Biology subjects

Hagmann, P.

Publications and source records attributed to Hagmann, P..

32 records · Page 2Linked to original sources

bioRxiv2021

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

The functional organization of neural processes is constrained by the brain s intrinsic structural connectivity. Here, we explore the potential of exploiting this structure in order to improve the signal representation properties of brain activity and its 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. The harmonic modes are termed connectome harmonics, and their representation is known as the connectome spectrum of the signal. We found that: first, the brain activity signal is more compactly represented 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 characterises fast brain dynamics with fewer degrees of freedom than area-based signal representations. Specifically, we show that with the connectome spectrum representation, fewer dimensions are needed to capture the differences between low-level and high-level visual processing, and the topological properties of the signal. In summary, this work provides statistical, functional and topological evidence supporting that by accounting for the brain s structural connectivity fosters a more comprehensive understanding of large-scale dynamic neural functioning.

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bioRxiv2020

Multiscale communication in cortico-cortical networks

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. By investigating the propensity for brain areas to communicate with their neighbors across multiple scales, we naturally reveal their functional diversity. We find that 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 how functional hierarchies emerge from hidden but highly structured multiscale connection patterns.

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bioRxiv2020

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

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.

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bioRxiv2020

Signal propagation via cortical hierarchies

The wiring of the brain is organized around a putative unimodal-transmodal hierarchy. Here we investigate how this intrinsic hierarchical organization of the brain shapes the transmission of information among regions. The hierarchical positioning of individual regions was quantified by applying diffusion map embedding to resting state functional MRI networks. Structural networks were reconstructed from diffusion spectrum imaging and topological shortest paths among all brain regions were computed. Sequences of nodes encountered along a path were labelled by their hierarchical position, tracing out path motifs. We find that the cortical hierarchy guides communication in the network. Specifically, nodes are more likely to forward signals to nodes closer in the hierarchy and cover a range of unimodal and transmodal regions, potentially enriching or diversifying signals en route. We also find evidence of systematic detours, particularly in attention networks, where communication is re-routed. Altogether, the present work highlights how the cortical hierarchy shapes signal exchange and imparts behaviourally-relevant communication patterns in brain networks.

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bioRxiv2019

Using structural connectivity to augment community structure in EEG functional connectivity

Recently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle from spurious FC introduced by volume conduction. Here, we investigate the relationship between white matter structural connectivity (SC) and large scale network structure encoded in EEG-FC. We start by confirming that FC (power envelope correlations) is predicted by SC beyond the impact of Euclidean distance, in line with the assumption that SC mediates genuine FC. We then use information from white matter structural connectivity (SC) in order to smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions increases while FC between non-connected regions remains unchanged, resulting in an increase in genuine, SC-mediated FC. We analyze the induced changes in FC, assessing the resemblance between EEG- and volume-conduction-free fMRI-FC, and find that smoothing increases resemblance in terms of overall correlation and community structure. This result suggests that our method boosts genuine FC, an outcome that is of interest for many EEG network neuroscience questions. Author summaryIn this study, we combine high-density EEG recorded during resting state with white matter connectivity obtained from diffusion MRI and fiber tracking. We leverage the additional information contained in the structural connectome towards augmenting the source level EEG functional connectivity. In particular, it is known - and confirmed in this study - that the activity of brain regions that possess a direct anatomical connection is, on average, more strongly correlated than that of regions that have no such direct link. We use the structural connectome to define a graph and smooth the source reconstructed EEG signal in the space spanned by this graph. We compare the resulting "filtered" signal correlation matrices to those obtained from fMRI and find that such "graph filtering" improves the agreement between EEG and fMRI functional connectivity structure. This suggests that structural connectivity can be used to attenuate some of the limitations imposed by volume conduction.

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bioRxiv2019

Stable Biomarker Identification For Predicting Schizophrenia in the Human Connectome

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 connec-tomics 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.

