Interoperable atlases of the human brain

Abstract

The last two decades have seen an unprecedented development of human brain mapping approaches at various spatial and temporal scales. Together, these have provided a large fundus of information on many different aspects of the human brain including micro-and macrostructural segregation, regional specialization of function, connectivity, and temporal dynamics. Atlases are central in order to integrate such diverse information in a topographically meaningful way. It is noteworthy, that the brain mapping field has been developed along several major lines such as structure vs. function, postmortem vs. in vivo, individual features of the brain vs. population-based aspects, or slow vs. fast dynamics. In order to understand human brain organization, however, it seems inevitable that these different lines are integrated and combined into a multimodal human brain model. To this aim, we held a workshop to determine the constraints of a multi-modal human brain model that are needed to enable (i) an integration of different spatial and temporal scales and data modalities into a common reference system, and (ii) efficient data exchange and analysis. As detailed in this report, to arrive at fully interoperable atlases of the human brain will still require much work at the frontiers of data acquisition, analysis, and representation. Among them, the latter may provide the most challenging task, in particular when it comes to representing features of vastly different scales of space, time and abstraction. The potential benefits of such endeavor, however, clearly outweigh the problems, as only such kind of multi-modal human brain atlas may provide a starting point from which the complex relationships between structure, function, and connectivity may be explored.

Connectivity‐based parcellation: Critique and implications

“…On the other hand, they are based on the problematic assumption [cf. Amunts et al, 2014] that different sources of information should yield the same parcellation of the brain. This has repeatedly been challenged and in fact can result in a strong confirmatory bias.…”

Method of Medical Images Similarity Estimation Based on Feature Analysis

“…where I is a given vector valued image, C is a closed contour in the image domain, K is a nonnegative kernel function, f 1 , f 2 are two values that approximate image intensities inside C and outside C respectively, λ 1 and λ 2 are positive constants [6]. The comparison of these methods enables concluding that Minimization of Region-Scalable Fitting Energy method is the most quick and accurate in search for contours of an image.…”

“…Medical images similarity estimation is an essential part of modern software tools used in numerous medical investigations including brain researches [1][2][3]. To develop software tools based on automated processing of images new effective approaches to feature extraction procedure are necessary.…”

Mining topological structures of protein-protein interaction networks for human brain-specific genes

“…Numerous research efforts have attempted to disclose the working mechanisms of the brain both from the viewpoint of physiological structure using imaging technologies (Amunts et al, 2014;Di Martino et al, 2014) and that of gene expression using genome-wide association studies (Hernandez et al, 2012;Medland et al, 2014).…”

“…The discovery of the key changes in genes or proteins that regulate the responses of the brain to external stimuli or lead to neuropsychiatric disorders can accelerate the pace of gene and drug treatments. Protein interaction is crucial for building the networks in the brain that underlie these phenomena (Amunts et al, 2014). Neural networks are formed by the interconnection of specific neurons in the brain.…”