Advanced database methodology for the Collation of Connectivity data on the Macaque brain (CoCoMac).

By K. E. Stephan, L. Kamper, A. Bozkurt, Gully A. Burns, M. Young, Rolf Kötter. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2001

375 citations recorded by the source corpus.

The need to integrate massively increasing amounts of data on the mammalian brain has driven several ambitious neuroscientific database projects that were started during the last decade. Databasing the brain's anatomical connectivity as delivered by tracing studies is of particular importance as these data characterize fundamental structural constraints of the complex and poorly understood functional interactions between the components of real neural systems. Previous connectivity databases have been crucial for analysing anatomical brain circuitry in various species and have opened exciting new ways to interpret functional data, both from electrophysiological and from functional imaging studies. The eventual impact and success of connectivity databases, however, will require the resolution of several methodological problems that currently limit their use. These problems comprise four main points: (i) objective representation of coordinate-free, parcellation-based data, (ii) assessment of the reliability and precision of individual data, especially in the presence of contradictory reports, (iii) data mining and integration of large sets of partially redundant and contradictory data, and (iv) automatic and reproducible transformation of data between incongruent brain maps. Here, we present the specific implementation of the 'collation of connectivity data on the macaque brain' (CoCoMac) database (http://www.cocomac.org). The design of this database addresses the methodological challenges listed above, and focuses on experimental and computational neuroscientists' needs to flexibly analyse and process the large amount of published experimental data from tracing studies. In this article, we explain step-by-step the conceptual rationale and methodology of CoCoMac and demonstrate its practical use by an analysis of connectivity in the prefrontal cortex.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/4bc48b1f3237c8c5f59c9af185b8f064aab4efe9

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Citations in the corpus

Identification and Classification of Hubs in Brain Networks (2007, methodology): “. The data were manually collated in the CoCoMac database from published tracing studies according to standard procedures”
Communication Efficiency and Congestion of Signal Traffic in Large-Scale Brain Networks (2014, methodology): “The anatomical connectivity data set used in the present study was derived from the online Collation of Connectivity data on the Macaque brain (CoCoMac) database, comprised of data from 413 tract tracing studies of the macaque [46,47].”
Dynamic models of large-scale brain activity (2017, background): “Suitable connec-tomic data can be obtained from collations of invasive tracing studies, such as the primate CoCoMac 56 or recent detailed rodent connec-tomes 57 .”
Comparing dynamic causal models (2004, background): “Decisions about the intrinsic connectivity of DCMs are therefore usually based on inferring connections from supposedly equivalent areas in the Macaque brain for which the anatomical connectivity is well known (Stephan et al., 2001).”
Situating the default-mode network along a principal gradient of macroscale cortical organization (2016, methodology): “The principal gradient was derived from human (39) and macaque (67, 68) connectivity matrices using diffusion embedding (42)—a nonlinear dimensionality reduction technique (Fig.”
Emerging concepts for the dynamical organization of resting-state activity in the brain (2010, positive): “The three following models address the characteristic spatial and temporal structure of RSNs observed in empirical data and use realistic neuroanatomical information from the macaque cortex provided by the CoCoMac neuroin-formatics tool 44,45 .”
A Weighted and Directed Interareal Connectivity Matrix for Macaque Cerebral Cortex (2012, methodology): “Keywords: connection, cortex, graph, monkey, network”
Diffusion MRI at 25: Exploring brain tissue structure and function (2012, background): “There is a vast literature on brain connectivity patterns derived from the macaque monkey (Stephan et al., 2001) and this has proved invaluable for study of the human brain in health and disease.”
Small-World Brain Networks Revisited (2016, background): “…connectivity, that was more continuously quantiﬁed than the binary or ordinal rating of connectivity from traditional tract-tracing experiments [67], and much less ambiguously related to the cellular substrates of brain networks than the statistical measures of functional connectivity [48, 68]…”
Routing in the brain (2014, background): “Now, a paper by Mišić et al. (2014) has simulated communication across a comprehensive macaque cortex anatomical model (CoCoMac: Stephan et al., 2001; Kötter, 2004).”
Modular Brain Networks (2015, methodology): “…mapped using various techniques, including reconstruction from serially sectioned electron micrographs (White et al. 1986) or the aggregation of data from anatomical tract-tracing experiments (Kötter et al. 2004, Markov et al. 2014, Oh et al. 2014, Scannell et al. 1995, Stephan et al. 2001).”
Multi-scale brain networks (2016, neutral): “Additionally, meta-analytic studies that aggregate and summarize the results of individual tracttracing experiments have produced convergent maps of macaque (Stephan et al., 2001) and rat (Bota et al.”
The Scalable Brain Atlas: Instant Web-Based Access to Public Brain Atlases and Related Content (2013, methodology): “…tool (CP3D, Bezgin et al. 2009), which is a Java-based platform that volume-renders brain regions taken from the Paxinos rhesus monkey atlas (Paxinos et al. 2000) and displays structural connectivity data from the CoCoMac database (Stephan et al. 2001, Kötter 2004) as directed arrows.”
Free-energy and the brain (2007, background): “The amount of reproducible anatomic information pertaining to the brain is now so vast it can only be organised electronically (e.g., Stephan et al 2001).”