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bioRxiv2019

Functional harmonics reveal multi-dimensional basis functions underlying cortical organization

The human brain consists of functionally specialized areas, which flexibly interact and integrate forming a multitude of complex functional networks. However, the nature and governing principles of these specialized areas remain controversial: a distinct modular architecture versus a smooth continuum across the whole cortex. Here, we demonstrate a candidate governing principle ubiquitous in nature, that resolves this controversy for the brain at rest, during perception, cognition and action: functional harmonic modes. We calculated the harmonic modes of the brains functional connectivity, called "functional harmonics", from functional magnetic resonance imaging (fMRI) data in resting state of 812 participants. Each functional harmonic provides an elementary pattern of brain activity with a different spatial frequency. The set of all functional harmonics - ordered according to their spatial frequencies - can reconstruct any pattern of brain activity. The activity patterns elicited by 7 different tasks from the Human Connectome Project can be reconstructed from a very small subset of functional harmonics, suggesting a novel relationship between task and resting state brain activity. Further, the isolines of the continuous functional harmonic patterns delineate the borders of specialized cortical areas as well as somatotopic and retinotopic organization. Our results demonstrate a candidate scalable governing principle for functional brain organization, resolving the controversy between modular versus gradiental views, and demonstrate that a universal principle in nature also underlies human brain cortical organization.

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bioRxiv2019

Spatial patterning of tissue volume deformation in schizophrenia reflects brain network architecture

BackgroundThere is growing recognition that connectome architecture shapes cortical and sub-cortical grey matter atrophy across a spectrum of neurological and psychiatric diseases. Whether connectivity contributes to tissue volume loss in schizophrenia in the same manner remains unknown.\n\nMethodsHere we relate tissue volume loss in patients with schizophrenia to patterns of structural and functional connectivity. Grey matter deformation was estimated in a sample of N = 133 individuals with chronic schizophrenia (48 female, 34.7 {+/-} 12.9 years) and N = 113 controls (64 female, 23.5 {+/-} 8.4 years). Deformation-based morphometry (DBM) was used to estimate cortical and subcortical grey matter deformation from T1-weighted MR images. Structural and functional connectivity patterns were derived from an independent sample of N = 70 healthy participants using diffusion spectrum imaging and resting-state functional MRI.\n\nResultsWe find that regional deformation is correlated with the deformation of structurally- and functionally-connected neighbours. Distributed deformation patterns are circumscribed by specific functional systems (the ventral attention network) and cytoarchitectonic classes (limbic class), with an epicenter in the anterior cingulate cortex.\n\nConclusionsAltogether, the present study demonstrates that brain tissue volume loss in schizophrenia is conditioned by structural and functional connectivity, accounting for 25-35% of regional variance in deformation.

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bioRxiv2019

Gradients of structure-function tethering across neocortex

The white matter architecture of brain networks imparts a distinct signature on neuronal co-activation patterns. Inter-regional projections promote synchrony among distant neuronal populations, giving rise to richly patterned functional networks. A variety of statistical, communication and biophysical models have been proposed to study the relationship between brain structure and function, but the link is not yet known. In the present report we seek to relate the structural and functional connection profiles of individual brain areas. We apply a simple multilinear model that incorporates information about spatial proximity, routing and diffusion between brain regions to predict their functional connectivity. We find that structure-function relationships vary markedly across the neocortex. Structure and function correspond closely in unimodal, primary sensory and motor regions, but diverge in transmodal cortex, corresponding to the default mode and salience networks. The divergence between structure and function systematically follows functional and cytoarchitectonic hierarchies. Altogether, the present results demonstrate that structural and functional networks do not align uniformly across the brain, but gradually uncouple in higher-order polysensory areas.

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bioRxiv2018

Distance-dependent consistency thresholds for generating group-representative structural brain networks

Large-scale structural brain networks encode white-matter connectivity patterns among distributed brain areas. These connection patterns are believed to support cognitive processes and, when compromised, can lead to neurocognitive deficits and maladaptive behavior. A powerful approach for studying the organizing principles of brain networks is to construct group-representative networks from multi-subject cohorts. Doing so amplifies signal to noise ratios and provides a clearer picture of brain network organization. Here, we show that current approaches for generating grouprepresentative networks over-estimate the proportion of short-range connections present in a network and, as a result, fail to match subject-level networks along a wide range of network statistics. We present an alternative approach that preserves the connection-length distribution of individual subjects. Due to this simple modification, the networks generated using this novel approach successfully recapitulate subject-level properties, outperforming all existing approaches by better preserving features that promote integrative brain function rather than segregative. The method developed here holds promise for future studies investigating basic organizational principles and features of largescale structural brain networks.

