Author name cluster

Patric Hagmann

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23 papers

1 author row

YNICL Journal 2026 Journal Article

Spatiotemporal brain dynamics in 8-to-9-year-old children: A comparative study between preterm and term schoolchildren

Solange Denervaud
Paola Zanchi
Céline J Fischer Fumeaux
Cléo Huguenin-Virchaux
Laureline Besuchet
Patric Hagmann
Anita C Truttmann
Juliane Schneider

Preterm birth disrupts critical phases of brain maturation, placing individuals at increased risk for long-term cognitive and functional impairments. This study investigated how very preterm birth affects the spatial and temporal organization of functional brain networks in school-aged children born very preterm using a spatiotemporal connectome framework. Multimodal MRI, including diffusion-weighted imaging and resting-state fMRI, was acquired from 25 children born before 30 gestational weeks and 25 age- and sex-matched full-term controls (8-9 years). We characterized the structure-function coupling of dynamic brain activity through Connected Components (CCs) defined as structurally constrained sets of functionally co-active regions identified on a multilayer graph. Three different metrics were computed: CC number (count of distinct co-activation patterns), CC height (peak number of regions within a CC, representing the spatial extent) and CC width (temporal span across consecutive time repetitions (TRs)). In addition, we quantified System Diversity (SD) and Spatiotemporal Diversity (STD), indices reflecting integrative richness and temporal variability of functional network dynamics. CC number decreased with age across groups, reflecting typical developmental patterns, while CC height was significantly greater in preterm children and positively associated with processing speed, suggesting altered or compensatory network co-activation. No significant group differences were observed for SD metrics. However, network-level analyses revealed significantly lower STD values in all functional networks in the preterm group, indicating possible heightened temporal stability and reduced functional flexibility. These findings suggest that very preterm birth selectively alters the dynamic engagement of functional systems, with potential implications for cognitive vulnerabilities. (243 mots; 250 max).

YNICL Journal 2025 Journal Article

Assessing workflow impact and clinical utility of AI-assisted brain aneurysm detection: A multi-reader study

Tommaso Di Noto
Sofyan Jankowski
Francesco Puccinelli
Guillaume Marie
Sebastien Tourbier
Yasser Alemán-Gómez
Oscar Esteban
Ricardo Corredor-Jerez

Despite the plethora of AI-based algorithms developed for anomaly detection in radiology, subsequent integration into clinical setting is rarely evaluated. In this work, we assess the applicability and utility of an AI-based model for brain aneurysm detection comparing the performance of two readers with different levels of experience (2 and 13 years). We aim to answer the following questions: 1) Do the readers improve their performance when assisted by the AI algorithm? 2) How much does the AI algorithm impact routine clinical workflow? We reuse and enlarge our open-access, Time-Of-Flight Magnetic Resonance Angiography dataset (N = 460). We use 360 subjects for training/validating our algorithm and 100 as unseen test set for the reading session. Even though our model reaches state-of-the-art results on the test set (sensitivity = 74 %, false positive rate = 1. 6), we show that neither the junior nor the senior reader significantly increase their sensitivity (p = 0. 59, p = 1, respectively). In addition, we find that reading time for both readers is significantly higher in the “AI-assisted” setting than in the “Unassisted” (+15 s, on average; p = 3 × 10 - 4 junior, p = 3 × 10 - 5 senior). The confidence reported by the readers is unchanged across the two settings, indicating that the AI assistance does not influence the certainty of the diagnosis. Our findings highlight the importance of clinical validation of AI algorithms in a clinical setting involving radiologists. This study should serve as a reminder to the community to always examine the real-word effectiveness and workflow impact of proposed algorithms.

