The topology of structural brain connectivity in diseases and spatio-temporal connectomics

The brain is a complex system, composed of multiple neural units interconnected at different spatial and temporal scales. Diffusion MRI allows probing in vivo the anatomical connectivity between different cortical areas through white matter tracts. In parallel, functional MRI records neural-related signals of brain activity. Particularly, during rest (in absence of specific external task) reproducible dynamical patterns of functional synchronization have been shown across different brain areas. This rich information can be conveniently represented in the form of a graph, a mathematical object where nodes correspond to cortical regions and are connected by edges representing anatomical connections. On the top of this structural network, or brain connectome, individual nodes are associated to functional signals representing neural activity over observation periods. Network science has fundamentally contributed to the characterization of the human connectome. The brain is a small-world network, able to combine segregation and integration aspects. These properties allow functional specialization on the one side, and efficient communication between distant brain areas on the other side, supporting complex cognitive and executive functions. Graph theoretical methods quantify brain topological properties, and allow their comparison between different populations and conditions. In fact, brain connectivity patterns and interdependences between anatomical substrate and functional synchronization have been proved to be impaired in a variety of brain disorders, and to change across human development and aging. Despite these important advancements in the understanding of the brain structure and functioning, many questions are currently unanswered. It is not clear for instance how structural connectivity features are related to individual cognitive capabilities and deficits, and if they have the concrete potential to distinguish pathological subgroups for early diagnosis of brain diseases. Most importantly, it is not yet understood how the connectome topology relates to specific brain functions, and how the transmission of information happens on the top of the structural connectivity infrastructure in order to generate observed functional dynamics. This thesis was motivated by these interdisciplinary inputs, and is the result of a strong interaction between biological and clinical questions on the one hand, and methodological development needs on the other hand. First, we have contributed to the characterization of the human connectome in health and pathologies by adapting and developing network measures for the description of the brain architecture at different scales. Particularly, we have focused on the topological characterization of subnetworks role within the overall brain network. Importantly, we have shown that the topological alteration of distinct brain subsystems may be a biomarker for different brain disorders. Second, we have proposed an original network model for the joint representation of brain structural and functional connectivity properties. This flexible spatio-temporal framework allows the investigation of functional dynamics at multiple temporal scales. Importantly, the investigation of spatio-temporal graphs in healthy subjects have allowed to disclose temporal relationships between local brain activations in resting state recordings, and has highlighted functional communication principles across the brain structural network

Multi-scale community organization of the human structural connectome and its relationship with resting-state functional connectivity

The human connectome has been widely studied over the past decade. A principal finding is that it can be decomposed into communities of densely interconnected brain regions. Past studies have often used single-scale modularity measures in order to infer the connectome's community structure, possibly overlooking interesting structure at other organizational scales. In this report, we used the partition stability framework, which defines communities in terms of a Markov process (random walk), to infer the connectome's multi-scale community structure. Comparing the community structure to observed resting-state functional connectivity revealed communities across a broad range of scales that were closely related to functional connectivity. This result suggests a mapping between communities in structural networks, models of influence-spreading and diffusion, and brain function. It further suggests that the spread of influence among brain regions may not be limited to a single characteristic scal

Multi-scale community organization of the human structural connectome and its relationship with resting-state functional connectivity

The human connectome has been widely studied over the past decade. A principal finding is that it can be decomposed into communities of densely interconnected brain regions. This result, however, may be limited methodologically. Past studies have often used a flawed modularity measure in order to infer the connectome's community structure. Also, these studies relied on the intuition that community structure is best defined in terms of a network's static topology as opposed to a more dynamical definition. In this report we used the partition stability framework, which defines communities in terms of a Markov process (random walk), to infer the connectome's multi-scale community structure. Comparing the community structure to observed resting-state functional connectivity revealed communities across a broad range of dynamical scales that were closely related to functional connectivity. This result suggests a mapping between communities in structural networks, models of communication processes, and brain function. It further suggests that communication in the brain is not limited to a single characteristic scale, leading us to posit a heuristic for scale-selective communication in the cerebral cortex.Comment: Corrected small typographical mistakes, changed order of authors and funding information, and also chose a more efficient compression for figure

