Abstract
In recent years, connectomics—the study of whole-brain maps of connectivity—has become a popular method in systems-level neuroscience. Commonly referred to as the brain connectome, it has been used to focus on quantifying, visualizing, and understanding brain network organization, including their applications in neuroimaging. It is being used to study the structural and functional organization in healthy and clinical application. The primary objective of this special issue is to bring together computational researchers (computer scientists, data scientists, computation neuroscientists, etc.) to present new advancements in network construction, analysis, and visualization techniques in connectomics and their use in clinical diagnosis and group comparison studies.
We are indebted to the Guest Editors, Drs. Brent C. Munsell (College of Charleston), Guorong Wu (University of North Carolina at Chapel Hill), Leonardo Bonilha (Medical University of South Carolina), and Paul J. Laurienti (Wake Forest School of Medicine) who helped to make this special issue possible. They assisted in all aspects of this issue, including identifying potential topics, reviewing articles, giving expert advice, and selecting the final articles.
In the two issues, a total of 17 articles covering all aspects of connectomics have been selected for publication. A number of novel methods, mechanisms that give rise and can modulate connectomics, and clinical applications where differences in connectomics use different modalities have been covered.
In the first article, Levman and colleagues analyzed the typical development of insular connections in a large-scale pediatric population using 642 diffusion tensor imaging to illustrate the investigative potential of performing connectomics-style analyses in a clinical context across a large population of children as part of routine imaging. They demonstrate the feasibility of using current technologies to perform regionally focused clinical connectivity studies.
Palande and colleagues applied topological data analysis in conjunction with structural covariance magnetic resonance imaging (MRI) to explore network-specific differences in the gray-matter structure in subjects with autism spectrum disorder (ASD) compared to age-, sex-, and IQ-matched controls. They demonstrate that combining topological data analysis with statistical inference provides statistically significant evidence of network-specific structural abnormalities.
Lisowska and Rekik propose a joint morphological brain multiplexes pairing and mapping strategy for the early detection of mild cognitive impairment, where a brain multiplex not only encodes the relationship in morphology between pairs of brain regions, but also a pair of brain morphological networks. This was experimentally validated using the ADNI data sets.
Petersen and colleagues introduce and use correlation densities to quantify and provide visual interpretation for intra-regional functional connectivity in the brain. The utility of these methods in neuroimaging is demonstrated across a number of cognitive tasks.
Anteraper and colleagues used high temporal resolution resting state functional MRI studies using multi-voxel pattern analysis (MVPA) to revealed two clusters of abnormal connectivity in the cerebellum. Whole-brain seed-based functional connectivity analyses informed by MVPA-derived clusters showed significant under connectivity between the cerebellum and social, emotional, and language brain regions in the high-functioning ASD group compared to healthy controls.
Mennigen and colleagues demonstrate the dynamic behavior of connectivity with regard to functional network connectivity. Dynamic indexes appeared to be altered in early illness schizophrenia patients and is mostly intact in clinical high risk for psychosis individuals.
Fan and colleagues demonstrate that it is crucial for future seed-based functional connectivity studies to consider these two subregions separately in terms of seed location and discussion of findings. Their findings highlight the functional importance of connectivity changes toward regions outside the canonical networks.
Finally, Mokhtari and colleagues provide guidance on how to interpret the spatial matrixes and spatial maps that result from data decomposition approaches. As part of this goal, they examine how decomposition methods and data structure interact to affect the ultimate interpretation of the results.
It is hoped that the wide variety of articles published will prompt further interest and collaboration in the field of connectomics.