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152. Gradients of structure-function tethering across neocortex

Author

Vázquez-Rodríguez, Bertha, primary, Suárez, Laura E., additional, Shafiei, Golia, additional, Markello, Ross D., additional, Paquola, Casey, additional, Hagmann, Patric, additional, van den Heuvel, Martijn P., additional, Bernhardt, Boris C., additional, Spreng, R. Nathan, additional, and Mišić, Bratislav, additional

Published
2019
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155. Stochastic resonance and optimal information transfer at criticality on a network model of the human connectome

Author

V��zquez-Rodr��guez, Bertha, Avena-Koenigsberger, Andrea, Sporns, Olaf, Griffa, Alessandra, Hagmann, Patric, and Larralde, Hern��n

Subjects
Biological Physics (physics.bio-ph), FOS: Biological sciences, Quantitative Biology - Neurons and Cognition, FOS: Physical sciences, Neurons and Cognition (q-bio.NC), Physics - Biological Physics
Abstract

Stochastic resonance is a phenomenon in which noise enhances the response of a system to an input signal. The brain is an example of a system that has to detect and transmit signals in a noisy environment, suggesting that it is a good candidate to take advantage of SR. In this work, we aim to identify the optimal levels of noise that promote signal transmission through a simple network model of the human brain. Specifically, using a dynamic model implemented on an anatomical brain network (connectome), we investigate the similarity between an input signal and a signal that has traveled across the network while the system is subject to different noise levels. We find that non-zero levels of noise enhance the similarity between the input signal and the signal that has traveled through the system. The optimal noise level is not unique; rather, there is a set of parameter values at which the information is transmitted with greater precision, this set corresponds to the parameter values that place the system in a critical regime. The multiplicity of critical points in our model allows it to adapt to different noise situations and remain at criticality.

Published
2017

156. Geometric renormalization unravels self-similarity of the multiscale human connectome.

Author

Muhua Zheng, Allard, Antoine, Hagmann, Patric, Alemán-Gómez, Yasser, and Serrano, M. Ángeles

Subjects
ARCHAEOLOGICAL human remains, DIGITAL computer simulation, GEOMETRIC modeling
Abstract

Structural connectivity in the brain is typically studied by reducing its observation to a single spatial resolution. However, the brain possesses a rich architecture organized over multiple scales linked to one another. We explored the multiscale organization of human connectomes using datasets of healthy subjects reconstructed at five different resolutions. We found that the structure of the human brain remains self-similar when the resolution of observation is progressively decreased by hierarchical coarse-graining of the anatomical regions. Strikingly, a geometric network model, where distances are not Euclidean, predicts the multiscale properties of connectomes, including self-similarity. The model relies on the application of a geometric renormalization protocol which decreases the resolution by coarse-graining and averaging over short similarity distances. Our results suggest that simple organizing principles underlie the multiscale architecture of human structural brain networks, where the same connectivity law dictates short- and long-range connections between different brain regions over many resolutions. The implications are varied and can be substantial for fundamental debates, such as whether the brain is working near a critical point, as well as for applications including advanced tools to simplify the digital reconstruction and simulation of the brain. [ABSTRACT FROM AUTHOR]

Published
2020
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157. Interindividual Covariations of Brain Functional and Structural Connectivities Are Decomposed Blindly to Subnetworks: A Fusion-Based Approach.

Author

Keyvanfard, Farzaneh, Nasiraei‐Moghaddam, Abbas, Hagmann, Patric, and Nasiraei-Moghaddam, Abbas

Subjects
INDEPENDENT component analysis, PEARSON correlation (Statistics), DIFFUSION tensor imaging, BRAIN, RESEARCH, RESEARCH evaluation, RESEARCH methodology, MAGNETIC resonance imaging, BRAIN mapping, RETROSPECTIVE studies, MEDICAL cooperation, EVALUATION research, COMPARATIVE studies, RESEARCH funding
Abstract

