Abstract
Brain Connectivity has expanded its remit to provide comprehensive coverage of articles in clinical neurology, neuroscience, and neuroimaging. Although neuroimaging plays an important role in the diagnosis and management of different neurological disorders, by integrating neuroimaging with articles focusing on clinical neurology and neuroscience gives an opportunity for the journal audience to have a comprehensive knowledge of topics covered in Brain Connectivity. Involvement of the brain in different neurological diseases is accompanied by different molecular changes and neuropathological processes.
Pathological substrates such as amyloid deposition, tau deposition, microglial activation, synuclein pathology, astrocyte activation, mitochondrial function, and other changes in structural and functional connectivity (FC) are closely interrelated. By expanding the remit of Brain Connectivity, we hope to provide comprehensive cover of individual pathological process in different neurological disorders.
The multiorgan involvement in COVID-19 causes significant involvement of the brain along with other organs. The damage caused by the virus directly or indirectly can lead to disruption of the integrity of structural and FC by different mechanisms. The impact of COVID-19 on the nervous system needs further evaluation. At Brain Connectivity, being one of the leading journals in the field of neuroscience, we are now inviting articles addressing central nervous system involvement in COVID-19.
After the expansion of the remit of Brain Connectivity, we are now inviting articles of a translational nature in the field of clinical neurology, neuroscience, and neuroimaging by focusing on four special issues on: ▪ Alzheimer's disease and other neurodegenerative diseases, ▪ stroke, ▪ Parkinson's disease and other movement disorders, and ▪ neurological complications of COVID-19.
We invite you to submit articles focusing on the mentioned theme. Any of the following themes will be of huge interest:
▪ clinical and translational research and review articles,
▪ novel positron emission tomography (PET) and magnetic resonance imaging (MRI) biomarkers in neurodegenerative diseases and stroke,
▪ influence of genetic and epigenetic factors on structural and FC in brain disorders,
▪ multimodal imaging in brain disorders in both human subjects and animal models, and
▪ experimental techniques combining MRI (connectivity), electroencephalography (EEG), magnetoencephalography, 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
In this issue you will find several high-quality articles by experts in their fields.
Age-Related Differences of Frequency-Dependent Functional Connectivity in Brain Networks and Their Link to Motor Performance (https://doi.org/10.1089/brain.2021.0135 )
Aging affects the brain at anatomical and functional levels, resulting in a decline in motor and cognitive performance. Functional magnetic resonance imaging (fMRI) studies documented lower connectivity within brain networks and higher connectivity between them, for older as compared with young adults. In an attempt to understand the neurophysiological underpinnings, Jessica Samogin and Dante Mantini along with their colleagues collected high-density electroencephalography (hdEEG) data in 24 young and 24 older adults at rest.
Using the hdEEG data, they reconstructed oscillatory power and FC for six large-scale brain networks in delta, theta, alpha, beta, and gamma frequency bands. They observed that the level of network segregation generally decreased with age, in line with fMRI findings. However, there was a relatively strong dependence on the frequency band and the brain network being considered. EEG connectivity in the sensorimotor network predicted motor performance differences across older individuals, particularly when neural oscillations in the beta frequency band were considered. Hence, the authors provide evidence in support of the “de-differentiation hypothesis” for the aging brain and for the existence of a clear link between the strength of EEG connectivity at rest and motor performance.
Predicting Treatment Selections for Individuals with Major Depressive Disorder According to Functional Connectivity Subgroups (https://doi.org/10.1089/brain.2021.0153 )
Major depressive disorder (MDD) is a highly prevalent and disabling disease. Currently, patients' treatment choices depend on their clinical symptoms observed by clinicians, which are subjective, and evidence suggests that different functional networks' dysfunctions correspond to different intervention preferences. In this study, Xinyi Wang and Quing Lu along with their colleagues aimed to develop a prediction model based on data-driven subgroups.
Large number of subjects underwent fMRI at baseline. In the discovery data set they first identified MDD subgroups by the hierarchical clustering method using the canonical variates of resting-state FC through canonical correlation analyses. The demographic, symptom improvement, and FC were compared among subgroups along with the preference intervention. Then they predicted the individual treatment strategy.
Three subgroups with specific treatment recommendations were emerged including (1) a selective serotonin reuptake inhibitors-oriented subgroup with early improvements in working and activities, (2) a stimulation-oriented subgroup with more alleviation in suicide, (3) a selective serotonin noradrenaline reuptake inhibitors-oriented subgroup with more alleviation in hypochondriasis. Through cross-data set testing, respectively, conducted on three testing data sets, results showed an overall accuracy of 72.83%.
The authors revealed correspondences between subgroups and their treatment preferences and predicted individual treatment strategy based on such correspondences. They conclude that their model has the potential to support psychiatrists in early clinical decision making for better treatment outcomes.
Brain Connectivity Changes in Postconcussion Syndrome as the Neural Substrate of a Heterogeneous Syndrome (https://doi.org/10.1089/brain.2021.0127 )
Postconcussion syndrome (PCS) or persistent symptoms of concussion refer to a constellation of symptoms that persist for weeks and months after a concussion. To better capture the heterogeneity of the symptoms of patients with PCS, Melisa Gumus and Maria Carmela Tartaglia along with their colleagues aimed to separate patients into clinical subtypes based on brain connectivity changes.
