Brain-wide functional architecture remodeling by alcohol dependence and abstinence

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Abstract

Alcohol abuse and alcohol dependence are key factors in the development of alcohol use disorder, which is a pervasive societal problem with substantial economic, medical, and psychiatric consequences. Although our understanding of the neurocircuitry that underlies alcohol use has improved, novel brain regions that are involved in alcohol use and novel biomarkers of alcohol use need to be identified. The present study used a single-cell whole-brain imaging approach to 1) assess whether abstinence from alcohol in an animal model of alcohol dependence alters the functional architecture of brain activity and modularity, 2) validate our current knowledge of the neurocircuitry of alcohol abstinence, and 3) discover brain regions that may be involved in alcohol use. Alcohol abstinence resulted in the whole-brain reorganization of functional architecture in mice and a pronounced decrease in modularity that was not observed in nondependent moderate drinkers. Structuring of the alcohol abstinence network revealed three major brain modules: 1) extended amygdala module, 2) midbrain striatal module, and 3) cortico-hippocampo-thalamic module, reminiscent of the three-stage theory. Many hub brain regions that control this network were identified, including several that have been previously overlooked in alcohol research. These results identify brain targets for future research and demonstrate that alcohol use and dependence remodel brain-wide functional architecture to decrease modularity. Further studies are needed to determine whether the changes in coactivation and modularity that are associated with alcohol abstinence are causal features of alcohol dependence or a consequence of excessive drinking and alcohol exposure.

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A mesoscale connectome of the mouse brain.

Seung Wook Oh, Julie Harris, Lydia Ng … (2014)

Comprehensive knowledge of the brain's wiring diagram is fundamental for understanding how the nervous system processes information at both local and global scales. However, with the singular exception of the C. elegans microscale connectome, there are no complete connectivity data sets in other species. Here we report a brain-wide, cellular-level, mesoscale connectome for the mouse. The Allen Mouse Brain Connectivity Atlas uses enhanced green fluorescent protein (EGFP)-expressing adeno-associated viral vectors to trace axonal projections from defined regions and cell types, and high-throughput serial two-photon tomography to image the EGFP-labelled axons throughout the brain. This systematic and standardized approach allows spatial registration of individual experiments into a common three dimensional (3D) reference space, resulting in a whole-brain connectivity matrix. A computational model yields insights into connectional strength distribution, symmetry and other network properties. Virtual tractography illustrates 3D topography among interconnected regions. Cortico-thalamic pathway analysis demonstrates segregation and integration of parallel pathways. The Allen Mouse Brain Connectivity Atlas is a freely available, foundational resource for structural and functional investigations into the neural circuits that support behavioural and cognitive processes in health and disease.

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Scale-free networks: a decade and beyond.

Albert-László Barabási (2009)

For decades, we tacitly assumed that the components of such complex systems as the cell, the society, or the Internet are randomly wired together. In the past decade, an avalanche of research has shown that many real networks, independent of their age, function, and scope, converge to similar architectures, a universality that allowed researchers from different disciplines to embrace network theory as a common paradigm. The decade-old discovery of scale-free networks was one of those events that had helped catalyze the emergence of network science, a new research field with its distinct set of challenges and accomplishments.

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Identification and Classification of Hubs in Brain Networks

Olaf Sporns, Christopher Honey, Rolf Kötter (2007)

Brain regions in the mammalian cerebral cortex are linked by a complex network of fiber bundles. These inter-regional networks have previously been analyzed in terms of their node degree, structural motif, path length and clustering coefficient distributions. In this paper we focus on the identification and classification of hub regions, which are thought to play pivotal roles in the coordination of information flow. We identify hubs and characterize their network contributions by examining motif fingerprints and centrality indices for all regions within the cerebral cortices of both the cat and the macaque. Motif fingerprints capture the statistics of local connection patterns, while measures of centrality identify regions that lie on many of the shortest paths between parts of the network. Within both cat and macaque networks, we find that a combination of degree, motif participation, betweenness centrality and closeness centrality allows for reliable identification of hub regions, many of which have previously been functionally classified as polysensory or multimodal. We then classify hubs as either provincial (intra-cluster) hubs or connector (inter-cluster) hubs, and proceed to show that lesioning hubs of each type from the network produces opposite effects on the small-world index. Our study presents an approach to the identification and classification of putative hub regions in brain networks on the basis of multiple network attributes and charts potential links between the structural embedding of such regions and their functional roles.