AI Medical Compendium Journal:
Frontiers in neuroscience

Showing 21 to 30 of 35 articles

Motion Biomarkers Showing Maximum Contrast Between Healthy Subjects and Parkinson's Disease Patients Treated With Deep Brain Stimulation of the Subthalamic Nucleus. A Pilot Study.

Frontiers in neuroscience
Classic motion abnormalities in Parkinson's disease (PD), such as tremor, bradykinesia, or rigidity, are well-covered by standard clinical assessments such as the Unified Parkinson's Disease Rating Scale (UPDRS). However, PD includes motor abnormali...

A Sparse EEG-Informed fMRI Model for Hybrid EEG-fMRI Neurofeedback Prediction.

Frontiers in neuroscience
Measures of brain activity through functional magnetic resonance imaging (fMRI) or electroencephalography (EEG), two complementary modalities, are ground solutions in the context of neurofeedback (NF) mechanisms for brain rehabilitation protocols. Wh...

Memristor-Based Edge Detection for Spike Encoded Pixels.

Frontiers in neuroscience
Memristors have many uses in machine learning and neuromorphic hardware. From memory elements in dot product engines to replicating both synapse and neuron wall behaviors, the memristor has proved a versatile component. Here we demonstrate an analog ...

A Digital Hardware System for Spiking Network of Tactile Afferents.

Frontiers in neuroscience
In the present research, we explore the possibility of utilizing a hardware-based neuromorphic approach to develop a tactile sensory system at the level of first-order afferents, which are slowly adapting type 1 (SA-I) and fast adapting type 1 (FA-I)...

Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations.

Frontiers in neuroscience
Gliomas are the most common primary malignant brain tumors in adults. Accurate grading is crucial as therapeutic strategies are often disparate for different grades and may influence patient prognosis. This study aims to provide an automated glioma g...

Clinical Value of Machine Learning in the Automated Detection of Focal Cortical Dysplasia Using Quantitative Multimodal Surface-Based Features.

Frontiers in neuroscience
To automatically detect focal cortical dysplasia (FCD) lesion by combining quantitative multimodal surface-based features with machine learning and to assess its clinical value. Neuroimaging data and clinical information for 74 participants (40 wit...

Topological Properties of Resting-State fMRI Functional Networks Improve Machine Learning-Based Autism Classification.

Frontiers in neuroscience
Automatic algorithms for disease diagnosis are being thoroughly researched for use in clinical settings. They usually rely on pre-identified biomarkers to highlight the existence of certain problems. However, finding such biomarkers for neurodevelopm...

Longitudinal Connectomes as a Candidate Progression Marker for Prodromal Parkinson's Disease.

Frontiers in neuroscience
Parkinson's disease is the second most prevalent neurodegenerative disorder in the Western world. It is estimated that the neuronal loss related to Parkinson's disease precedes the clinical diagnosis by more than 10 years (prodromal phase) which lead...

Deep Learning Based Attenuation Correction of PET/MRI in Pediatric Brain Tumor Patients: Evaluation in a Clinical Setting.

Frontiers in neuroscience
Positron emission tomography (PET) imaging is a useful tool for assisting in correct differentiation of tumor progression from reactive changes. O-(2-18F-fluoroethyl)-L-tyrosine (FET)-PET in combination with MRI can add valuable information for clin...