Publications associées à Altered structural connectivity in patients with medial temporal lobe epilepsy: A Diffusion Spectrum Imaging and Graph Analysis study

Publications associées (32)

Approximating Dasgupta Cost in Sublinear Time from a Few Random Seeds

Mikhail Kapralov, Weronika Wrzos-Kaminska

Testing graph cluster structure has been a central object of study in property testing since the foundational work of Goldreich and Ron [STOC’96] on expansion testing, i.e. the problem of distinguishing between a single cluster (an expander) and a graph th ...

Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing2025

White adipose tissue distribution and amount are associated with increased white matter connectivity

Jean-Philippe Thiran, Elda Fischi Gomez, Gabriel Girard, Philipp Johannes Koch

Obesity represents a significant public health concern and is linked to various comorbidities and cognitive impairments. Previous research indicates that elevated body mass index (BMI) is associated with structural changes in white matter (WM). However, a ...

Wiley2024

Development and clinical validation of computational imaging biomarkers for neurodegenerative diseases

Veronica Lily Ravano

Neurodegenerative and neuroinflammatory disorders often involve complex pathophysiological mechanisms that are â to this date â only partially understood. A more comprehensive understanding of those microstructural processes and their characterization ...

EPFL2024

Brain Fingerprinting Using Fmri Spectral Signatures On High-Resolution Cortical Graphs

Dimitri Nestor Alice Van De Ville, Hamid Behjat, Maria Giulia Preti, Stefano Moia

Resting-state fMRI has proven to entail subject-specific signatures that can serve as a fingerprint to identify individuals. Conventional methods are based on building a connectivity matrix based on correlation between the average time course of pairs of b ...

IEEE2023

GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation

Daniel Gatica-Perez, Sina Sajadmanesh

In this paper, we study the problem of learning Graph Neural Networks (GNNs) with Differential Privacy (DP). We propose a novel differentially private GNN based on Aggregation Perturbation (GAP), which adds stochastic noise to the GNN's aggregation functio ...

Usenix Assoc2023

Privacy-Preserving Machine Learning on Graphs

Sina Sajadmanesh

Graph Neural Networks (GNNs) have emerged as a powerful tool for learning on graphs, demonstrating exceptional performance in various domains. However, as GNNs become increasingly popular, new challenges arise. One of the most pressing is the need to ensur ...

EPFL2023

Equivariant Neural Architectures for Representing and Generating Graphs

Clément Arthur Yvon Vignac

Graph machine learning offers a powerful framework with natural applications in scientific fields such as chemistry, biology and material sciences. By representing data as a graph, we encode the prior knowledge that the data is composed of a set of entiti ...

EPFL2023

Redox and Immune Signaling in Schizophrenia: New Therapeutic Potential

Lijing Xin

Redox biology and immune signaling play major roles in the body, including in brain function. A rapidly growing literature also suggests that redox and immune abnormalities are implicated in neuropsychiatric conditions such as schizophrenia (SZ), bipolar d ...

OXFORD UNIV PRESS2023

Scalable maximal subgraph mining with backbone-preserving graph convolutions

Karl Aberer, Quoc Viet Hung Nguyen, Thành Tâm Nguyên, Thanh Trung Huynh

Maximal subgraph mining is increasingly important in various domains, including bioinformatics, genomics, and chemistry, as it helps identify common characteristics among a set of graphs and enables their classification into different categories. Existing ...

ELSEVIER SCIENCE INC2023

A parcellation scheme of mouse isocortex based on reversals in connectivity gradients

Michael Reimann

The brain is composed of several anatomically clearly separated structures. This parcellation is often extended into the isocortex, based on anatomical, physiological or functional differences. Here, we derive a parcellation scheme based purely on the spat ...