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bioRxiv2018

Distinct Functional Connectivity Mode during Viewing Natural Scenes Revealed by Principal Component Analysis

A fundamental question in systems neuroscience is how spontaneous activity at rest is reorganized during task performance. Recent studies suggest a strong relationship between resting and task FC. Furthermore, the relationship between resting and task FC has been shown to reflect individual differences. Particularly, various studies have demonstrated that the FC has higher reliability and provides enhanced detection of individual differences while viewing natural scenes. Although the large-scale organization of FC during rest and movie-viewing conditions have been well studied in relation to individual variations, the re-organization of FC during viewing natural scenes have not been studied in depth. In this study, we used principal component analysis on FC during rest and movie-viewing condition to characterize the dimensionality of FC patterns across conditions and subjects. We found that the variations in FC patterns related to viewing natural scenes can be explained by a single component, which enables identification of the task over subjects with 100% accuracy. We showed that the FC mode associated to viewing natural scenes better reflects individual variations. Furthermore, we investigated the signatures of movie-viewing-specific functional modes in dynamic FC based on phase-locking values between brain regions. We found that the movie-specific functional mode is persistent across time; suggesting the emergence of a stable processing mode. To explain the reorganization of whole-brain FC through the changes in local dynamics, we appeal to a large-scale computational model. This modelling suggested that the reorganization of whole-brain FC is associated to the interaction between frontal-parietal and frontal-temporal activation patterns.

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bioRxiv2018

Network-based asymmetry of the human auditory system

Converging evidence from activation, connectivity and stimulation studies suggests that auditory brain networks are lateralized. Here we show that these findings can be at least partly explained by the asymmetric network embedding of the primary auditory cortices. Using diffusion-weighted imaging in three independent datasets, we investigate the propensity for left and right auditory cortex to communicate with other brain areas by quantifying the centrality of the auditory network across a spectrum of communication mechanisms, from shortest path communication to diffusive spreading. Across all datasets, we find that the right auditory cortex is better integrated in the connectome, facilitating more efficient communication with other areas, with much of the asymmetry driven by differences in communication pathways to the opposite hemisphere. Critically, the primacy of the right auditory cortex emerges only when communication is conceptualized as a diffusive process, taking advantage of more than just the topologically shortest paths in the network. Altogether, these results highlight how the network configuration and embedding of a particular region may contribute to its functional lateralization.

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bioRxiv2017

Network Connectivity Predicts Cortical Thinning and Cognitive Decline in Early Parkinson’s Disease

Parkinsons Disease (PD) is a progressive neurodegenerative disorder characterized by motor and cognitive deficits. The neurodegenerative process is thought to move stereotypically from the brainstem up to the cerebral cortex, possibly reflecting the spread of toxic alpha-synuclein molecules. Using a large, longitudinal, multi-center database of de novo PD patients, we tested whether focal reductions in cortical thickness could be explained by disease spread from a subcortical \"disease reservoir\" along the brains connectome. PD patients (n=105) and matched controls (n=57) underwent T1-MRI at entry and one year later. Over this period, PD patients demonstrated significantly greater loss of cortical thickness than healthy controls in parts of the left occipital and bilateral frontal lobes and right somatomotor-sensory cortex. Cortical regions with greater connectivity (measured functionally or structurally) to a \"disease reservoir\" evaluated via MRI at baseline demonstrated greater atrophy one year later. The atrophy pattern in the ventral frontal lobes resembled one described in certain cases of Alzheimers disease. Moreover, a multiple linear regression model suggested that cortical thinning was associated with impaired cognitive function at follow-up. Our findings suggest that disease propagation to the cortex in PD follows neural connectivity, and that disease spread to the cortex may herald the onset of cognitive impairment.

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bioRxiv2017

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

Our behavior entails a flexible and context-sensitive interplay between brain areas to integrate information according to goal-directed requirements. How-ever, 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.

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