Neurobiological and metabolic basis of motivation and effort-based decision-making - A human 1H-MRS, fMRI and machine learning study

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Recent research shows prominent effects of pregnancy and the parenthood transition on structural brain characteristics in humans. Here, we present a comprehensive study of how parental status and number of children born/fathered links to markers of brain a ...

This study introduces a Machine Learning (ML) approach for estimating the T2 spectrum and myelin water fraction (MWF) using multi-echo T2 (MET2) data from preclinical 7T Magnetic Resonance Imaging (MRI) scanners. ML methods have shown promise in MWF estima ...

Conducting neutron scattering experiments in the presence of high pulsed magnetic fields, namely above 40 T, provides valuable information about the magnetic structures of materials. However, these experiments are challenging and time-consuming because the ...

This article describes a field-based analytical model of single-sided linear induction motors (SLIMs) that explicitly considers the following effects altogether: finite motor length, magnetomotive force mmf space harmonics, slot effect, edge effect, and ta ...

Studying the intricacies of individual subjects' moods and cognitive processing over extended periods of time presents a formidable challenge in medicine. While much of systems neuroscience appropriately focuses on the link between neural circuit functions ...

Data-driven approaches have been applied to reduce the cost of accurate computational studies on materials, by using only a small number of expensive reference electronic structure calculations for a representative subset of the materials space, and using ...

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 ...

Copyright © 2024 Meystre, Jacquemier, Burri, Zsolnai, Frank, Vieira, Shi, Perin, Keller and Markram.Nissl histology underpins our understanding of brain anatomy and architecture. Despite its importance, no high-resolution datasets are currently available i ...

We investigate the magnetic structure and magnetoelectric(ME) effect in the high -field phase of the antiferromagnet LiFePO 4 above the critical field of 31 T. A neutron diffraction study in pulsed magnetic fields reveals the propagation vector to be q = 0 ...

Multiple drug concentrations concurrent detection and quantification based on electrochemical sensors are of great importance for therapeutic drug monitoring (TDM) and the development of personalized therapy. Cyclic voltammogram (CV) results obtained by el ...

This study aims to determine whether 1) individuals with treatment-resistant schizophrenia display early cognitive impairment compared to treatment-responders and healthy controls and 2) N-methyl-D-aspartate-receptor hypofunction is an underlying mechanism ...

The growing availability of distributed data has led to the increased use of machine learning (ML) algorithms in distributed topologies, where multiple nodes collaborate to train models under the coordination of a central server. However, distributed learn ...

Aim: Adolescents born very preterm (VPT;

The growing global obesity issue emphasizes the importance of understanding its health implications. Previous research has identified consistent alterations in gray matter (GM) volume in connection with obesity. Given the various implications of distinct f ...

Implantable neural interfaces with the central and peripheral nervous systems are currently used to restore sensory, motor, and cognitive functions in disabled people with very promising results. They have also been used to modulate autonomic activities to ...

The contextual stochastic block model (CSBM) was proposed for unsupervised community detection on attributed graphs where both the graph and the high-dimensional node information correlate with node labels. In the context of machine learning on graphs, the ...

Deep learning has achieved remarkable success in diverse fields in recent years. However, this growth presents significant challenges, particularly in terms of energy consumption during both training and inference phases. While there have been efforts to i ...

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict t ...

Solid-state photochemically induced dynamic nuclear polarization (photo-CIDNP) is a nuclear magnetic resonance spectroscopy technique in which nuclear spin hyperpolarization is generated upon optical irradiation of an appropriate donor-acceptor system. Unt ...

Novice students in law courses or students who encounter legal education face the challenge of acquiring specialized and highly concept-oriented knowledge. Structured and persuasive writing combined with the necessary domain knowledge is challenging for ma ...

Neurobiological and metabolic basis of motivation and effort-based decision-making - A human 1H-MRS, fMRI and machine learning study

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Parental status and markers of brain and cellular age: A 3D convolutional network and classification study

In-Silico Trained AI for Enhanced T2Spectrum Imaging and Myelin Water Fraction Mapping in Preclinical 7T MRI

High brightness neutron source optimized for very high pulsed magnetic field experiments

Analytical Model of Single-Sided Linear Induction Motors for High-Speed Applications

Ethological computational psychiatry: Challenges and opportunities

Efficient and insightful descriptors for representing molecular and material space

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

Cell density quantification of high resolution Nissl images of the juvenile rat brain

Magnetic structure and magnetoelectric properties of the spin-flop phase in LiFePO4

Automatic simulation of electrochemical sensors by machine learning for drugs quantification

Neurocognition and NMDAR co-agonists pathways in individuals with treatment resistant first-episode psychosis: a 3-year follow-up longitudinal study

Towards Practical Homomorphic Aggregation in Byzantine-Resilient Distributed Learning

Impact of a mindfulness-based intervention on neurobehavioral functioning and its association with large-scale brain networks in preterm young adolescents

The interplay between white adipose tissue, adipokines, and structural gray matter changes

Organ Neuroprosthetics: Connecting Transplanted and Artificial Organs with the Nervous System

Optimal Inference in Contextual Stochastic Block Models

Backpropagation-free Training of Analog AI Accelerators

Driving and suppressing the human language network using large language models

Magic Angle Spinning Solid-State 13C Photochemically Induced Dynamic Nuclear Polarization by a Synthetic Donor-Chromophore-Acceptor System at 9.4 T

LegalWriter: An Intelligent Writing Support System for Structured and Persuasive Legal Case Writing for Novice Law Students