AIMC Topic: Brain

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Digital Multiplierless Realization of Coupled Wilson Neuron Model.

IEEE transactions on biomedical circuits and systems
The human brain is composed of 10 neurons with a switching speed of about 1 ms. Studying spiking neural networks, including the modeling, simulation, and implementation of the biological neuron models, helps us to learn about the brain and the relate...

Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review.

Artificial intelligence in medicine
In recent years, deep convolutional neural networks (CNNs) have shown record-shattering performance in a variety of computer vision problems, such as visual object recognition, detection and segmentation. These methods have also been utilised in medi...

Regularized aggregation of statistical parametric maps.

Human brain mapping
Combining statistical parametric maps (SPM) from individual subjects is the goal in some types of group-level analyses of functional magnetic resonance imaging data. Brain maps are usually combined using a simple average across subjects, making them ...

Annual Research Review: Developmental computational psychiatry.

Journal of child psychology and psychiatry, and allied disciplines
Most psychiatric disorders emerge during childhood and adolescence. This is also a period that coincides with the brain undergoing substantial growth and reorganisation. However, it remains unclear how a heightened vulnerability to psychiatric disord...

Parkinson's Disease Diagnosis via Joint Learning From Multiple Modalities and Relations.

IEEE journal of biomedical and health informatics
Parkinson's disease (PD) is a neurodegenerative progressive disease that mainly affects the motor systems of patients. To slow this disease deterioration, early and accurate diagnosis of PD is an effective way, which alleviates mental and physical su...

A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation.

IEEE transactions on bio-medical engineering
Magnetic resonance imaging (MRI) is the non-invasive modality of choice for body tissue composition analysis due to its excellent soft-tissue contrast and lack of ionizing radiation. However, quantification of body composition requires an accurate se...

Segmenting the Brain Surface From CT Images With Artifacts Using Locally Oriented Appearance and Dictionary Learning.

IEEE transactions on medical imaging
The accurate segmentation of the brain surface in post-surgical computed tomography (CT) images is critical for image-guided neurosurgical procedures in epilepsy patients. Following surgical implantation of intracranial electrodes, surgeons require a...

Epilepsy classification using optimized artificial neural network.

Neurological research
OBJECTIVES: An Electroencephalogram (EEG) is the result of co-operative actions performed by brain cells. In other words, it can be defined as the time course of extracellular field potentials that are generated due to the synchronous action of cells...

Image categorization from functional magnetic resonance imaging using functional connectivity.

Journal of neuroscience methods
BACKGROUND: Previous studies have attempted to infer the category of objects in a stimulus image from functional magnetic resonance imaging (fMRI) data recoded during image-viewing. Most studies focus on extracting activity patterns within a given re...

Machine learning in major depression: From classification to treatment outcome prediction.

CNS neuroscience & therapeutics
AIMS: Major depression disorder (MDD) is the single greatest cause of disability and morbidity, and affects about 10% of the population worldwide. Currently, there are no clinically useful diagnostic biomarkers that are able to confirm a diagnosis of...