Mapping the Connectome: Multi-Level Analysis of Brain Connectivity

AdvAnces in multi-level connectivity mApping Sophisticated neuroimaging techniques have opened up new possibilities to infer structural and functional connectivity at a macroscopic scale. Through measurement of oriented water diffusion restricted by cellular elements in the brain, non-invasive methods based on diffusion magnetic resonance imaging (dMRI, Figures 1A,B) play a key role in current neuroanatomical efforts to explore the human connectome (Hagmann et al., 2010; Van Essen and Ugurbil, 2012). The different dMRI tractography methods proposed so far still require time-consuming manual intervention and supervision that may compromise reliability. To overcome this problem, Yendiki et al. (2011) present a method for automated probabilistic reconstruction of white matter pathways that incorporates a priori anatomical knowledge, and demonstrate automatic tractography analyses in schizophrenia patients and healthy subjects (Figure 1B). The ability to perform dMRI tractography without manual intervention will greatly facilitate studies with very large populations, which will be essential for establishing a connectome for the human brain (Marcus et al., 2011) as well as for improving early diagnostic imaging in brain disease. Estimates of “functional networks” described on the basis of statistical associations derived from time series data (neuronal recordings) represent another important category of approaches to define the human brain connectome. The relationship of anatomical to functional networks is explored by Daffertshofer and van Wijk (2011). Using computational modeling of large-scale neural networks these authors argue that patterns of synchronization should be analyzed in the context of changes in local amplitude to improve prediction of brain dynamics from structure. In a related paper, Segall et al. (2012) also employ statistical methods and independent component analysis to describe spatial correspondences between gray matter density measurements and resting state functional MRI signal fluctuations recorded from a very large group of healthy subjects. But while associations between several structural and functional features can be observed (Segall et al., 2012), the anatomical substrates underlying such indirect in vivo measurements remain obscure and require further investigation. BAckground And scope The brain contains vast numbers of interconnected neurons that constitute anatomical and functional networks. Structural descriptions of neuronal network elements and connections make up the “connectome” of the brain (Hagmann, 2005; Sporns et al., 2005; Sporns, 2011), and are important for understanding normal brain function and disease-related dysfunction. A long-standing ambition of the neuroscience community has been to achieve complete connectome maps for the human brain as well as the brains of non-human primates, rodents, and other species (Bohland et al., 2009; Hagmann et al., 2010; Van Essen and Ugurbil, 2012). A wide repertoire of experimental tools is currently available to map neural connectivity at multiple levels, from the tracing of mesoscopic axonal connections and the delineation of white matter tracts (Saleem et al., 2002; Van der Linden et al., 2002; Sporns et al., 2005; Schmahmann et al., 2007; Hagmann et al., 2010), the mapping of neurons organized into functional circuits (Geerling and Loewy, 2006; Ohara et al., 2009; Thompson and Swanson, 2010; Ugolini, 2011), to the identification of cellular-level connections, and the molecular properties of individual synapses (Harris et al., 2003; Arellano et al., 2007; Staiger et al., 2009; Micheva et al., 2010; Wouterlood et al., 2011). But despite the numerous connectivity studies conducted through many decades we are still far from achieving comprehensive descriptions of the connectome across all these levels. There is increasing awareness that new neuroinformatics tools and strategies are needed to achieve the goal of compiling the brain’s connectome, and that any such effort will require systematic, large-scale approaches (Bohland et al., 2009; Akil et al., 2011; Zakiewicz et al., 2011; Van Essen and Ugurbil, 2012). Systematic literature mining to compile and share complete overview of known connections in the macaque brain was pioneered by Rolf Kötter and co-workers (Stephan et al., 2001, 2010). While yielding promising results (Kötter, 2004; Bota et al., 2005; van Strien et al., 2009), more coordinated efforts are needed to collect, organize, and disseminate connectome data sets. To this end, there is an urgent need to develop and identify neuroinformatics approaches that allow different levels of connectivity data to be described, integrated, compared, and shared within the broader neuroscience community. This Research Topic of Frontiers in Neuroinformatics, dedicated to the memory of Rolf Kötter (1961–2010) and his pioneering work in the field of brain connectomics, comprises contributions that elucidate different levels of connectivity analysis (from MRIbased methods, through axonal tracing techniques, to mapping of functional connectivity in relation to detailed 3-D reconstructions Mapping the connectome: multi-level analysis of brain connectivity