Macroscale Structural Connectivity Research Articles

Development of Brain Structural Networks Over Age 8: A Preliminary Study Based on Diffusion Weighted Imaging.

Brain structural network changes provide key information about the aging process of the brain. Unfortunately, there has yet to be a detailed characterization of these structural networks across different age groups. Efforts to classify these networks have also been hampered by their reliance on technically limited traditional methods, which are unable to track multiple fiber orientations within a voxel and consequently are prone to false detection and artifacts. In this study, a newly developed Ensemble Average Propagator (EAP) based probabilistic tractography method was applied to construct a structural network, with the strength of the link between any two brain functional regions estimated according to the alignment of the EAP along connecting pathways. Age-related changes in the topological organization of human brain structural networks were thereby characterized across a broad age range (ages 8–75 years). The data from 48 healthy participants were divided into four age groups (Group 1 aged 8–15 years; Group 2 aged 25–35 years; Group 3 aged 45–55 years; and, Group 4 aged 65–75 years; N = 12 per group). We found that the brain structural network continues to strengthen during later adolescence and adulthood, through the first 20–30 years of life. Older adults, aged 65–75, had a significantly less optimized topological organization in their structural network, with decreased global efficiency and increased path lengths versus subjects in other groups. This study suggests that probabilistic tractography based on EAP provides a reliable method to construct macroscale structural connectivity networks to capture the age-associated changes of brain structures.

Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature

The development of human cognition results from the emergence of coordinated activity between distant brain areas. Network science, combined with non-invasive functional imaging, has generated unprecedented insights regarding the adult brain's functional organization, and promises to help elucidate the development of functional architectures supporting complex behavior. Here we review what is known about functional network development from birth until adulthood, particularly as understood through the use of resting-state functional connectivity MRI (rs-fcMRI). We attempt to synthesize rs-fcMRI findings with other functional imaging techniques, with macro-scale structural connectivity, and with knowledge regarding the development of micro-scale structure. We highlight a number of outstanding conceptual and technical barriers that need to be addressed, as well as previous developmental findings that may need to be revisited. Finally, we discuss key areas ripe for future research in order to (1) better characterize normative developmental trajectories, (2) link these trajectories to biologic mechanistic events, as well as component behaviors and (3) better understand the clinical implications and pathophysiological basis of aberrant network development.

The flexible brain.

This scientific commentary refers to ‘Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders’ by Zhang et al. (doi:10.1093/aww143). The vastness of the brain’s dynamic repertoire is one of the remarkable features of brain function, making it possible to adapt rapidly and efficiently to external task demands, implement novel behaviours, and switch from one task to another. Variability in the neural dynamics is, nonetheless, constrained and displays heterogeneous topography—specific regions appear more or less variable over time, both in terms of their activity time courses (Garrett et al. , 2011) and also in terms of their interactions with other brain regions (their functional connectivity). In this issue of Brain , Zhang and co-workers present a novel method for characterizing the temporal variability of a region’s functional connectivity profile [estimated from blood oxygen level-dependent (BOLD) functional MRI], relating this variability to the region’s electrophysiology (measured with electroencephalography; EEG) and to its macroscale structural connectivity (white-matter pathways), and further demonstrating its potential utility as a neural marker for mental disorders (Zhang et al. , 2016). The past decade has witnessed a burgeoning interest in the functional network architecture of the human brain. Most of these earlier studies have adopted a ‘static’ point of view, wherein functional connections between regions are characterized over long time scales, obscuring faster dynamics. More recently, however, it has become apparent that over the course of seconds to minutes, the human brain displays network-wide reconfigurations both at rest (Zalesky et al. , 2014) and during task performance (Braun et al. , 2015). These findings …

CONNECTOMICS SIGNATURE FOR CHARACTERIZATON OF MILD COGNITIVE IMPAIRMENT AND SCHIZOPHRENIA.

Human connectomes constructed via neuroimaging data offer a comprehensive description of the macro-scale structural connectivity within the brain. Thus quantitative assessment of connectome-scale structural and functional connectivities will not only fundamentally advance our understanding of normal brain organization and function, but also have significant importance to systematically and comprehensively characterize many devastating brain conditions. In recognition of the importance of connectome and connectomics, in this paper, we develop and evaluate a novel computational framework to construct structural connectomes from diffusion tensor imaging (DTI) data and assess connectome-scale functional connectivity alterations in mild cognitive impairment (MCI) and schizophrenia (SZ) from concurrent resting state fMRI (R-fMRI) data, in comparison with their healthy controls. By applying effective feature selection approaches, we discovered informative and robust functional connectomics signatures that can distinctively characterize and successfully differentiate the two brain conditions of MCI and SZ from their healthy controls (classification accuracies are 96% and 100%, respectively). Our results suggest that connectomics signatures could be a general, powerful methodology for characterization and classification of many brain conditions in the future.

Comparative Analysis of the Macroscale Structural Connectivity in the Macaque and Human Brain

The macaque brain serves as a model for the human brain, but its suitability is challenged by unique human features, including connectivity reconfigurations, which emerged during primate evolution. We perform a quantitative comparative analysis of the whole brain macroscale structural connectivity of the two species. Our findings suggest that the human and macaque brain as a whole are similarly wired. A region-wise analysis reveals many interspecies similarities of connectivity patterns, but also lack thereof, primarily involving cingulate regions. We unravel a common structural backbone in both species involving a highly overlapping set of regions. This structural backbone, important for mediating information across the brain, seems to constitute a feature of the primate brain persevering evolution. Our findings illustrate novel evolutionary aspects at the macroscale connectivity level and offer a quantitative translational bridge between macaque and human research.