Subject-specific structural and functional connectomes were created based on diffusion weighted and resting state functional magnetic resonance imaging, respectively. After an informed dimensionality reduction, a Gaussian mixture model was used on patient-specific structural and FC matrices to find potential patient clusters. The resulting patient subtypes were compared in terms of cognitive, neuropsychiatric, and postconcussive symptom differences.
They demonstrated that multimodal analyses of brain connectivity were predictive of behavioral outcomes. Their modeling revealed two patient subtypes: mild and severe. The severe group showed significantly higher levels of depression, anxiety, aggression, and a greater number of symptoms than the mild patient subgroup.
They conclude that structural and FC changes can help us better understand the symptom severity and neuropsychiatric profiles of patients with PCS, which will allow us to move toward precision medicine in concussions and provides a novel machine learning approach that can be applied to other heterogeneous conditions.
Adaptive and Maladaptive Brain Functional Network Reorganization After Stroke in Hemianopia Patients: An EEG-Tracking Study (https://doi.org/10.1089/brain.2021.0145 )
Hemianopia after occipital stroke is believed to be mainly due to local damage at or near the lesion site. Yet, MRI studies suggest functional connectivity network (FCN) reorganization in distant brain regions. Because it is unclear whether reorganization is adaptive or maladaptive, compensating for, or aggravating vision loss, Jiahua Xu and Bernahard A. Sabel along with their colleagues characterized FCNs electrophysiologically to explore local and global brain plasticity and correlated FCN reorganization with visual performance.
They recorded resting-state EEG in chronic unilateral stroke patients and healthy age-matched controls. The correlation of oscillating EEG activity was calculated with the imaginary part of coherence between pairs of interested regions, and FCN graph theory metrics (degree, strength, and clustering coefficient) were correlated with stimulus detection and reaction time.
Stroke brains showed altered FCNs in the alpha band and beta band in numerous occipital, temporal, and frontal brain structures. On a global level, FCN had a less efficient network organization while on the local level node networks reorganized especially in the intact hemisphere. Here, the occipital network was more rigid (with a more “regular” network structure) while the temporal network was more efficient (showing greater “small-worldness”). The authors conclude that occipital stroke is associated with local and global FCN reorganization, and this can be both adaptive and maladaptive.
Reduction of Motion Artifacts in Functional Connectivity Resulting from Infrequent Large Motion (https://doi.org/10.1089/brain.2021.0133 )
Rasmus Birn, Douglas Dean III, William Wooten, Elizabeth Planalp, Steven Kecskemeti, Andrew Alexander, H. Hill Goldsmith, and Richard Davidson
Subject head motion is an ongoing challenge in fMRI, particularly in the estimation of FC. Head movement results in residual signal changes even after image realignment, and can distort estimates of FC. Rasmus M. Birn and Richard J. Davidson introduced new motion correction technique, JumpCor, to reduce the effects of this motion and compared with other existing techniques.
Motion-related signal changes resulting from infrequent large motion were significantly reduced both by regressing out the estimated motion parameters and by JumpCor. Furthermore, JumpCor significantly reduced artifacts and improved the quality of FC estimates when combined with typical preprocessing approaches. They conclude that motion-related signal changes resulting from occasional large motion can be effectively corrected using JumpCor, and to a certain extent also by regressing out the estimated motion, and thus should reduce the data loss in studies where participants exhibit this type of motion, such as sleeping infants.
Structural Brain Network Reproducibility: Influence of Different Diffusion Acquisition and Tractography Reconstruction Schemes on Graph Metrics (https://doi.org/10.1089/brain.2021.0123 )
Graph metrics of structural brain networks is a powerful tool for investigating brain topology at a large scale. However, the variability of the results related to applying different magnetic resonance acquisition schemes and tractography reconstruction techniques is not fully characterized.
In this study, Pasquale Borrelli and Marco Aiello along with their colleagues aim to evaluate the influence of different combinations of diffusion acquisition schemes (single and multishell), diffusion models (tensor and spherical deconvolution), and tractography reconstruction approaches (deterministic and probabilistic) on the reproducibility of graph metrics derived from structural connectome on test–retest data released by the Human Connectome Project. From each implemented experimental setup, both global and local graph metrics were evaluated and their reproducibility was estimated by the intraclass correlation coefficient (ICC). Moreover, the percentage relative standard deviation (pRSD) from the ICC values of local graph metrics was calculated.
They demonstrate that different combinations of diffusion acquisition schemes, diffusion models, and tractography algorithms can strongly affect the reproducibility of global and local graph metrics. The combination of constrained spherical deconvolution (CSD) and deterministic tractography gave generally high reproducibility (ICCs >0.75) and lowest pRSD for the considered graph metrics, meanwhile probabilistic CSD with high b-value returned the highest reproducibility. The authors conclude that the test–retest reproducibility of graph metrics is generally high but can vary substantially with different combinations of acquisition and reconstruction schemes.
Finally, I thank all the researchers and all the staff at Mary Ann Liebert, Inc., Publishers, editors and reviewers of Brain Connectivity who are dedicated to advancing research and improving our lives in every corner of the world.