AIMC Topic: Brain

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Looking for Alzheimer's Disease morphometric signatures using machine learning techniques.

Journal of neuroscience methods
BACKGROUND: We present our results in the International challenge for automated prediction of MCI from MRI data. We evaluate the performance of MRI-based neuromorphometrics features (nMF) in the classification of Healthy Controls (HC), Mild Cognitive...

Residual Convolutional Neural Network for the Determination of Status in Low- and High-Grade Gliomas from MR Imaging.

Clinical cancer research : an official journal of the American Association for Cancer Research
Isocitrate dehydrogenase () mutations in glioma patients confer longer survival and may guide treatment decision making. We aimed to predict the status of gliomas from MR imaging by applying a residual convolutional neural network to preoperative r...

An EEG-based functional connectivity measure for automatic detection of alcohol use disorder.

Artificial intelligence in medicine
BACKGROUND: The abnormal alcohol consumption could cause toxicity and could alter the human brain's structure and function, termed as alcohol used disorder (AUD). Unfortunately, the conventional screening methods for AUD patients are subjective and m...

Alcoholism Detection by Data Augmentation and Convolutional Neural Network with Stochastic Pooling.

Journal of medical systems
Alcohol use disorder (AUD) is an important brain disease. It alters the brain structure. Recently, scholars tend to use computer vision based techniques to detect AUD. We collected 235 subjects, 114 alcoholic and 121 non-alcoholic. Among the 235 imag...

An introduction and overview of machine learning in neurosurgical care.

Acta neurochirurgica
BACKGROUND: Machine learning (ML) is a branch of artificial intelligence that allows computers to learn from large complex datasets without being explicitly programmed. Although ML is already widely manifest in our daily lives in various forms, the c...

Embodiment and Estrangement: Results from a First-in-Human "Intelligent BCI" Trial.

Science and engineering ethics
While new generations of implantable brain computer interface (BCI) devices are being developed, evidence in the literature about their impact on the patient experience is lagging. In this article, we address this knowledge gap by analysing data from...

Beyond modularity: Fine-scale mechanisms and rules for brain network reconfiguration.

NeuroImage
The human brain is in constant flux, as distinct areas engage in transient communication to support basic behaviors as well as complex cognition. The collection of interactions between cortical and subcortical areas forms a functional brain network w...

Code-modulated visual evoked potentials using fast stimulus presentation and spatiotemporal beamformer decoding.

Scientific reports
When encoding visual targets using various lagged versions of a pseudorandom binary sequence of luminance changes, the EEG signal recorded over the viewer's occipital pole exhibits so-called code-modulated visual evoked potentials (cVEPs), the phase ...

Convolutional Neural Networks with 3D Input for P300 Identification in Auditory Brain-Computer Interfaces.

Computational intelligence and neuroscience
From allowing basic communication to move through an environment, several attempts are being made in the field of brain-computer interfaces (BCI) to assist people that somehow find it difficult or impossible to perform certain activities. Focusing on...

Ensemble based on static classifier selection for automated diagnosis of Mild Cognitive Impairment.

Journal of neuroscience methods
BACKGROUND: Alzheimer's disease (AD) is the most common cause of neurodegenerative dementia in the elderly population. Scientific research is very active in the challenge of designing automated approaches to achieve an early and certain diagnosis. Re...