Neurology

Leading a Digital Transformation in the Pharmaceutical Industry: Reimagining the Way We Work in Global Drug Development.

We are experiencing seminal times in computing that seem to define a fourth industrial revolution. This may fundamentally change the way we live, work, and relate to one another. Embracing data and digital information is a top priority for most industries these days, and Life Sciences is no exception. The pharmaceutical industry in particular is fundamentally a data-driven business. Inspired by a ...

Interpretable and lightweight convolutional neural network for EEG decoding: Application to movement execution and imagination.

Convolutional neural networks (CNNs) are emerging as powerful tools for EEG decoding: these techniques, by automatically learning relevant features for class discrimination, improve EEG decoding performances without relying on handcrafted features. Nevertheless, the learned features are difficult to interpret and most of the existing CNNs introduce many trainable parameters. Here, we propose a lig...

Automated computer-assisted detection system for cerebral aneurysms in time-of-flight magnetic resonance angiography using fully convolutional network.

BACKGROUND: As the rupture of cerebral aneurysm may lead to fatal results, early detection of unruptured aneurysms may save lives. At present, the con...

Classifying creativity: Applying machine learning techniques to divergent thinking EEG data.

Prior research has shown that greater EEG alpha power (8-13 ​Hz) is characteristic of more creative individuals, and more creative task conditions. Th...

A Real-Time Depth of Anesthesia Monitoring System Based on Deep Neural Network With Large EDO Tolerant EEG Analog Front-End.

In this article, we present a real-time electroencephalogram (EEG) based depth of anesthesia (DoA) monitoring system in conjunction with a deep learni...

EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia.

The Temporal Sampling Framework (TSF) theorizes that the characteristic phonological difficulties of dyslexia are caused by an atypical oscillatory sa...

Robot technology identifies a Parkinsonian therapeutics repurpose to target stem cells of glioblastoma.

Glioblastoma is a heterogeneous lethal disease, regulated by a stem-cell hierarchy and the neurotransmitter microenvironment. The identification of c...

Pilot study: can machine learning analyses of movement discriminate between leg movements in sleep (LMS) with vs. without cortical arousals?

PURPOSE: Clinical and animal studies indicate frequent small micro-arousals (McA) fragment sleep leading to health complications. McA in humans is def...

Machine Learning and Multiparametric Brain MRI to Differentiate Hereditary Diffuse Leukodystrophy with Spheroids from Multiple Sclerosis.

BACKGROUND AND PURPOSE: Hereditary diffuse leukoencephalopathy with spheroids (HDLS) and multiple sclerosis (MS) are demyelinating and neurodegenerati...

Real-time Inference and Detection of Disruptive EEG Networks for Epileptic Seizures.

Recent studies in brain science and neurological medicine paid a particular attention to develop machine learning-based techniques for the detection a...

Automatic post-stroke lesion segmentation on MR images using 3D residual convolutional neural network.

In this paper, we demonstrate the feasibility and performance of deep residual neural networks for volumetric segmentation of irreversibly damaged bra...

A data-driven approach for detecting gait events during turning in people with Parkinson's disease and freezing of gait.

BACKGROUND: Manual annotation of initial contact (IC) and end contact (EC) is a time consuming process. There are currently no robust techniques avail...

Evidence that the Kennedy and polyamine pathways are dysregulated in human brain in cases of dementia with Lewy bodies.

Disruptions of brain metabolism are considered integral to the pathogenesis of dementia, but thus far little is known of how dementia with Lewy bodies...

The fourier M2 robotic machine combined with occupational therapy on post-stroke upper limb function and independence-related quality of life: A randomized clinical trial.

Most post-stroke patients experience upper limb functionality challenges. Emergent therapies using upper limb-based robot machines present opportuniti...

Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies.

PET attenuation correction (AC) on systems lacking CT/transmission scanning, such as dedicated brain PET scanners and hybrid PET/MRI, is challenging. ...

Detection of Depression and Scaling of Severity Using Six Channel EEG Data.

Depression is a psychiatric problem which affects the growth of a person, like how a person thinks, feels and behaves. The major reason behind wrong d...

Machine learning provides evidence that stroke risk is not linear: The non-linear Framingham stroke risk score.

Current stroke risk assessment tools presume the impact of risk factors is linear and cumulative. However, both novel risk factors and their interplay...

Psychosocial profiles and their predictors in epilepsy using patient-reported outcomes and machine learning.

OBJECTIVE: To apply unsupervised machine learning to patient-reported outcomes to identify clusters of epilepsy patients exhibiting unique psychosocia...

Development of a Novel Task-oriented Rehabilitation Program using a Bimanual Exoskeleton Robotic Hand.

A robot-assisted hand is used for the rehabilitation of patients with impaired upper limb function, particularly for stroke patients with a loss of mo...

An improved common spatial pattern combined with channel-selection strategy for electroencephalography-based emotion recognition.

Emotional human-computer interaction (HCI) has become an important research area in the fields of artificial intelligence and cognitive science, owing...