Claus C. Hilgetag

The challenge of mapping the human connectome based on diffusion tractography

Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations.Though tractography is widely used, it has not been systematically validated. Here, authors report results from 20 groups showing that many tractography algorithms produce both valid and invalid bundles.

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

Bridging Cytoarchitectonics and Connectomics in Human Cerebral Cortex

The rich variation in cytoarchitectonics of the human cortex is well known to play an important role in the differentiation of cortical information processing, with functional multimodal areas noted to display more branched, more spinous, and an overall more complex cytoarchitecture. In parallel, connectome studies have suggested that also the macroscale wiring profile of brain areas may have an important contribution in shaping neural processes; for example, multimodal areas have been noted to display an elaborate macroscale connectivity profile. However, how these two scales of brain connectivity are related—and perhaps interact—remains poorly understood. In this communication, we combined data from the detailed mappings of early twentieth century cytoarchitectonic pioneers Von Economo and Koskinas (1925) on the microscale cellular structure of the human cortex with data on macroscale connectome wiring as derived from high-resolution diffusion imaging data from the Human Connectome Project. In a cross-scale examination, we show evidence of a significant association between cytoarchitectonic features of human cortical organization—in particular the size of layer 3 neurons—and whole-brain corticocortical connectivity. Our findings suggest that aspects of microscale cytoarchitectonics and macroscale connectomics are related. SIGNIFICANCE STATEMENT One of the most widely known and perhaps most fundamental properties of the human cortex is its rich variation in cytoarchitectonics. At the same time, neuroimaging studies have also revealed cortical areas to vary in their level of macroscale connectivity. Here, we provide evidence that aspects of local cytoarchitecture are associated with aspects of global macroscale connectivity, providing insight into the question of how the scales of micro-organization and macro-organization of the human cortex are related.

Tractography-based connectomes are dominated by false-positive connections

Fiber tractography based on non-invasive diffusion imaging is at the heart of connectivity studies of the human brain. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain dataset with ground truth white matter tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. While most state-of-the-art algorithms reconstructed 90% of ground truth bundles to at least some extent, on average they produced four times more invalid than valid bundles. About half of the invalid bundles occurred systematically in the majority of submissions. Our results demonstrate fundamental ambiguities inherent to tract reconstruction methods based on diffusion orientation information, with critical consequences for the approach of diffusion tractography in particular and human connectivity studies in general.

Characterization of Visual Percepts Evoked by Noninvasive Stimulation of the Human Posterior Parietal Cortex

Phosphenes are commonly evoked by transcranial magnetic stimulation (TMS) to study the functional organization, connectivity, and excitability of the human visual brain. For years, phosphenes have been documented only from stimulating early visual areas (V1–V3) and a handful of specialized visual regions (V4, V5/MT+) in occipital cortex. Recently, phosphenes were reported after applying TMS to a region of posterior parietal cortex involved in the top-down modulation of visuo-spatial processing. In the present study, we systematically characterized parietal phosphenes to determine if they are generated directly by local mechanisms or emerge through indirect activation of other visual areas. Using technology developed in-house to record the subjective features of phosphenes, we found no systematic differences in the size, shape, location, or frame-of-reference of parietal phosphenes when compared to their occipital counterparts. In a second experiment, discrete deactivation by 1 Hz repetitive TMS yielded a double dissociation: phosphene thresholds increased at the deactivated site without producing a corresponding change at the non-deactivated location. Overall, the commonalities of parietal and occipital phosphenes, and our ability to independently modulate their excitability thresholds, lead us to conclude that they share a common neural basis that is separate from either of the stimulated regions.

Modeling of Large-Scale Functional Brain Networks Based on Structural Connectivity from DTI: Comparison with EEG Derived Phase Coupling Networks and Evaluation of Alternative Methods along the Modeling Path.

In this study, we investigate if phase-locking of fast oscillatory activity relies on the anatomical skeleton and if simple computational models informed by structural connectivity can help further to explain missing links in the structure-function relationship. We use diffusion tensor imaging data and alpha band-limited EEG signal recorded in a group of healthy individuals. Our results show that about 23.4% of the variance in empirical networks of resting-state functional connectivity is explained by the underlying white matter architecture. Simulating functional connectivity using a simple computational model based on the structural connectivity can increase the match to 45.4%. In a second step, we use our modeling framework to explore several technical alternatives along the modeling path. First, we find that an augmentation of homotopic connections in the structural connectivity matrix improves the link to functional connectivity while a correction for fiber distance slightly decreases the performance of the model. Second, a more complex computational model based on Kuramoto oscillators leads to a slight improvement of the model fit. Third, we show that the comparison of modeled and empirical functional connectivity at source level is much more specific for the underlying structural connectivity. However, different source reconstruction algorithms gave comparable results. Of note, as the fourth finding, the model fit was much better if zero-phase lag components were preserved in the empirical functional connectome, indicating a considerable amount of functionally relevant synchrony taking place with near zero or zero-phase lag. The combination of the best performing alternatives at each stage in the pipeline results in a model that explains 54.4% of the variance in the empirical EEG functional connectivity. Our study shows that large-scale brain circuits of fast neural network synchrony strongly rely upon the structural connectome and simple computational models of neural activity can explain missing links in the structure-function relationship.