YNICL Journal 2025 Journal Article

Brain white matter microstructure associations with blood markers of the GSH redox cycle in schizophrenia

Tommaso Pavan
Pascal Steullet
Yasser Alemán-Gómez
Raoul Jenni
Zoé Schilliger
Martine Cleusix
Luis Alameda
Kim Q. Do

In groups of patients suffering from schizophrenia (SZ), redox dysregulation was reported in both peripheral fluids and brain. It has been hypothesized that such dysregulation, including alterations of the glutathione (GSH) cycle could participate in the brain white matter (WM) abnormalities in SZ due to the oligodendrocytes' susceptibility to oxidative stress. In this study we aim to assess the differences between 82 schizophrenia patients (PT) and 86 healthy controls (HC) in GSH-redox peripheral blood markers: GSH peroxidase (GPx), reductase (GR) enzymatic activities and their ratio (GPx/GR-ratio), evaluating the hypotheses that alterations in the homeostasis of the systemic GSH cycle may be associated with pathological mechanisms in the brain WM in PT. To do so, we employ the advanced diffusion MRI methods: Diffusion Kurtosis Imaging (DKI) and White Matter Tract Integrity-Watson (WMTI-W), which provide excellent sensitivity to demyelination and neuroinflammation. We show that GPx levels are higher (p = 0.00041) in female control participants and decrease with aging (p = 0.026). We find differences between PT and HC in the association of GR and mean kurtosis (MK, p < 0.0001). Namely, lower MK was associated with higher blood GR activity in HC, but not in PT, suggesting that high GR activity (a hallmark of reductive stress) in HC was linked to changes in myelin integrity. However, GSH-redox peripheral blood markers did not explain the WM anomalies detected in PT, or the design of the present study could not detect subtle phenomenon, if present.

YNICL Journal 2023 Journal Article

Structural connectivity in recovery after coma: Connectome atlas approach

Polona Pozeg
Yasser Alemán-Goméz
Jane Jöhr
Dafin Muresanu
Alessandro Pincherle
Philippe Ryvlin
Patric Hagmann
Karin Diserens

AIM: Pathological states of recovery after coma as a result of a severe brain injury are marked with changes in structural connectivity of the brain. This study aimed to identify a topological correlation between white matter integrity and the level of functional and cognitive impairment in patients recovering after coma. METHODS: Structural connectomes were computed based on fractional anisotropy maps from 40 patients using a probabilistic human connectome atlas. We used a network based statistics approach to identify potential brain networks associated with a more favorable outcome, assessed with clinical neurobehavioral scores at the patient's discharge from the acute neurorehabilitation unit. RESULTS: We identified a subnetwork whose strength of connectivity correlated with a more favorable outcome as measured with the Disability Rating Scale (network based statistics: t >3.5, P =.010). The subnetwork predominated in the left hemisphere and included the thalamic nuclei, putamen, precentral and postcentral gyri, and medial parietal regions. Spearman correlation between the mean fractional anisotropy value of the subnetwork and the score was ρ = -0.60 (P 3.5, P =.033; Spearman's ρ = 0.58, P <.0001). CONCLUSION: The present findings suggest an important role of structural connectivity between the thalamus, putamen and somatomotor cortex in the recovery from coma as evaluated with neurobehavioral scores. These structures are part of the motor circuit involved in the generation and modulation of voluntary movement, as well as the forebrain mesocircuit supposedly underlying the maintenance of consciousness. As behavioural assessment of consciousness depends heavily on the signs of voluntary motor behaviour, further work will elucidate whether the identified subnetwork reflects the structural architecture underlying the recovery of consciousness or rather the ability to communicate its content.

YNIMG Journal 2023 Journal Article

The coupling between the spatial and temporal scales of neural processes revealed by a joint time-vertex connectome spectral analysis

Joan Rué-Queralt
Valentina Mancini
Vincent Rochas
Caren Latrèche
Peter J. Uhlhaas
Christoph M. Michel
Gijs Plomp
Stephan Eliez

Brain oscillations are produced by the coordinated activity of large groups of neurons and different rhythms are thought to reflect different modes of information processing. These modes, in turn, are known to occur at different spatial scales. Nevertheless, how these rhythms support different spatial modes of information processing at the brain scale is not yet fully understood. Here we use "Joint Time-Vertex Spectral Analysis" to characterize the joint spectral content of brain activity both in time (temporal frequencies) and in space over the connectivity graph (spatial connectome harmonics). This method allows us to characterize the relationship between spatially localized or distributed neural processes on one side and their respective temporal frequency bands in source-reconstructed M/EEG signals. We explore this approach on two different datasets, an auditory steady-state response (ASSR) and a visual grating task. Our results suggest that different information processing mechanisms are carried out at different frequency bands: while spatially distributed activity (which may also be interpreted as integration) specifically occurs at low temporal frequencies (alpha and theta) and low graph spatial frequencies, localized electrical activity (i.e., segregation) is observed at high temporal frequencies (high and low gamma) over restricted high spatial graph frequencies. Crucially, the estimated contribution of the distributed and localized neural activity predicts performance in a behavioral task, demonstrating the neurophysiological relevance of the joint time-vertex spectral representation.