Background: Studying brain interindividual variations has recently gained interest to understand different human behaviors. It is particularly important to investigate how a variety of functional differences can be associated with a few differences in brain structure. It would be more meaningful if such an investigation is performed jointly at the network level to connect structural building blocks to functional variations modules.Purpose: To decompose the interindividual variations of brain in the form of mutual functional and structural subnetworks based on a data-driven approach.Study Type: Retrospective.Population: In all, 92 healthy subjects.Field Strength/sequence: 3T Siemens/MPRAGE, diffusion spectrum imaging (DSI) acquisition protocol, gradient echo sequence.Assessment: The proposed approach was quantitatively assessed by examining the consistency of the networks against the number of subjects. Distribution of the obtained components across brain regions was studied and their relevance was qualitatively evaluated by comparison to variations that had been independently reported previously.Statistical Tests: Permutation test, two-sample t-test, Pearson correlation coefficient.Results: Ten pairs of components including functional and structural subnetworks were obtained. Assessing the reproducibility of the proposed method with respect to the sample size indicated reliable detection of connections (above 70%) for all components by reducing the number of subjects to 70. Specifically, one of the functional subnetworks can be used to distinguish left-handed from right-handed people (P = 2.6 × 10-8 ) as the basic interindividual variation. This functional subnetwork has a main overlap (40.18%) with the somatomotor system and the Broca part was captured in its corresponding structural subnetwork.Data Conclusion: These results show that the proposed method can reveal intersubject variations systematically through a mathematical algorithm of joint independent component analysis. They confirm that intersubject variations can be expressed in the form of building blocks. In contrast to the functional subnetworks that were discoverable independently, their structural counterparts were found and interpreted only in conjunction with the functional subnetworks.Level Of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1779-1788. [ABSTRACT FROM AUTHOR]

Published
2020
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162. Network-Based Asymmetry of the Human Auditory System

Author

Mišić, Bratislav, primary, Betzel, Richard F, additional, Griffa, Alessandra, additional, de Reus, Marcel A, additional, He, Ye, additional, Zuo, Xi-Nian, additional, van den Heuvel, Martijn P, additional, Hagmann, Patric, additional, Sporns, Olaf, additional, and Zatorre, Robert J, additional

Published
2018
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164. T52. N-ACETYL-CYSTEINE ADD-ON TREATMENT LEADS TO AN IMPROVEMENT OF FORNIX WHITE MATTER INTEGRITY IN EARLY PSYCHOSIS

Author

Klauser, Paul, primary, Xin, Lijing, additional, Fournier, Margot, additional, Griffa, Alessandra, additional, Cleusix, Martine, additional, Jenni, Raoul, additional, Cuenod, Michel, additional, Gruetter, Rolf, additional, Hagmann, Patric, additional, Conus, Philippe, additional, Baumann, Philipp, additional, and Do, Kim Q, additional

Published
2018
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167. 16.2 CHILDHOOD TRAUMA ENGAGES OXIDATIVE STRESS, HIPPOCAMPUS ALTERATIONS, AND POORER CLINICAL OUTCOME IN EARLY PSYCHOSIS PATIENTS

Author

Alameda, Luis, primary, Fournier, Margot, additional, Khadimallah, Ines, additional, Baumann, Philippe S, additional, Cleusix, Martine, additional, Griffa, Alessandra, additional, Klauser, Paul, additional, Jenni, Raoul, additional, Cuenod, Michel, additional, Hagmann, Patric, additional, Conus, Philippe, additional, and Do, Kim, additional

Published
2018
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169. Nutrient Intake in the First Two Weeks of Life and Brain Growth in Preterm Neonates

Author

Schneider, Juliane, primary, Fischer Fumeaux, Céline J., additional, Duerden, Emma G., additional, Guo, Ting, additional, Foong, Justin, additional, Graz, Myriam Bickle, additional, Hagmann, Patric, additional, Chakravarty, M. Mallar, additional, Hüppi, Petra S., additional, Beauport, Lydie, additional, Truttmann, Anita C., additional, and Miller, Steven P., additional

Published
2018
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172. Structural Brain Connectivity in School-Age Preterm Infants Provides Evidence for Impaired Networks Relevant for Higher Order Cognitive Skills and Social Cognition

Author

Fischi-Gómez, Elda, Vasung, Lana, Meskaldji, Djalel-Eddine, Lazeyras, Fançois, Borradori-Tolsa, Cristina, Hagmann, Patric, Barisnikov, Koviljka, Thiran, Jean-Philippe, Hüppi, Petra S., Fischi-Gómez, Elda, Vasung, Lana, Meskaldji, Djalel-Eddine, Lazeyras, Fançois, Borradori-Tolsa, Cristina, Hagmann, Patric, Barisnikov, Koviljka, Thiran, Jean-Philippe, and Hüppi, Petra S.

