Publications associées à Longitudinal Analysis of Brain Function-Structure Dependencies in 22q11.2 Deletion Syndrome and Psychotic Symptoms

Publications associées (12)

Voxel-Wise Brain Graphs From Diffusion MRI: Intrinsic Eigenspace Dimensionality and Application to Functional MRI

Goal: Structural brain graphs are conventionally limited to defining nodes as gray matter regions from an atlas, with edges reflecting the density of axonal projections between pairs of nodes. Here we explicitly model the entire set of voxels within a brai ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2025

Individually optimized dynamic parallel transmit pulses for 3D high‐resolution SPACE imaging at 7T

Emilie Claire Sleight, Natalia Pato Montemayor, Bénédicte Marie Maréchal, Tom Hilbert, Thomas Yu, Tommaso Di Noto, Jocelyn Philippe, Lina Bacha

Purpose: Although clinical 7T MRI offers various advantages compared to lower field strengths, achieving spatially uniform flip angle distributions remains a challenge. Sampling Perfection with Application optimized Contrast using different flip angle Evol ...

Wiley2025

Neural anticipation of virtual infection triggers an immune response

Tommaso Enrico Bertoni

Once contact with a pathogen has occurred, it might be too late for the immune system to react. Here, we asked whether anticipatory neural responses might sense potential infections and signal to the immune system, priming it for a response. We show that p ...

2025

Informed Graph Learning By Domain Knowledge Injection and Smooth Graph Signal Representation

Olga Fink, Lukas Kühn, Gaëtan Michel Frusque, Keivan Faghih Niresi

Graph signal processing represents an important advancement in the field of data analysis, extending conventional signal processing methodologies to complex networks and thereby facilitating the exploration of informative patterns and structures across var ...

IEEE2024

BiomedBench: A benchmark suite of TinyML biomedical applications for low-power wearables

Dimitrios Samakovlis, David Atienza Alonso, Miguel Peon Quiros, Pasquale Davide Schiavone, Rubén Rodríguez Álvarez, Denisa-Andreea Constantinescu, Stefano Albini

The design of low-power wearables for the biomedical domain has received a lot of attention in recent decades, as technological advances in chip manufacturing have allowed real-time monitoring of patients using low-complexity ML within the mW range. Despit ...

Institute of Electrical and Electronics Engineers (IEEE)2024

Diffusion of brain metabolites highlights altered brain microstructure in type C hepatic encephalopathy: a 9.4 T preliminary study

Cristina Ramona Cudalbu Moldovan, Dunja Simicic, Katarzyna Pierzchala, Jessie Julie Mosso, Guillaume Miro André Briand

Introduction Type C hepatic encephalopathy (HE) is a decompensating event of chronic liver disease leading to severe motor and cognitive impairment. The progression of type C HE is associated with changes in brain metabolite concentrations measured by 1H m ...

Frontiers Media Sa2024

Noninvasive modulation of the hippocampal-entorhinal complex during spatial navigation in humans

Olaf Blanke, Friedhelm Christoph Hummel, Maximilian Jonas Wessel, Elena Beanato, Traian Popa, Pierre Theopistos Vassiliadis, Baptiste Gauthier, Emanuela De Falco, Fabienne Windel, Pauline Menoud, Hyukjun Moon, Esra Neufeld

Because of the depth of the hippocampal-entorhinal complex (HC-EC) in the brain, understanding of its role in spatial navigation via neuromodulation was limited in humans. Here, we aimed to better elucidate this relationship in healthy volunteers, using tr ...

American Association for the Advancement of Science (AAAS)2024

Modeling and simulation of neocortical micro- and mesocircuitry. Part II: Physiology and experimentation

Weina Ji, Henry Markram, Felix Schürmann, Daniel Keller, Eilif Benjamin Muller, Michael Reimann, Werner Alfons Hilda Van Geit, Srikanth Ramaswamy, Matthias Wolf, James Gonzalo King, Alexis Arnaudon, András Ecker, Rajnish Ranjan, Jean-Denis Georges Emile Courcol, Armando Romani, Pramod Shivaji Kumbhar, Judit Planas Carbonell, Giuseppe Chindemi, Michael Emiel Gevaert, Christian Andreas Rössert, Fernando Joaquim Leite Pereira, Omar Awile, Mustafa Anil Tuncel, Daniela Egas Santander, James Bryden Isbister, Natali Barros Zulaica, Samuel Lieven D. Lapere, Sirio Bolaños Puchet, Maria Reva, Genrich Ivaska, Tanguy Pierre Louis Damart, Darshan Mandge, Joni Henrikki Herttuainen, Christoph Pokorny, Elvis Boci, Vishal Sood, Thomas Brice Delemontex, Aleksandra Zuzanna Teska, Polina Litvak, Alexander Dietz, Jorge Blanco Alonso, Gianluca Ficarelli

Cortical dynamics underlie many cognitive processes and emerge from complex multi-scale interactions, which are challenging to study in vivo. Large-scale, biophysically detailed models offer a tool which can complement laboratory approaches. We present a m ...

2024

MEP and TEP features variability: is it just the brain-state?

Friedhelm Christoph Hummel, Estelle Emeline Raffin, Sylvain Jean-François Harquel, Andéol Geoffroy Cadic-Melchior, Michele Bevilacqua, Claudia Bigoni

Objective. The literature investigating the effects of alpha oscillations on corticospinal excitability is divergent. We believe inconsistency in the findings may arise, among others, from the electroencephalography (EEG) processing for brain-state determi ...

Iop Publishing Ltd2024

A connectome manipulation framework for the systematic and reproducible study of structure–function relationships through simulations

Michael Reimann, Matthias Wolf, Omar Awile, James Bryden Isbister, Christoph Pokorny, Mustafa Kerem Kurban

Synaptic connectivity at the neuronal level is characterized by highly non-random features. Hypotheses about their role can be developed by correlating structural metrics to functional features. But to prove causation, manipulations of connectivity would h ...

2024

Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse

Silvestro Micera

Neural decoding of the visual system is a subject of research interest, both to understand how the visual system works and to be able to use this knowledge in areas, such as computer vision or brain-computer interfaces. Spike-based decoding is often used, ...

Ieee-Inst Electrical Electronics Engineers Inc2024

High-performance deep spiking neural networks with 0.3 spikes per neuron

Wulfram Gerstner, Stanislaw Andrzej Wozniak, Guillaume Emmanuel Fernand Bellec, Ana Stanojevic, Angeliki Pantazi

Communication by rare, binary spikes is a key factor for the energy efficiency of biological brains. However, it is harder to train biologically-inspired spiking neural networks than artificial neural networks. This is puzzling given that theoretical resul ...

Springer Science and Business Media LLC2024