YNICL Journal 2021 Journal Article

Discriminating cognitive motor dissociation from disorders of consciousness using structural MRI

Polona Pozeg
Jane Jöhr
Alessandro Pincherle
Guillaume Marie
Philippe Ryvlin
Reto Meuli
Patric Hagmann
Karin Diserens

An accurate evaluation and detection of awareness after a severe brain injury is crucial to a patient’s diagnosis, therapy, and end-of-life decisions. Misdiagnosis is frequent as behavior-based assessments often overlook subtle signs of consciousness. This study aimed to identify brain MRI characteristics of patients with residual consciousness after a severe brain injury and to develop a simple MRI-based scoring system according to the findings. We retrieved data from 128 patients and split them into a development or validation set. Structural brain MRIs were qualitatively assessed for lesions in 18 brain regions. We used logistic regression and support vector machine algorithms to first identify the most relevant brain regions predicting a patient’s outcome in the development set. We next built a diagnostic MRI-based score and estimated its optimal diagnostic cut-off point. The classifiers were then tested on the validation set and their performance compared using the receiver operating characteristic curve. Relevant brain regions predicting negative outcome highly overlapped between both classifiers and included the left mesencephalon, right basal ganglia, right thalamus, right parietal cortex, and left frontal cortex. The support vector machine classifier showed higher accuracy (0.93, 95% CI: 0.81–0.96) and specificity (0.97, 95% CI: 0.85–1) than logistic regression (accuracy: 0.87, 95% CI: 0.73 – 0.95; specificity: 0.90, 95% CI: 0.75–0.97), but equal sensitivity (0.67, 95% CI: 0.24–0.94 and 0.22–0.96, respectively) for distinguishing patients with and without residual consciousness. The novel MRI-based score assessing brain lesions in patients with disorders of consciousness accurately detects patients with residual consciousness. It could complement valuably behavioral evaluation as it is time-efficient and requires only conventional MRI.

YNIMG Journal 2021 Journal Article

Multiscale communication in cortico-cortical networks

Vincent Bazinet
Reinder Vos de Wael
Patric Hagmann
Boris C. Bernhardt
Bratislav Misic

Signaling in brain networks unfolds over multiple topological scales. Areas may exchange information over local circuits, encompassing direct neighbours and areas with similar functions, or over global circuits, encompassing distant neighbours with dissimilar functions. Here we study how the organization of cortico-cortical networks mediate localized and global communication by parametrically tuning the range at which signals are transmitted on the white matter connectome. We show that brain regions vary in their preferred communication scale. By investigating the propensity for brain areas to communicate with their neighbors across multiple scales, we naturally reveal their functional diversity: unimodal regions show preference for local communication and multimodal regions show preferences for global communication. We show that these preferences manifest as region- and scale-specific structure-function coupling. Namely, the functional connectivity of unimodal regions emerges from monosynaptic communication in small-scale circuits, while the functional connectivity of transmodal regions emerges from polysynaptic communication in large-scale circuits. Altogether, the present findings reveal that communication preferences are highly heterogeneous across the cortex, shaping regional differences in structure-function coupling.

YNIMG Journal 2021 Journal Article

The connectome spectrum as a canonical basis for a sparse representation of fast brain activity

Joan Rué-Queralt
Katharina Glomb
David Pascucci
Sébastien Tourbier
Margherita Carboni
Serge Vulliémoz
Gijs Plomp
Patric Hagmann

The functional organization of neural processes is constrained by the brain's intrinsic structural connectivity, i.e., the connectome. Here, we explore how structural connectivity can improve the representation of brain activity signals and their dynamics. Using a multi-modal imaging dataset (electroencephalography, structural MRI, and diffusion MRI), we represent electrical brain activity at the cortical surface as a time-varying composition of harmonic modes of structural connectivity. These harmonic modes are known as connectome harmonics. Here we describe brain activity signal as a time-varying combination of connectome harmonics. We term this description as the connectome spectrum of the signal. We found that: first, the brain activity signal is represented more compactly by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants. And last, the connectome spectrum characterizes fast brain dynamics with fewer degrees of freedom than area-based signal representations. Specifically, we show that a smaller number of dimensions capture the differences between low-level and high-level visual processing in the connectome spectrum. Also, we demonstrate that connectome harmonics capture more sensitively the topological properties of brain activity. In summary, this work provides statistical, functional, and topological evidence indicating that the description of brain activity in terms of structural connectivity fosters a more comprehensive understanding of large-scale dynamic neural functioning.