Abstract

Extreme prematurity and pregnancy conditions leading to intrauterine growth restriction (IUGR) affect thousands of newborns every year and increase their risk for poor higher order cognitive and social skills at school age. However, little is known about the brain structural basis of these disabilities. To compare the structural integrity of neural circuits between prematurely born controls and children born extreme preterm (EP) or with IUGR at school age, long-ranging and short-ranging connections were noninvasively mapped across cortical hemispheres by connection matrices derived from diffusion tensor tractography. Brain connectivity was modeled along fiber bundles connecting 83 brain regions by a weighted characterization of structural connectivity (SC). EP and IUGR subjects, when compared with controls, had decreased fractional anisotropy-weighted SC (FAw-SC) of cortico-basal ganglia-thalamo-cortical loop connections while cortico-cortical association connections showed both decreased and increased FAw-SC. FAw-SC strength of these connections was associated with poorer socio-cognitive performance in both EP and IUGR children

Published
2017

173. Modeling Resting-State Functional Networks When the Cortex Falls Asleep: Local and Global Changes

Author

Deco, Gustavo, Hagmann, Patric, Hudetz, Anthony G., Tononi, Giulio, Deco, Gustavo, Hagmann, Patric, Hudetz, Anthony G., and Tononi, Giulio

Abstract

The transition from wakefulness to sleep represents the most conspicuous change in behavior and the level of consciousness occurring in the healthy brain. It is accompanied by similarly conspicuous changes in neural dynamics, traditionally exemplified by the change from "desynchronized” electroencephalogram activity in wake to globally synchronized slow wave activity of early sleep. However, unit and local field recordings indicate that the transition is more gradual than it might appear: On one hand, local slow waves already appear during wake; on the other hand, slow sleep waves are only rarely global. Studies with functional magnetic resonance imaging also reveal changes in resting-state functional connectivity (FC) between wake and slow wave sleep. However, it remains unclear how resting-state networks may change during this transition period. Here, we employ large-scale modeling of the human cortico-cortical anatomical connectivity to evaluate changes in resting-state FC when the model "falls asleep” due to the progressive decrease in arousal-promoting neuromodulation. When cholinergic neuromodulation is parametrically decreased, local slow waves appear, while the overall organization of resting-state networks does not change. Furthermore, we show that these local slow waves are structured macroscopically in networks that resemble the resting-state networks. In contrast, when the neuromodulator decrease further to very low levels, slow waves become global and resting-state networks merge into a single undifferentiated, broadly synchronized network

Published
2017

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

Author

BETZEL, RICHARD F., GRIFFA, ALESSANDRA, AVENA-KOENIGSBERGER, ANDREA, GOÑI, JOAQUÍN, THIRAN, JEAN-PHILIPPE, HAGMANN, PATRIC, SPORNS, OLAF, BETZEL, RICHARD F., GRIFFA, ALESSANDRA, AVENA-KOENIGSBERGER, ANDREA, GOÑI, JOAQUÍN, THIRAN, JEAN-PHILIPPE, HAGMANN, PATRIC, and SPORNS, OLAF

Abstract

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 scale

Published
2017

176. Routes Obey Hierarchy in Complex Networks

Author

Csoma, Attila, primary, Kőrösi, Attila, additional, Rétvári, Gábor, additional, Heszberger, Zalán, additional, Bíró, József, additional, Slíz, Mariann, additional, Avena-Koenigsberger, Andrea, additional, Griffa, Alessandra, additional, Hagmann, Patric, additional, and Gulyás, András, additional

Published
2017
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181. Minimizing adjuvant treatment after transoral robotic surgery through surgical margin revision and exclusion of radiographic extracapsular extension: A Prospective observational cohort study

Author

Morisod, Benoit, primary, Venara-Vulpe, Ioana I., additional, Alzuphar, Stephen, additional, Monnier, Yan, additional, Bongiovanni, Massimo, additional, Hagmann, Patric, additional, Bouchaab, Hanan, additional, Bourhis, Jean, additional, and Simon, Christian, additional

Published
2017
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183. Frontal cortical thickness correlates positively with impulsivity in early psychosis male patients.

Author

Baumann, Philipp S., Klauser, Paul, Griffa, Alessandra, Golay, Philippe, Palix, Julie, Alameda, Luis, Moulin, Valérie, Hagmann, Patric, Do, Kim Q., and Conus, Philippe

Subjects
PSYCHOSES, FRONTAL lobe, BRAIN physiology, IMPULSE (Psychology), MEN
Abstract

Aim: Impulsive behaviours, which are frequent in young people suffering from psychosis have been linked to risky and violent behaviours and participate to the burden of psychotic illness. Given that morphological brain correlates of impulsivity in schizophrenia have been poorly investigated especially in young adults, the aim of this study was to investigate the relationship between impulsivity and cortical thickness in early psychosis (EP) patients. Method: A total of 17 male subjects in the early phase of psychosis were recruited. Impulsivity was assessed with the Lecrubier Impulsivity Rating Scale. Mean cortical thickness was extracted from magnetic resonance imaging brain scans, using surface‐based methods. Results: Mean cortical thickness in the frontal lobe correlated positively with mean impulsivity in EP male patients. Conclusion: Our results suggest that psychotic subjects exhibiting higher impulsivity have larger frontal cortical thickness, which may pave the way towards the identification of patients with a higher risk to display impulsive behaviours. [ABSTRACT FROM AUTHOR]

Published
2019
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196. Path ensembles and a tradeoff between communication efficiency and resilience in the human connectome.