Cytoarchitectonic similarity is a wiring principle of the human connectome

Understanding the wiring diagram of the human cerebral cortex is a fundamental challenge in neuroscience. Elemental aspects of its organization remain elusive. Here we examine which structural traits of cortical regions, particularly their cytoarchitecture and thickness, relate to the existence and strength of inter-regional connections. We use the architecture data from the classic work of von Economo and Koskinas and state-of-the-art diffusion-based connectivity data from the Human Connectome Project. Our results reveal a prominent role of the cytoarchitectonic similarity of supragranular layers for predicting the existence and strength of connections. In contrast, cortical thickness similarity was not related to the existence or strength of connections. These results are in line with findings for non-human mammalian cerebral cortices, suggesting overarching wiring principles of the mammalian cerebral cortex. The results invite hypotheses about evolutionary conserved neurobiological mechanisms that give rise to the relation of cytoarchitecture and connectivity in the human cerebral cortex.

Brain network science needs to become predictive Comment on "Understanding brain networks and brain organization" by Luiz Pessoa

In his thought-provoking review of current concepts in neuroscience, Pessoa [1] addresses the ongoing paradigm shift of the field, in which the perspective has moved from individual nodes to distributed networks in order to account for distributed brain function. Within this perspective, Pessoa describes diverse aspects and topological features of brain networks that are potentially relevant for brain function. As he notes, however, the shift to networks does not solve all problems of linking brain function to structure. In particular, Pessoa considers one issue to be central, the correspondence problem of mapping many functions to many elements, where a single element may contribute to multiple functions, and a particular function may involve many system elements. This problem is not simply resolved by attributing function to network features, such as modules, hubs, rich clubs, etc., instead of nodes. Nonetheless, it seems that the problem can be addressed in a straightforward manner by defining indices for participation, similar to the diversity profiles described by Pessoa, or comparable measures for localization and specialization derived from functional contribution matrices, e.g. [2]. In such approaches, which consider the functional contributions of a set of neural elements to a set of tasks, elements that are contributing highly and significantly to a particular task can be said to be specialized for the task, while functions that show particularly high involvement of some elements might be said to be localized in these elements. (Naturally, as also pointed out by Pessoa, an important question is how functions of neural elements should be defined in the first place. Any definition that is not matched to the actual biophysical, computational or functional properties of the elements is of little value [3].) While the correspondence problem may be addressed in a straightforward way, there exist some other, fundamental problems of the network perspective, of which we outline two. First, the role of statistical procedures in network research needs to be discussed on a much more fundamental level. Current research into brain networks is largely exploratory and focuses on issues such as computing centrality indices or evaluating whether the brain possesses small world features. It is important to note that there is little value per se in such explorative findings. The purpose of exploratory research is to help forming intuitions about the

Influence of stimulus type on effects of flanker, flanker position, and trial sequence in a saccadic eye movement task.

Using the flanker paradigm in a task requiring eye movement responses, we examined how stimulus type (arrows vs. letters) modulated effects of flanker and flanker position. Further, we examined trial sequence effects and the impact of stimulus type on these effects. Participants responded to a central target with a left- or rightward saccade. We reasoned that arrows, being overlearned symbols of direction, are processed with less effort and are therefore linked more easily to a direction and a required response than are letters. The main findings demonstrate that (a) flanker effects were stronger for arrows than for letters, (b) flanker position more strongly modulated the flanker effect for letters than for arrows, and (c) trial sequence effects partly differed between the two stimulus types. We discuss these findings in the context of a more automatic and effortless processing of arrow relative to letter stimuli.

Neuron Density Is A Fundamental Determinant Of Structural Connectivity In The Primate Cerebral Cortex

Studies of structural brain connectivity have revealed many intriguing features of complex cortical networks. To advance integrative theories of cortical organisation, an understanding of how connectivity interrelates with other aspects of brain architecture is required. Recent studies have suggested that interareal connectivity may be related to macroscopic as well as microscopic features of cortical areas. However, it is unclear how these features are interdependent and which most strongly and closely relate to structural corticocortical connectivity. Here, we systematically investigated the relation of many microscopic and macroscopic architectonic features of cortical organization to cortical connectivity, using a comprehensive, up-to-date structural connectome of the primate brain. Importantly, relationships were investigated by multi-variate analyses to account for the interrelations of features. Of all considered factors, the classical architectonic parameter of neuron density was most strongly and consistently related to cortical connectivity, and in conjoint analyses largely abolished effects of other features. These results reveal neuron density as a central architectonic factor of the primate cerebral cortex that is closely related to essential aspects of brain connectivity and which determines further features of the architectonic organization of cortical areas. Our findings integrate several aspects of cortical organization, with implications for cortical development and function.