YNICL Journal 2020 Journal Article

Abnormal directed connectivity of resting state networks in focal epilepsy

Margherita Carboni
Pia De Stefano
Bernd J. Vorderwülbecke
Sebastien Tourbier
Emeline Mullier
Maria Rubega
Shahan Momjian
Karl Schaller

OBJECTIVE: Epilepsy diagnosis can be difficult in the absence of interictal epileptic discharges (IED) on scalp EEG. We used high-density EEG to measure connectivity in large-scale functional networks of patients with focal epilepsy (Temporal and Extratemporal Lobe Epilepsy, TLE and ETLE) and tested for network alterations during resting wakefulness without IEDs, compared to healthy controls. We measured global efficiency as a marker of integration within networks. METHODS: We analysed 49 adult patients with focal epilepsy and 16 healthy subjects who underwent high-density-EEG and structural MRI. We estimated cortical activity using electric source analysis in 82 atlas-based cortical regions based on the individual MRI. We applied directed connectivity analysis (Partial Directed Coherence) on these sources and performed graph analysis: we computed the Global Efficiency on the whole brain and on each resting state network. We tested these features in different group of patients. RESULTS: Compared to controls, efficiency was increased in both TLE and ETLE (p < 0.05). The somato-motor-network, the ventral-attention-network and the default-mode-network had a significantly increased efficiency (p < 0.05) in both TLE and ETLE as well as TLE with hippocampal sclerosis. SIGNIFICANCE: During interictal scalp EEG epochs without IED, patients with focal epilepsy show brain functional connectivity alterations in the whole brain and in specific resting-state-networks. This higher integration reflects a chronic effect of pathological activity within these structures and complement previous work on altered information outflow. These findings could increase the diagnostic sensitivity of scalp EEG to identify epileptic activity in the absence of IED.

YNIMG Journal 2020 Journal Article

Connectome spectral analysis to track EEG task dynamics on a subsecond scale

Katharina Glomb
Joan Rué Queralt
David Pascucci
Michaël Defferrard
Sébastien Tourbier
Margherita Carboni
Maria Rubega
Serge Vulliémoz

We present an approach for tracking fast spatiotemporal cortical dynamics in which we combine white matter connectivity data with source-projected electroencephalographic (EEG) data. We employ the mathematical framework of graph signal processing in order to derive the Fourier modes of the brain structural connectivity graph, or "network harmonics". These network harmonics are naturally ordered by smoothness. Smoothness in this context can be understood as the amount of variation along the cortex, leading to a multi-scale representation of brain connectivity. We demonstrate that network harmonics provide a sparse representation of the EEG signal, where, at certain times, the smoothest 15 network harmonics capture 90% of the signal power. This suggests that network harmonics are functionally meaningful, which we demonstrate by using them as a basis for the functional EEG data recorded from a face detection task. There, only 13 network harmonics are sufficient to track the large-scale cortical activity during the processing of the stimuli with a 50 ms resolution, reproducing well-known activity in the fusiform face area as well as revealing co-activation patterns in somatosensory/motor and frontal cortices that an unconstrained ROI-by-ROI analysis fails to capture. The proposed approach is simple and fast, provides a means of integration of multimodal datasets, and is tied to a theoretical framework in mathematics and physics. Thus, network harmonics point towards promising research directions both theoretically - for example in exploring the relationship between structure and function in the brain - and practically - for example for network tracking in different tasks and groups of individuals, such as patients.

