Joaquín Goñi

High-cost, high-capacity backbone for global brain communication

Network studies of human brain structural connectivity have identified a specific set of brain regions that are both highly connected and highly central. Recent analyses have shown that these putative hub regions are mutually and densely interconnected, forming a “rich club” within the human brain. Here we show that the set of pathways linking rich club regions forms a central high-cost, high-capacity backbone for global brain communication. Diffusion tensor imaging (DTI) data of two sets of 40 healthy subjects were used to map structural brain networks. The contributions to network cost and communication capacity of global cortico-cortical connections were assessed through measures of their topology and spatial embedding. Rich club connections were found to be more costly than predicted by their density alone and accounted for 40% of the total communication cost. Furthermore, 69% of all minimally short paths between node pairs were found to travel through the rich club and a large proportion of these communication paths consisted of ordered sequences of edges (“path motifs”) that first fed into, then traversed, and finally exited the rich club, while passing through nodes of increasing and then decreasing degree. The prevalence of short paths that follow such ordered degree sequences suggests that neural communication might take advantage of strategies for dynamic routing of information between brain regions, with an important role for a highly central rich club. Taken together, our results show that rich club connections make an important contribution to interregional signal traffic, forming a central high-cost, high-capacity backbone for global brain communication.

Abnormal Rich Club Organization and Functional Brain Dynamics in Schizophrenia

IMPORTANCE The human brain forms a large-scale structural network of regions and interregional pathways. Recent studies have reported the existence of a selective set of highly central and interconnected hub regions that may play a crucial role in the brains integrative processes, together forming a central backbone for global brain communication. Abnormal brain connectivity may have a key role in the pathophysiology of schizophrenia. OBJECTIVE To examine the structure of the rich club in schizophrenia and its role in global functional brain dynamics. DESIGN Structural diffusion tensor imaging and resting-state functional magnetic resonance imaging were performed in patients with schizophrenia and matched healthy controls. SETTING Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, the Netherlands. PARTICIPANTS Forty-eight patients and 45 healthy controls participated in the study. An independent replication data set of 41 patients and 51 healthy controls was included to replicate and validate significant findings. MAIN OUTCOME(S) AND MEASURES: Measures of rich club organization, connectivity density of rich club connections and connections linking peripheral regions to brain hubs, measures of global brain network efficiency, and measures of coupling between brain structure and functional dynamics. RESULTS Rich club organization between high-degree hub nodes was significantly affected in patients, together with a reduced density of rich club connections predominantly comprising the white matter pathways that link the midline frontal, parietal, and insular hub regions. This reduction in rich club density was found to be associated with lower levels of global communication capacity, a relationship that was absent for other white matter pathways. In addition, patients had an increase in the strength of structural connectivity-functional connectivity coupling. CONCLUSIONS Our findings provide novel biological evidence that schizophrenia is characterized by a selective disruption of brain connectivity among central hub regions of the brain, potentially leading to reduced communication capacity and altered functional brain dynamics.

Changes in structural and functional connectivity among resting-state networks across the human lifespan

At rest, the brains sensorimotor and higher cognitive systems engage in organized patterns of correlated activity forming resting-state networks. An important empirical question is how functional connectivity and structural connectivity within and between resting-state networks change with age. In this study we use network modeling techniques to identify significant changes in network organization across the human lifespan. The results of this study demonstrate that whole-brain functional and structural connectivity both exhibit reorganization with age. On average, functional connections within resting-state networks weaken in magnitude while connections between resting-state networks tend to increase. These changes can be localized to a small subset of functional connections that exhibit systematic changes across the lifespan. Collectively, changes in functional connectivity are also manifest at a system-wide level, as components of the control, default mode, saliency/ventral attention, dorsal attention, and visual networks become less functionally cohesive, as evidenced by decreased component modularity. Paralleling this functional reorganization is a decrease in the density and weight of anatomical white-matter connections. Hub regions are particularly affected by these changes, and the capacity of those regions to communicate with other regions exhibits a lifelong pattern of decline. Finally, the relationship between functional connectivity and structural connectivity also appears to change with age; functional connectivity along multi-step structural paths tends to be stronger in older subjects than in younger subjects. Overall, our analysis points to age-related changes in inter-regional communication unfolding within and between resting-state networks.

