Peri-personal space encoding in patients with disorders of consciousness and cognitive-motor dissociation

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Subcortical brain structures such as the basal ganglia or the thalamus are involved in regulating motor and cognitive behavior. However, their contribution to perceptual consciousness is still unclear, due to the inherent difficulties of recording subcorti ...

Background Disrupted sense of agency (SoA)-the sense of being the agent of one's own actions-has been demonstrated in patients with functional neurological disorder (FND), and a key area of the corresponding neuronal network is the right temporoparietal ju ...

Neuroprostheses have been used clinically for decades, to help restore or preserve brain functions, when pharmaceutical treatments are inefficient. Although great progress in the field has been made over the years to interface with the nervous system, surf ...

Traditionally, studies in schizophrenia research employ a single experimental paradigm. The results typically demonstrate a significant difference between patients and controls. Subsequent studies aim to describe the underlying abnormal mechanism at the ge ...

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

Reinforcement learning (RL) is crucial for learning to adapt to new environments. In RL, the prediction error is an important component that compares the expected and actual rewards. Dopamine plays a critical role in encoding these prediction errors. In my ...

The temporal variability of the thalamus in functional networks may provide valuable insights into the pathophysiology of schizophrenia. To address the complexity of the role of the thalamic nuclei in psychosis, we introduced micro-co-activation patterns ( ...

In humans and animals, surprise is a physiological reaction to an unexpected event, but how surprise can be linked to plausible models of neuronal activity is an open problem. We propose a self-supervised spiking neural network model where a surprise signa ...

The rapid development of wearable biomedical systems now enables real-time monitoring of electroencephalography (EEG) signals. Acquisition of these signals relies on electrodes. These systems must meet the design challenge of selecting an optimal set of el ...

In this research paper, we conducted a study to investigate the connection between three objective measures: Electrocardiogram(ECG), Electrogastrogram (EGG), and Electroencephalogram (EEG), and individuals' susceptibility to cybersickness. Our primary obje ...

Recently, cutting-edge brain-machine interfaces (BMIs) have revealed the potential of decoders such as recurrent neural networks (RNNs) in predicting attempted handwriting [1] or speech [2], enabling rapid communication recovery after paralysis. However, c ...

The rise of robotic body augmentation brings forth new developments that will transform robotics, human-machine interaction, and wearable electronics. Extra robotic limbs, although building upon restorative technologies, bring their own set of challenges i ...

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of the ...

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

Electrical stimulation of the visual nervous system could improve the quality of life of patients affected by acquired blindness by restoring some visual sensations, but requires careful optimization of stimulation parameters to produce useful perceptions. ...

The growing popularity of virtual reality systems has led to a renewed interest in understanding the neurophysiological correlates of the illusion of self-motion (vection), a phenomenon that can be both intentionally induced or avoided in such systems, dep ...

To fully comprehend visual perception, we need to necessarily understand its temporal dimension. Our visual environment is highly dynamic, requiring the processing and integration of temporal signals in order to make sense of it. Many processes, such as th ...

The recollection of sensory information and subjective experience related to a personal past event depends on our episodic memory (EM). At the neural level, EM retrieval is linked with the reinstatement of hippocampal activity thought to recollect the sens ...

Peri-personal space encoding in patients with disorders of consciousness and cognitive-motor dissociation

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Subcortical correlates of consciousness with human single neuron recordings

Non-invasive neuromodulation of the right temporoparietal junction using theta-burst stimulation in functional neurological disorder

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Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

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Thalamic contributions to psychosis susceptibility: Evidence from co-activation patterns accounting for intra-seed spatial variability (μCAPs)

Fast adaptation to rule switching using neuronal surprise

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Investigating neural resource allocation in the sensorimotor control of extra limbs

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Predicting the long-term collective behaviour of fish pairs with deep learning

Enabling Uncertainty Estimation in Iterative Neural Networks

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Decoding electroencephalographic responses to visual stimuli compatible with electrical stimulation

Modulation of Visually Induced Self-motion Illusions by α Transcranial Electric Stimulation over the Superior Parietal Cortex

Temporal aspects of feature integration in vision

Bodily self-consciousness as a framework to link sensory information and self-related components of episodic memory: behavioral, neuroimaging, and clinical evidence