YNICL Journal 2020 Journal Article

Stable biomarker identification for predicting schizophrenia in the human connectome

Leonardo Gutiérrez-Gómez
Jakub Vohryzek
Benjamin Chiêm
Philipp S. Baumann
Philippe Conus
Kim Do Cuenod
Patric Hagmann
Jean-Charles Delvenne

Schizophrenia, as a psychiatric disorder, has recognized brain alterations both at the structural and at the functional magnetic resonance imaging level. The developing field of connectomics has attracted much attention as it allows researchers to take advantage of powerful tools of network analysis in order to study structural and functional connectivity abnormalities in schizophrenia. Many methods have been proposed to identify biomarkers in schizophrenia, focusing mainly on improving the classification performance or performing statistical comparisons between groups. However, the stability of biomarkers selection has been for long overlooked in the connectomics field. In this study, we follow a machine learning approach where the identification of biomarkers is addressed as a feature selection problem for a classification task. We perform a recursive feature elimination and support vector machines (RFE-SVM) approach to identify the most meaningful biomarkers from the structural, functional, and multi-modal connectomes of healthy controls and patients. Furthermore, the stability of the retrieved biomarkers is assessed across different subsamplings of the dataset, allowing us to identify the affected core of the pathology. Considering our technique altogether, it demonstrates a principled way to achieve both accurate and stable biomarkers while highlighting the importance of multi-modal approaches to brain pathology as they tend to reveal complementary information.

YNIMG Journal 2019 Journal Article

Distinct modes of functional connectivity induced by movie-watching

Murat Demirtaş
Adrian Ponce-Alvarez
Matthieu Gilson
Patric Hagmann
Dante Mantini
Viviana Betti
Gian Luca Romani
Karl Friston

A fundamental question in systems neuroscience is how endogenous neuronal activity self-organizes during particular brain states. Recent neuroimaging studies have demonstrated systematic relationships between resting-state and task-induced functional connectivity (FC). In particular, continuous task studies, such as movie watching, speak to alterations in coupling among cortical regions and enhanced fluctuations in FC compared to the resting-state. This suggests that FC may reflect systematic and large-scale reorganization of functionally integrated responses while subjects are watching movies. In this study, we characterized fluctuations in FC during resting-state and movie-watching conditions. We found that the FC patterns induced systematically by movie-watching can be explained with a single principal component. These condition-specific FC fluctuations overlapped with inter-subject synchronization patterns in occipital and temporal brain regions. However, unlike inter-subject synchronization, condition-specific FC patterns were characterized by increased correlations within frontal brain regions and reduced correlations between frontal-parietal brain regions. We investigated these condition-specific functional variations as a shorter time scale, using time-resolved FC. The time-resolved FC showed condition-specificity over time; notably when subjects watched both the same and different movies. To explain self-organisation of global FC through the alterations in local dynamics, we used a large-scale computational model. We found that condition-specific reorganization of FC could be explained by local changes that engendered changes in FC among higher-order association regions, mainly in frontal and parietal cortices.

YNIMG Journal 2018 Journal Article

Effective connectivity inferred from fMRI transition dynamics during movie viewing points to a balanced reconfiguration of cortical interactions

Matthieu Gilson
Gustavo Deco
Karl J. Friston
Patric Hagmann
Dante Mantini
Viviana Betti
Gian Luca Romani
Maurizio Corbetta

Our behavior entails a flexible and context-sensitive interplay between brain areas to integrate information according to goal-directed requirements. However, the neural mechanisms governing the entrainment of functionally specialized brain areas remain poorly understood. In particular, the question arises whether observed changes in the regional activity for different cognitive conditions are explained by modifications of the inputs to the brain or its connectivity? We observe that transitions of fMRI activity between areas convey information about the tasks performed by 19 subjects, watching a movie versus a black screen (rest). We use a model-based framework that explains this spatiotemporal functional connectivity pattern by the local variability for 66 cortical regions and the network effective connectivity between them. We find that, among the estimated model parameters, movie viewing affects to a larger extent the local activity, which we interpret as extrinsic changes related to the increased stimulus load. However, detailed changes in the effective connectivity preserve a balance in the propagating activity and select specific pathways such that high-level brain regions integrate visual and auditory information, in particular boosting the communication between the two brain hemispheres. These findings speak to a dynamic coordination underlying the functional integration in the brain.