Resting-brain functional connectivity predicted by analytic measures of network communication

Significance Patterns of distributed brain activity are thought to underlie virtually all aspects of cognition and behavior. In this paper, we explore the degree to which it is possible to predict such functional patterns from the network of anatomical connections that link brain regions. To this end, we use three separately acquired neuroimaging datasets recording anatomical and functional connections in the human brain. We apply several measures of network communication that are derived analytically from the brain’s anatomical network. Our principal finding is that such network measures can predict empirically measured functional connectivity at levels that exceed other modeling approaches. Our study sheds light on the important role of anatomical networks and communication processes in shaping the brain’s functional activity. The complex relationship between structural and functional connectivity, as measured by noninvasive imaging of the human brain, poses many unresolved challenges and open questions. Here, we apply analytic measures of network communication to the structural connectivity of the human brain and explore the capacity of these measures to predict resting-state functional connectivity across three independently acquired datasets. We focus on the layout of shortest paths across the network and on two communication measures—search information and path transitivity—which account for how these paths are embedded in the rest of the network. Search information is an existing measure of information needed to access or trace shortest paths; we introduce path transitivity to measure the density of local detours along the shortest path. We find that both search information and path transitivity predict the strength of functional connectivity among both connected and unconnected node pairs. They do so at levels that match or significantly exceed path length measures, Euclidean distance, as well as computational models of neural dynamics. This capacity suggests that dynamic couplings due to interactions among neural elements in brain networks are substantially influenced by the broader network context adjacent to the shortest communication pathways.

Fatigue in multiple sclerosis is associated with the disruption of frontal and parietal pathways

Background Fatigue is one of the most frequent and disturbing symptoms in multiple sclerosis (MS), directly affecting the patient’s quality of life. However, many questions remain unclear regarding the anatomic brain correlate of MS-related fatigue. Objective To assess the relationship between fatigue and white matter lesion location and gray matter atrophy. Methods In this study, 60 patients with MS were evaluated with the Modified Fatigue Impact Scale and magnetic resonance imaging. Location of white matter lesion was analyzed using a voxel-by-voxel lesion probability mapping approach and gray matter atrophy degree and location using an optimized voxel-based morphometry method. Results We found a correlation between lesion load and fatigue score (T2 lesion load: r = 0.415, P = 0.001; T1 lesion load r = 0.328, P = 0.011). Moreover, fatigue correlated with lesions in the right parietotemporal (periatrial area, juxtaventricular white matter deep in the parietal lobe and callosal forceps) and left frontal (middle-anterior corpus callosum, anterior cingulum and centrum semiovale of the superior and middle frontal gyri) white matter regions (P < 0.001 in all cases). Finally, fatigue score significantly correlated with gray matter atrophy in frontal regions, specifically, the left superior frontal gyrus and bilateral middle frontal gyri (P < 0.001 in all cases). Conclusion Our results suggest that the symptom of fatigue is associated with a disruption of brain networks involved in cognitive/attentional processes.

Cooperative and Competitive Spreading Dynamics on the Human Connectome

Increasingly detailed data on the network topology of neural circuits create a need for theoretical principles that explain how these networks shape neural communication. Here we use a model of cascade spreading to reveal architectural features of human brain networks that facilitate spreading. Using an anatomical brain network derived from high-resolution diffusion spectrum imaging (DSI), we investigate scenarios where perturbations initiated at seed nodes result in global cascades that interact either cooperatively or competitively. We find that hub regions and a backbone of pathways facilitate early spreading, while the shortest path structure of the connectome enables cooperative effects, accelerating the spread of cascades. Finally, competing cascades become integrated by converging on polysensory associative areas. These findings show that the organizational principles of brain networks shape global communication and facilitate integrative function.

Contribution of white matter lesions to gray matter atrophy in multiple sclerosis: evidence from voxel-based analysis of T1 lesions in the visual pathway.