Author

Avena-Koenigsberger, Andrea, Mišić, Bratislav, Hawkins, Robert, Griffa, Alessandra, Hagmann, Patric, Goñi, Joaquín, and Sporns, Olaf

Subjects
BRAIN mapping, COMMUNICATION, GRAPH theory, NEURAL circuitry, BRAIN anatomy
Abstract

Computational analysis of communication efficiency of brain networks often relies on graph-theoretic measures based on the shortest paths between network nodes. Here, we explore a communication scheme that relaxes the assumption that information travels exclusively through optimally short paths. The scheme assumes that communication between a pair of brain regions may take place through a path ensemble comprising the k-shortest paths between those regions. To explore this approach, we map path ensembles in a set of anatomical brain networks derived from diffusion imaging and tractography. We show that while considering optimally short paths excludes a significant fraction of network connections from participating in communication, considering k-shortest path ensembles allows all connections in the network to contribute. Path ensembles enable us to assess the resilience of communication pathways between brain regions, by measuring the number of alternative, disjoint paths within the ensemble, and to compare generalized measures of path length and betweenness centrality to those that result when considering only the single shortest path between node pairs. Furthermore, we find a significant correlation, indicative of a trade-off, between communication efficiency and resilience of communication pathways in structural brain networks. Finally, we use k-shortest path ensembles to demonstrate hemispherical lateralization of efficiency and resilience. [ABSTRACT FROM AUTHOR]

Published
2017
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197. From diffusion MRI to brain connectomics

Author

Hagmann, Patric, Thiran, Jean-Philippe, and Meuli, Reto

Abstract

The success of diffusion MRI is deeply rooted in the fact that during their micrometric random displacements water molecules explore tissue microstructure. Hence by labeling magnetically spins of displaced water, diffusion MRI provides us with exquisite information about the sizes and orientations on the multiple compartments present inside an imaging voxel. Through the causal relation between on one hand the imaged restricted and oriented water mobility and on the other hand the axonal orientations in brain white matter, diffusion MRI has become a powerful method to infer fiber tract architecture and brain anatomic connectivity. This work is not only a journey that takes us from essential diffusion MRI physics to an investigation of the brain neuronal circuitry, but also a thesis aiming at demonstrating the power of large scale analysis of brain connectivity, where every technological component is essential. After a short introduction on molecular diffusion and diffusion NMR, we start by showing that diffusion contrast is positive. This key issue that was only postulated up to now, allows us to justify why diffusion can be computed accurately with the only signal modulus. Accordingly, various emerging MRI techniques that map non-Gaussian diffusion have found a sound justification. In particular it is precisely the result that allows us to map distribution of diffusion related spin displacements by Fourier transformation of the measured signal modulus, hence to do Diffusion Spectrum MRI. We show through multiples MR experiments how the shape of this distribution in a complicated multi-compartment biological system can be measured and how its characteristic local heterogeneity is a mirror of fiber architectures. We discuss in detail its interpretation and its relation to other diffusion MRI techniques. Tractography is the necessary link that from diffusion MRI provides us with nerve fiber trajectories and maps of brain axonal connectivity. In this thesis two algorithms are proposed, while the first is designed for diffusion tensor MRI, the second is shaped for high angular resolution diffusion MRI and specifically tested on Diffusion Spectrum MRI. After having considered the potentials and the limitations of these line generation algorithms, we investigate whether tractography can be formulated as a segmentation problem in a high dimensional non Euclidean space, i.e. position-orientation space. With the help of the developed tools we address some key neuro-scientific questions. Based on diffusion tensor MRI data of 32 healthy volunteers, language networks are investigated. It is shown that right-handed men are massively interconnected between the left-hemisphere speech areas whereas the homologous in the right hemisphere are sparse; furthermore interhemispheric connections between the speech areas and their contralatera1 homologues are relatively strong. Women and left-handed men have equally strong intrahemispheric connections in both hemispheres, but women have a higher density of interhemispheric connections. After this quantitative study, Diffusion Spectrum MRI data of a single subject is collected and tractography performed in order to analyze the global connectivity pattern of the human brain. For this purpose we propose to model this large scale architecture by an abstract graph. It is shown that the long-range axonal network exhibits a "small world" topology. This type of particular architecture is present in various large scale communication networks. They emerge usually from a growing process where optimal communication has to be developed under some resource constraints. Furthermore we show also that the architecture of axonal connectivity between cortical areas exhibits a hierarchical organization. These results, which confirm more indirect studies, provide essential material to discuss not only brain evolution and development but also information processing at the level of the brain.

Published
2005
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