YNIMG Journal 2017 Journal Article

Automated template-based brain localization and extraction for fetal brain MRI reconstruction

Sébastien Tourbier
Clemente Velasco-Annis
Vahid Taimouri
Patric Hagmann
Reto Meuli
Simon K. Warfield
Meritxell Bach Cuadra
Ali Gholipour

Most fetal brain MRI reconstruction algorithms rely only on brain tissue-relevant voxels of low-resolution (LR) images to enhance the quality of inter-slice motion correction and image reconstruction. Consequently the fetal brain needs to be localized and extracted as a first step, which is usually a laborious and time consuming manual or semi-automatic task. We have proposed in this work to use age-matched template images as prior knowledge to automatize brain localization and extraction. This has been achieved through a novel automatic brain localization and extraction method based on robust template-to-slice block matching and deformable slice-to-template registration. Our template-based approach has also enabled the reconstruction of fetal brain images in standard radiological anatomical planes in a common coordinate space. We have integrated this approach into our new reconstruction pipeline that involves intensity normalization, inter-slice motion correction, and super-resolution (SR) reconstruction. To this end we have adopted a novel approach based on projection of every slice of the LR brain masks into the template space using a fusion strategy. This has enabled the refinement of brain masks in the LR images at each motion correction iteration. The overall brain localization and extraction algorithm has shown to produce brain masks that are very close to manually drawn brain masks, showing an average Dice overlap measure of 94. 5%. We have also demonstrated that adopting a slice-to-template registration and propagation of the brain mask slice-by-slice leads to a significant improvement in brain extraction performance compared to global rigid brain extraction and consequently in the quality of the final reconstructed images. Ratings performed by two expert observers show that the proposed pipeline can achieve similar reconstruction quality to reference reconstruction based on manual slice-by-slice brain extraction. The proposed brain mask refinement and reconstruction method has shown to provide promising results in automatic fetal brain MRI segmentation and volumetry in 26 fetuses with gestational age range of 23 to 38 weeks.

YNIMG Journal 2017 Journal Article

Transient networks of spatio-temporal connectivity map communication pathways in brain functional systems

Alessandra Griffa
Benjamin Ricaud
Kirell Benzi
Xavier Bresson
Alessandro Daducci
Pierre Vandergheynst
Jean-Philippe Thiran
Patric Hagmann

The study of brain dynamics enables us to characterize the time-varying functional connectivity among distinct neural groups. However, current methods suffer from the absence of structural connectivity information. We propose to integrate infra-slow neural oscillations and anatomical-connectivity maps, as derived from functional and diffusion MRI, in a multilayer-graph framework that captures transient networks of spatio-temporal connectivity. These networks group anatomically wired and temporary synchronized brain regions and encode the propagation of functional activity on the structural connectome. In a group of 71 healthy subjects, we find that these transient networks demonstrate power-law spatial and temporal size, globally organize into well-known functional systems and describe wave-like trajectories of activation across anatomically connected regions. Within the transient networks, activity propagates through polysynaptic paths that include selective ensembles of structural connections and differ from the structural shortest paths. In the light of the communication-through-coherence principle, the identified spatio-temporal networks could encode communication channels' selection and neural assemblies, which deserves further attention. This work contributes to the understanding of brain structure-function relationships by considering the time-varying nature of resting-state interactions on the axonal scaffold, and it offers a convenient framework to study large-scale communication mechanisms and functional dynamics.

YNICL Journal 2016 Journal Article

An affected core drives network integration deficits of the structural connectome in 22q11.2 deletion syndrome

František Váša
Alessandra Griffa
Elisa Scariati
Marie Schaer
Sébastien Urben
Stephan Eliez
Patric Hagmann

Chromosome 22q11.2 deletion syndrome (22q11DS) is a genetic disease known to lead to cerebral structural alterations, which we study using the framework of the macroscopic white-matter connectome. We create weighted connectomes of 44 patients with 22q11DS and 44 healthy controls using diffusion tensor magnetic resonance imaging, and perform a weighted graph theoretical analysis. After confirming global network integration deficits in 22q11DS (previously identified using binary connectomes), we identify the spatial distribution of regions responsible for global deficits. Next, we further characterize the dysconnectivity of the deficient regions in terms of sub-network properties, and investigate their relevance with respect to clinical profiles. We define the subset of regions with decreased nodal integration (evaluated using the closeness centrality measure) as the affected core (A-core) of the 22q11DS structural connectome. A-core regions are broadly bilaterally symmetric and consist of numerous network hubs - chiefly parietal and frontal cortical, as well as subcortical regions. Using a simulated lesion approach, we demonstrate that these core regions and their connections are particularly important to efficient network communication. Moreover, these regions are generally densely connected, but less so in 22q11DS. These specific disturbances are associated to a rerouting of shortest network paths that circumvent the A-core in 22q11DS, "de-centralizing" the network. Finally, the efficiency and mean connectivity strength of an orbito-frontal/cingulate circuit, included in the affected regions, correlate negatively with the extent of negative symptoms in 22q11DS patients, revealing the clinical relevance of present findings. The identified A-core overlaps numerous regions previously identified as affected in 22q11DS as well as in schizophrenia, which approximately 30-40% of 22q11DS patients develop.

