis of Functional Connectivity Combined with Machine Learning Approaches Demonstrate Widespread Network Differences and Predict Clinical Variables in Temporal Lobe Epilepsy. In this study, Mohsen Mazrooyisebdani and Raheel Ahmed along with their colleagues investigated functional changes within neural networks in temporal lobe epilepsy (TLE) using graph theory analysis of resting-state connectivity. Understanding how global brain networks are affected in epilepsy allows us to understand the pathogenesis of seizures and accompanying neurobehavioral comorbidities. The authors evaluated 27 TLE presurgical patients and 85 age-, sex-, and handedness-matched healthy controls (HC). RS-fMRI scans were analyzed to compare network properties and functional connectivity changes. TLE subjects showed significantly higher global efficiency, a lower clustering coefficient ratio, and a lower shortest path lengths ratio compared to HC as an indication of a more synchronized yet less segregated network. A trend of functional reorganization with a shift of network hubs to the contralateral hemisphere was noted in TLE subjects. A support vector machine (SVM) with a linear kernel was trained to separate between neural networks in TLE and HC subjects based on graph measurements. SVM analysis allowed separation between TLE and HC networks with 81% accuracy using eight features of graph measurements. Support vector regression (SVR) was also used to predict neurocognitive performance from graph metrics. A SVR linear predictor showed discriminative prediction accuracy for four key neurocognitive variables in TLE. The results showed both local and global network topology differences that reflect widespread alterations in functional connectivity in TLE. Network differences are discriminative between TLE and HCs using data-driven analysis and predicted severity of neurocognitive sequelae in this cohort.
Human Brain Functional Network Organization Is Disrupted Following Whole-Brain Radiation Therapy. Timothy J. Mitchell and Eric C. Leuthardt evaluate the influence of whole-brain radiation therapy on functional network organization. Radiation therapy plays a vital role in the treatment of brain cancers but frequently results in cognitive decline in the patients who receive it. However, the underlying mechanisms for this decline remain poorly understood. The brain is typically treated as a single, uniform volume when evaluating the toxic effects of radiation therapy plans. In this study, the authors investigate the influence of a uniform dose of radiation across the whole brain on RS-fMRI scans before and after radiation therapy. They observed substantial changes in the subject's behavior and functional network organization over a 12-month time frame. Interestingly, the homogenous radiation dose to the brain had a heterogeneous effect on cortical networks, and the functional networks most affected correspond with observed cognitive behavioral deficits. This novel study suggests that the cognitive decline that occurs after whole-brain radiation therapy may be network specific and related to the disruption of large-scale distributed functional systems, and indicates that fMRI is a promising avenue of study for optimizing cognitive outcomes following radiation therapy.
Finally, I would like to wish all readers of Brain Connectivity a very happy 2020, and hope that many of you will consider submitting your research papers to the journal this year.
