arXiv subjects

Vincent Wens

Publications and source records attributed to Vincent Wens.

7 recordsLinked to original sources

arXiv2024

STIED: A deep learning model for the SpatioTemporal detection of focal Interictal Epileptiform Discharges with MEG

Magnetoencephalography (MEG) allows the non-invasive detection of interictal epileptiform discharges (IEDs). Clinical MEG analysis in epileptic patients traditionally relies on the visual identification of IEDs, which is time consuming and partially subjective. Automatic, data-driven detection methods exist but show limited performance. Still, the rise of deep learning (DL)-with its ability to reproduce human-like abilities-could revolutionize clinical MEG practice. Here, we developed and validated STIED, a simple yet powerful supervised DL algorithm combining two convolutional neural networks with temporal (1D time-course) and spatial (2D topography) features of MEG signals inspired from current clinical guidelines. Our DL model enabled both temporal and spatial localization of IEDs in patients suffering from focal epilepsy with frequent and high amplitude spikes (FE group), with high-performance metrics-accuracy, specificity, and sensitivity all exceeding 85%-when learning from spatiotemporal features of IEDs. This performance can be attributed to our handling of input data, which mimics established clinical MEG practice. Reverse engineering further revealed that STIED encodes fine spatiotemporal features of IEDs rather than their mere amplitude. The model trained on the FE group also showed promising results when applied to a separate group of presurgical patients with different types of refractory focal epilepsy, though further work is needed to distinguish IEDs from physiological transients. This study paves the way of incorporating STIED and DL algorithms into the routine clinical MEG evaluation of epilepsy.

physics.med-ph↗

arXiv2023

Exploring the limits of MEG spatial resolution with multipolar expansions

The advent of scalp magnetoencephalography (MEG) based on optically pumped magnetometers (OPMs) may represent a step change in the field of human electrophysiology. Compared to cryogenic MEG based on superconducting quantum interference devices, scalp MEG promises significantly higher spatial resolution imaging but it also comes with numerous challenges regarding how to optimally design OPM arrays. In this context, we sought to provide a systematic description of MEG spatial resolution as a function of the number of sensors, sensor-to-brain distance, sensor type, and signal-to-noise ratio. To that aim, we present an analytical theory based on MEG multipolar expansions that enables, once supplemented with experimental input and simulations, quantitative assessment of the limits of MEG spatial resolution in terms of two qualitatively distinct regimes. In the regime of asymptotically high-density MEG, we provide a mathematically rigorous description of how magnetic field smoothness constraints spatial resolution to a slow, logarithmic divergence. In the opposite regime of low-density MEG, it is sensor density that constraints spatial resolution to a faster increase following a square-root law. The transition between these two regimes controls how MEG spatial resolution saturates as sensors approach sources of neural activity. This two-regime model of MEG spatial resolution integrates known observations (e.g., the difficulty of improving spatial resolution by increasing sensor density, the gain brought by moving sensors on scalp, or the usefulness of multi-component sensors) and gathers them under a unifying theoretical framework that highlights the underlying physics and reveals properties inaccessible to simulations. We propose that this framework may find useful applications to benchmark the design of future OPM-based scalp MEG systems.

physics.med-ph↗

arXiv2018

Brownian forgery of statistical dependences

The balance held by Brownian motion between temporal regularity and randomness is embodied in a remarkable way by Levy's forgery of continuous functions. Here we describe how this property can be extended to forge arbitrary dependences between two statistical systems, and then establish a new Brownian independence test based on fluctuating random paths. We also argue that this result allows revisiting the theory of Brownian covariance from a physical perspective and opens the possibility of engineering nonlinear correlation measures from more general functional integrals.

cond-mat.stat-mech↗

arXiv2015

Investigating complex networks with inverse models: analytical aspects of spatial leakage and connectivity estimation

Network theory and inverse modeling are two standard tools of applied physics, whose combination is needed when studying the dynamical organization of spatially distributed systems from indirect measurements. However, the associated connectivity estimation may be affected by spatial leakage, an artifact of inverse modeling that limits the interpretability of network analysis. This paper investigates general analytical aspects pertaining to this issue. First, the existence of spatial leakage is derived from the topological structure of inverse operators. Then, the geometry of spatial leakage is modeled and used to define a geometric correction scheme, which limits spatial leakage effects in connectivity estimation. Finally, this new approach for network analysis is compared analytically to existing methods based on linear regressions, which are shown to yield biased coupling estimates.

physics.data-an↗

arXiv2009

Flavors in the microscopic approach to N=1 gauge theories

In this note, we solve an extended version of the N=1 super Yang-Mills theory with gauge group U(N), an adjoint chiral multiplet and Nf flavors of quarks, by using the N=1 microscopic formalism based on Nekrasov's sums over colored partitions. Our main new result is the computation of the general mesonic operators. We prove that the generalized Konishi anomaly equations with flavors are satisfied at the non-perturbative level. This yields in particular a microscopic, first principle derivation of the matrix model disk diagram contributions that must be included in the Dijkgraaf-Vafa approach.

hep-th↗

arXiv2008

Consistency conditions in the chiral ring of super Yang-Mills theories

Starting from the generalized Konishi anomaly equations at the non-perturbative level, we demonstrate that the algebraic consistency of the quantum chiral ring of the N=1 super Yang-Mills theory with gauge group U(N), one adjoint chiral superfield X and N_f<=2N flavours of quarks implies that the periods of the meromorphic one-form Tr dz/(z-X) must be quantized. This shows in particular that identities in the open string description of the theory, that follow from the fact that gauge invariant observables are expressed in terms of gauge variant building blocks, are mapped onto non-trivial dynamical equations in the closed string description.

hep-th↗

arXiv2007

Glueball operators and the microscopic approach to N=1 gauge theories

We explain how to generalize Nekrasov's microscopic approach to N=2 gauge theories to the N=1 case, focusing on the typical example of the U(N) theory with one adjoint chiral multiplet X and an arbitrary polynomial tree-level superpotential Tr W(X). We provide a detailed analysis of the generalized glueball operators and a non-perturbative discussion of the Dijkgraaf-Vafa matrix model and of the generalized Konishi anomaly equations. We compute in particular the non-trivial quantum corrections to the Virasoro operators and algebra that generate these equations. We have performed explicit calculations up to two instantons, that involve the next-to-leading order corrections in Nekrasov's Omega-background.

hep-th↗