YNIMG Journal 2016 Journal Article

Generative models of the human connectome

Richard F. Betzel
Andrea Avena-Koenigsberger
Joaquín Goñi
Ye He
Marcel A. de Reus
Alessandra Griffa
Petra E. Vértes
Bratislav Mišic

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.

YNIMG Journal 2015 Journal Article

An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization

Sébastien Tourbier
Xavier Bresson
Patric Hagmann
Jean-Philippe Thiran
Reto Meuli
Meritxell Bach Cuadra

Although fetal anatomy can be adequately viewed in new multi-slice MR images, many critical limitations remain for quantitative data analysis. To this end, several research groups have recently developed advanced image processing methods, often denoted by super-resolution (SR) techniques, to reconstruct from a set of clinical low-resolution (LR) images, a high-resolution (HR) motion-free volume. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has been quite attracted by Total Variation energies because of their ability in edge preserving but only standard explicit steepest gradient techniques have been applied for optimization. In a preliminary work, it has been shown that novel fast convex optimization techniques could be successfully applied to design an efficient Total Variation optimization algorithm for the super-resolution problem. In this work, two major contributions are presented. Firstly, we will briefly review the Bayesian and Variational dual formulations of current state-of-the-art methods dedicated to fetal MRI reconstruction. Secondly, we present an extensive quantitative evaluation of our SR algorithm previously introduced on both simulated fetal and real clinical data (with both normal and pathological subjects). Specifically, we study the robustness of regularization terms in front of residual registration errors and we also present a novel strategy for automatically select the weight of the regularization as regards the data fidelity term. Our results show that our TV implementation is highly robust in front of motion artifacts and that it offers the best trade-off between speed and accuracy for fetal MRI recovery as in comparison with state-of-the art methods.

YNIMG Journal 2015 Journal Article

Improved statistical evaluation of group differences in connectomes by screening–filtering strategy with application to study maturation of brain connections between childhood and adolescence

Djalel-Eddine Meskaldji
Lana Vasung
David Romascano
Jean-Philippe Thiran
Patric Hagmann
Stephan Morgenthaler
Dimitri Van De Ville

Detecting local differences between groups of connectomes is a great challenge in neuroimaging, because the large number of tests that have to be performed and the impact on multiplicity correction. Any available information should be exploited to increase the power of detecting true between-group effects. We present an adaptive strategy that exploits the data structure and the prior information concerning positive dependence between nodes and connections, without relying on strong assumptions. As a first step, we decompose the brain network, i. e. , the connectome, into subnetworks and we apply a screening at the subnetwork level. The subnetworks are defined either according to prior knowledge or by applying a data driven algorithm. Given the results of the screening step, a filtering is performed to seek real differences at the node/connection level. The proposed strategy could be used to strongly control either the family-wise error rate or the false discovery rate. We show by means of different simulations the benefit of the proposed strategy, and we present a real application of comparing connectomes of preschool children and adolescents.

YNIMG Journal 2013 Journal Article

Comparing connectomes across subjects and populations at different scales

Djalel Eddine Meskaldji
Elda Fischi-Gomez
Alessandra Griffa
Patric Hagmann
Stephan Morgenthaler
Jean-Philippe Thiran

Brain connectivity can be represented by a network that enables the comparison of the different patterns of structural and functional connectivity among individuals. In the literature, two levels of statistical analysis have been considered in comparing brain connectivity across groups and subjects: 1) the global comparison where a single measure that summarizes the information of each brain is used in a statistical test; 2) the local analysis where a single test is performed either for each node/connection which implies a multiplicity correction, or for each group of nodes/connections where each subset is summarized by one single test in order to reduce the number of tests to avoid a penalizing multiplicity correction. We comment on the different levels of analysis and present some methods that have been proposed at each scale. We highlight as well the possible factors that could influence the statistical results and the questions that have to be addressed in such an analysis.