2023

Linking connectivity of deep brain stimulation of nucleus accumbens area with clinical depression improvements: a retrospective longitudinal case series

Dimitri Nestor Alice Van De Ville, Maria Giulia Preti

Treatment-resistant depression is a severe form of major depressive disorder and deep brain stimulation is currently an investigational treatment. The stimulation's therapeutic effect may be explained through the functional and structural connectivities be ...

Springer Heidelberg2024

Cortical cell assemblies and their underlying connectivity: an in silico study

Michael Reimann, András Ecker, Daniela Egas Santander, Sirio Bolaños Puchet, James Bryden Isbister

Recent developments in experimental techniques have enabled simultaneous recordings from thousands of neurons, enabling the study of functional cell assemblies. However, determining the patterns of synaptic connectivity giving rise to these assemblies rema ...

2024

A full characterization of invariant embeddability of unimodular planar graphs

Laszlo Marton Toth

When can a unimodular random planar graph be drawn in the Euclidean or the hyperbolic plane in a way that the distribution of the random drawing is isometry-invariant? This question was answered for one-ended unimodular graphs in Benjamini and Timar, using ...

WILEY2023

Structure-function coupling increases during interictal spikes in temporal lobe epilepsy: A graph signal processing study

Patric Hagmann, Dimitri Nestor Alice Van De Ville, Maria Giulia Preti

Objective: Structure-function coupling remains largely unknown in brain disorders. We studied this coupling during interictal epileptic discharges (IEDs), using graph signal processing in temporal lobe epilepsy (TLE). ...

ELSEVIER IRELAND LTD2023

Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining

Qiang Liu, Yichen Xu

Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous Graph Neural Networks (GNN) require a large number of labeled ...

Assoc Computing Machinery2023

Prediction of post-stroke motor recovery benefits from measures of sub-acute widespread network damages

Dimitri Nestor Alice Van De Ville, Elvira Pirondini, Maria Giulia Preti

Rivier et al. report that global measures of brain connectivity estimated by introducing virtual lesions in healthy structural connectomes significantly increase the accuracy of motor recovery prediction in stroke patients when added to focal and clinical ...

OXFORD UNIV PRESS2023

Engram cell connectivity as a mechanism for information encoding and memory function

Maurizio Ferdinando Pezzoli

Information derived from experiences is incorporated into the brain as changes to ensembles of cells, termed engram cells, which allow memory storage and recall. The mechanism by which those changes hold specific information is unclear. Here, we test the h ...

Cell Press2023

How to combine the use of intraoperative magnetic resonance imaging (MRI) and awake craniotomy for microsurgical resection of hemorrhagic cavernous malformation in eloquent area: a case report

Constantin Tuleasca

BackgroundCavernous malformations are clusters of abnormal and hyalinized capillaries without interfering brain tissue. Here, we present a cavernous malformation operated under awake conditions, due to location, in an eloquent area and using intraoperative ...

2023

Differential Impact of Brain Network Efficiency on Poststroke Motor and Attentional Deficits

Dimitri Nestor Alice Van De Ville, Philip Egger, Nathalie Heidi Meyer, Elena Beanato, Gabriel Girard, Julia Brügger, Friedhelm Christoph Hummel, Takuya Morishita, Maximilian Jonas Wessel, Philipp Johannes Koch, Giorgia Giulia Evangelista, Andéol Geoffroy Cadic-Melchior, Diego de Leon Rodriguez, Martino Bruno Armando Ceroni, Lisa Aïcha Mireille Julie Fleury

Background:Most studies on stroke have been designed to examine one deficit in isolation; yet, survivors often have multiple deficits in different domains. While the mechanisms underlying multiple-domain deficits remain poorly understood, network-theoretic ...

2023

Nonperturbative aspects of scattering amplitudes

Miguel Alexandre Ribeiro Correia

In this thesis we study how physical principles imposed on the S-matrix, such as Lorentz invariance, unitarity, crossing symmetry and analyticity constrain quantum field theories at the nonperturbative level. We start with a pedagogical introduction to the ...

EPFL2023