BACKGROUND The biological basis of gray matter (GM) atrophy in multiple sclerosis is not well understood, but GM damage seems to be the most critical factor leading to permanent disability. OBJECTIVE To assess to what extent white matter (WM) lesions contribute to regional GM atrophy in multiple sclerosis. DESIGN Because optic pathway GM atrophy and optic radiation lesions, rather than being related to each other, could be independent results of the disease, we applied a nonaprioristic WM method to analyze the interrelationships of both phenomena. On a voxel-by-voxel basis, we correlated T1 magnetic resonance imaging-derived lesion probability maps of the entire brain with atrophy of the lateral geniculate nuclei and calcarine/pericalcarine cortices. SETTING Multiple sclerosis center, University of Navarra, Pamplona, Spain. PATIENTS Sixty-one patients with multiple sclerosis. MAIN OUTCOME MEASURE Mapping of WM regions contributing to GM atrophy in the optic pathway. RESULTS Patients with multiple sclerosis had lateral geniculate nucleus atrophy, which correlated with the presence of lesions specifically in the optic radiations but not in the rest of the brain. Optic pathway lesions explained up to 28% of the change of variance in lateral geniculate nucleus atrophy. Patients also had occipital cortex atrophy, which did not correlate with lesions in the optic radiations or any other WM region. CONCLUSIONS Focal WM damage is associated with upstream GM atrophy, suggesting that retrograde damage of the perikarya from axonal injury in multiple sclerosis plaques is one of the significant factors in the genesis of GM atrophy, although other neurodegenerative processes are probably at work as well.

Generative models of the human connectome

The human connectome represents a network map of the brains wiring diagram and the pattern into which its connections are organized is thought to play an important role in cognitive function. The generative rules that shape the topology of the human connectome remain incompletely understood. Earlier work in model organisms has suggested that wiring rules based on geometric relationships (distance) can account for many but likely not all topological features. Here we systematically explore a family of generative models of the human connectome that yield synthetic networks designed according to different wiring rules combining geometric and a broad range of topological factors. We find that a combination of geometric constraints with a homophilic attachment mechanism can create synthetic networks that closely match many topological characteristics of individual human connectomes, including features that were not included in the optimization of the generative model itself. We use these models to investigate a lifespan dataset and show that, with age, the model parameters undergo progressive changes, suggesting a rebalancing of the generative factors underlying the connectome across the lifespan.

A computational analysis of protein-protein interaction networks in neurodegenerative diseases

BackgroundRecent developments have meant that network theory is making an important contribution to the topological study of biological networks, such as protein-protein interaction (PPI) networks. The identification of differentially expressed genes in DNA array experiments is a source of information regarding the molecular pathways involved in disease. Thus, considering PPI analysis and gene expression studies together may provide a better understanding of multifactorial neurodegenerative diseases such as Multiple Sclerosis (MS) and Alzheimer disease (AD). The aim of this study was to assess whether the parameters of degree and betweenness, two fundamental measures in network theory, are properties that differentiate between implicated (seed-proteins) and non-implicated nodes (neighbors) in MS and AD. We used experimentally validated PPI information to obtain the neighbors for each seed group and we studied these parameters in four networks: MS-blood network; MS-brain network; AD-blood network; and AD-brain network.ResultsSpecific features of seed-proteins were revealed, whereby they displayed a lower average degree in both diseases and tissues, and a higher betweenness in AD-brain and MS-blood networks. Additionally, the heterogeneity of the processes involved indicate that these findings are not pathway specific but rather that they are spread over different pathways.ConclusionOur findings show differential centrality properties of proteins whose gene expression is impaired in neurodegenerative diseases.

Fractal dimension and white matter changes in multiple sclerosis

The brain white matter (WM) in multiple sclerosis (MS) suffers visible and non-visible (normal-appearing WM (NAWM)) changes in conventional magnetic resonance (MR) images. The fractal dimension (FD) is a quantitative parameter that characterizes the morphometric variability of a complex object. Our aim was to assess the usefulness of FD analysis in the measurement of WM abnormalities in conventional MR images in patients with MS, particularly to detect NAWM changes. First, we took on a voxel-based morphometry approach optimized for MS to obtain the segmented brain. Then, the FD of the whole grey-white matter interface (WM border) and skeletonized WM was calculated in patients with MS and healthy controls. To assess the FD of the NAWM, we focused our analysis on single sections without lesions at the centrum semiovale level. We found that patients with MS had a significant decrease in the FD of the entire brain WM compared with healthy controls. Such a decrease of the FD was detected not only on MR image sections with MS lesions but also on single sections with NAWM. Taken together, the results showed that FD identifies changes in the brain of patients with MS, including in NAWM, even at an early phase of the disease. Thus, FD might become a useful marker of diffuse damage of the central nervous system in MS.