YNIMG Journal 2013 Journal Article

Structural connectomics in brain diseases

Alessandra Griffa
Philipp S. Baumann
Jean-Philippe Thiran
Patric Hagmann

Imaging the connectome in vivo has become feasible through the integration of several rapidly developing fields of science and engineering, namely magnetic resonance imaging and in particular diffusion MRI on one side, image processing and network theory on the other side. This framework brings in vivo brain imaging closer to the real topology of the brain, contributing to narrow the existing gap between our understanding of brain structural organization on one side and of human behavior and cognition on the other side. Given the seminal technical progresses achieved in the last few years, it may be ready to tackle even greater challenges, namely exploring disease mechanisms. In this review we analyze the current situation from the technical and biological perspectives. First, we critically review the technical solutions proposed in the literature to perform clinical studies. We analyze for each step (i. e. MRI acquisition, network building and network statistical analysis) the advantages and potential limitations. In the second part we review the current literature available on a selected subset of diseases, namely, dementia, schizophrenia, multiple sclerosis and others, and try to extract for each disease the common findings and main differences between reports.

YNIMG Journal 2012 Journal Article

Classifying minimally disabled multiple sclerosis patients from resting state functional connectivity

Jonas Richiardi
Markus Gschwind
Samanta Simioni
Jean-Marie Annoni
Beatrice Greco
Patric Hagmann
Myriam Schluep
Patrik Vuilleumier

Multiple sclerosis (MS), a variable and diffuse disease affecting white and gray matter, is known to cause functional connectivity anomalies in patients. However, related studies published to-date are post hoc; our hypothesis was that such alterations could discriminate between patients and healthy controls in a predictive setting, laying the groundwork for imaging-based prognosis. Using functional magnetic resonance imaging resting state data of 22 minimally disabled MS patients and 14 controls, we developed a predictive model of connectivity alterations in MS: a whole-brain connectivity matrix was built for each subject from the slow oscillations (<0. 11Hz) of region-averaged time series, and a pattern recognition technique was used to learn a discriminant function indicating which particular functional connections are most affected by disease. Classification performance using strict cross-validation yielded a sensitivity of 82% (above chance at p <0. 005) and specificity of 86% (p <0. 01) to distinguish between MS patients and controls. The most discriminative connectivity changes were found in subcortical and temporal regions, and contralateral connections were more discriminative than ipsilateral connections. The pattern of decreased discriminative connections can be summarized post hoc in an index that correlates positively (ρ =0. 61) with white matter lesion load, possibly indicating functional reorganisation to cope with increasing lesion load. These results are consistent with a subtle but widespread impact of lesions in white matter and in gray matter structures serving as high-level integrative hubs. These findings suggest that predictive models of resting state fMRI can reveal specific anomalies due to MS with high sensitivity and specificity, potentially leading to new non-invasive markers.

YNIMG Journal 2006 Journal Article

Fibertract segmentation in position orientation space from high angular resolution diffusion MRI

Patric Hagmann
Lisa Jonasson
Thomas Deffieux
Reto Meuli
Jean-Philippe Thiran
Van J. Wedeen

In diffusion MRI, standard approaches for fibertract identification are based on algorithms that generate lines of coherent diffusion, currently known as tractography. A tract is then identified as a set of such lines selected on some criteria. In the present study, we investigate whether fibertract identification can be formulated as a segmentation task that recognizes a fibertract as a region where diffusion is intense and coherent. Indeed, we show that it is possible to segment efficiently well-known fibertracts with classical image processing methods provided that the problem is formulated in a five-dimensional space of position and orientation. As an example, we choose to adapt to this newly defined high-dimensional non-Euclidean space, called position orientation space, an algorithm based on the hidden Markov random field framework. Structures such as the cerebellar peduncles, corticospinal tract, association bundles can be identified and represented in three dimensions by a back projection technique similar to maximum intensity projection. Potential advantages and drawbacks as compared to classical tractography are discussed; for example, it appears that our formulation handles naturally crossing tracts and is not biased by human intervention.