Generative models of the human connectome

The human connectome represents a network map of the brain's 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.Comment: 38 pages, 5 figures + 19 supplemental figures, 1 tabl

Connectome-Based Patterns of First-Episode Medication-Naïve Patients With Schizophrenia

Emerging evidence indicates that a disruption in brain network organization may play an important role in the pathophysiology of schizophrenia. The neuroimaging fingerprint reflecting the pathophysiology of first-episode schizophrenia remains to be identified. Here, we aimed at characterizing the connectome organization of first-episode medication-naïve patients with schizophrenia. A cross-sectional structural and functional neuroimaging study using two independent samples (principal dataset including 42 medication-naïve, previously untreated patients and 48 healthy controls; replication dataset including 39 first-episode patients [10 untreated patients] and 66 healthy controls) was performed. Brain network architecture was assessed by means of white matter fiber integrity measures derived from diffusion-weighted imaging (DWI) and by means of structural-functional (SC-FC) coupling measured by combining DWI and resting-state functional magnetic resonance imaging. Connectome rich club organization was found to be significantly disrupted in medication-naïve patients as compared with healthy controls (P = .012, uncorrected), with rich club connection strength (P = .032, uncorrected) and SC-FC coupling (P < .001, corrected for false discovery rate) decreased in patients. Similar results were found in the replication dataset. Our findings suggest that a disruption of rich club organization and functional dynamics may reflect an early feature of schizophrenia pathophysiology. These findings add to our understanding of the neuropathological mechanisms of schizophrenia and provide new insights into the early stages of the disorder

Brain connectivity alterations in early psychosis: from clinical to neuroimaging staging.

Early in the course of psychosis, alterations in brain connectivity accompany the emergence of psychiatric symptoms and cognitive impairments, including processing speed. The clinical-staging model is a refined form of diagnosis that places the patient along a continuum of illness conditions, which allows stage-specific interventions with the potential of improving patient care and outcome. This cross-sectional study investigates brain connectivity features that characterize the clinical stages following a first psychotic episode. Structural brain networks were derived from diffusion-weighted MRI for 71 early-psychosis patients and 76 healthy controls. Patients were classified into stage II (first-episode), IIIa (incomplete remission), IIIb (one relapse), and IIIc (two or more relapses), according to the course of the illness until the time of scanning. Brain connectivity measures and diffusion parameters (fractional anisotropy, apparent diffusion coefficient) were investigated using general linear models and sparse linear discriminant analysis (sLDA), studying distinct subgroups of patients who were at specific stages of early psychosis. We found that brain connectivity impairments were more severe in clinical stages following the first-psychosis episode (stages IIIa, IIIb, IIIc) than in first-episode psychosis (stage II) patients. These alterations were spatially diffuse but converged on a set of vulnerable regions, whose inter-connectivity selectively correlated with processing speed in patients and controls. The sLDA suggested that relapsing-remitting (stages IIIb, IIIc) and non-remitting (stage IIIa) patients are characterized by distinct dysconnectivity profiles. Our results indicate that neuroimaging markers of brain dysconnectivity in early psychosis may reflect the heterogeneity of the illness and provide a connectomics signature of the clinical-staging model

Multi-scale community organization of the human structural connectome and its relationship with resting-state functional connectivity

The human connectome has been widely studied over the past decade. A principal finding is that it can be decomposed into communities of densely interconnected brain regions. This result, however, may be limited methodologically. Past studies have often used a flawed modularity measure in order to infer the connectome's community structure. Also, these studies relied on the intuition that community structure is best defined in terms of a network's static topology as opposed to a more dynamical definition. In this report we used the partition stability framework, which defines communities in terms of a Markov process (random walk), to infer the connectome's multi-scale community structure. Comparing the community structure to observed resting-state functional connectivity revealed communities across a broad range of dynamical scales that were closely related to functional connectivity. This result suggests a mapping between communities in structural networks, models of communication processes, and brain function. It further suggests that communication in the brain is not limited to a single characteristic scale, leading us to posit a heuristic for scale-selective communication in the cerebral cortex.Comment: Corrected small typographical mistakes, changed order of authors and funding information, and also chose a more efficient compression for figure