Predicting Modafinil-Treatment Response in Poststroke Fatigue Using Brain Morphometry and Functional Connectivity

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The reported rate of the occurrence of unilateral spatial neglect (USN) is highly variable likely due to the lack of validity and low sensitivity of classical tools used to assess it. Virtual reality (VR) assessments try to overcome these limitations by pr ...

Alterations of the limbic system may be present in the chronic phase of SARS-CoV-2 infection. Our aim was to study the long-term impact of this disease on limbic system-related behaviour and its associated brain functional connectivity, according to the se ...

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The outstanding information processing capacity of the brain relies on numerous molecular mechanisms. It is an extremely energy-expensive function, which involves specialization and collaboration of different cell types. A common framework to study the com ...

Background: A neurocognitive phenotype of post-COVID-19 infection has recently been described that is characterized by a lack of awareness of memory impairment (i.e., anosognosia), altered functional connectivity in the brain's default mode and limbic netw ...

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Exosuits typically provide limited mechanical support and rely on a user's residual functional ability. However, people with neurological impairments often suffer from both motor and sensory deficits that limit the assistance an exosuit can provide. To ove ...

We present a framework for performing regression when both covariate and response are probability distributions on a compact and convex subset of

\R^d

. Our regression model is based on the theory of optimal transport and links the conditional Fr'echet m ...

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 ...

Objective: To characterize ambulatory knee moments with respect to medial knee osteoarthritis (OA) severity comprehensively and to assess the possibility of developing a severity index combining knee moment parameters. ...

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

Rationale and objectives: To prospectively evaluate feasibility and robustness of an accelerated T2 mapping sequence (GRAPPATINI) in brain imaging and to assess its synthetic T2-weighted images (sT2w) in comparison with a standard T2-weighted sequence (T2 ...

Predicting Modafinil-Treatment Response in Poststroke Fatigue Using Brain Morphometry and Functional Connectivity

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An immersive virtual reality tool for assessing left and right unilateral spatial neglect

Markers of limbic system damage following SARS-CoV-2 infection

Harmonization of multi-scanner in vivo magnetic resonance spectroscopy: ENIGMA consortium task group considerations

Molecular reconstruction and simulation of the Neuron-Glia-Vasculature system

Acute TNF alpha levels predict cognitive impairment 6-9 months after COVID-19 infection

Semi-Supervised Deep Learning Representations in Earth Observation Based Forest Management

Sensory-Motor Neurostimulation to Enhance Exosuit Performance

Distributional Regression and Autoregression via Optimal Transport

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

Diverse parameters of ambulatory knee moments differ with medial knee osteoarthritis severity and are combinable into a severity index

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Clinical feasibility and validation of the accelerated T2 mapping sequence GRAPPATINI in brain imaging