AIMC Topic: Neuroimaging

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Fused Group Lasso Regularized Multi-Task Feature Learning and Its Application to the Cognitive Performance Prediction of Alzheimer's Disease.

Neuroinformatics
Alzheimer's disease (AD) is characterized by gradual neurodegeneration and loss of brain function, especially for memory during early stages. Regression analysis has been widely applied to AD research to relate clinical and biomarker data such as pre...

Machine learning-based prediction of clinical pain using multimodal neuroimaging and autonomic metrics.

Pain
Although self-report pain ratings are the gold standard in clinical pain assessment, they are inherently subjective in nature and significantly influenced by multidimensional contextual variables. Although objective biomarkers for pain could substant...

Microelectrode Recordings Validate the Clinical Visualization of Subthalamic-Nucleus Based on 7T Magnetic Resonance Imaging and Machine Learning for Deep Brain Stimulation Surgery.

Neurosurgery
BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a proven and effective therapy for the management of the motor symptoms of Parkinson's disease (PD). While accurate positioning of the stimulating electrode is critical for ...

Image Based Brain Segmentation: From Multi-Atlas Fusion to Deep Learning.

Current medical imaging reviews
BACKGROUND: This review aims to identify the development of the algorithms for brain tissue and structure segmentation in MRI images.

Deep Learning for Alzheimer's Disease Classification using Texture Features.

Current medical imaging reviews
BACKGROUND: We propose a classification method for Alzheimer's disease (AD) based on the texture of the hippocampus, which is the organ that is most affected by the onset of AD.

Understanding Mood Disorders in Children.

Advances in experimental medicine and biology
Mood disorders include all types of depression and bipolar disorder, and mood disorders are sometimes called affective disorders. We will discuss newly developing two issues in affective disorders in children and adolescents. Those are the new diagno...

Machine Learning in Neural Networks.

Advances in experimental medicine and biology
Evidence now suggests that precision psychiatry is becoming a cornerstone of medical practices by providing the patient of psychiatric disorders with the right medication at the right dose at the right time. In light of recent advances in neuroimagin...

Volumetric Histogram-Based Alzheimer's Disease Detection Using Support Vector Machine.

Journal of Alzheimer's disease : JAD
In this research work, machine learning techniques are used to classify magnetic resonance imaging brain scans of people with Alzheimer's disease. This work deals with binary classification between Alzheimer's disease and cognitively normal. Supervis...

[Artificial Intelligence in Psychiatry].

Brain and nerve = Shinkei kenkyu no shinpo
Diagnosis of psychiatric disorders is based primarily on subjective symptoms, and neuroimaging or other biological examinations are used for excluding organic disorders. Advances in artificial intelligence technologies, such as machine learning, may ...

Using Deep Learning Algorithms to Automatically Identify the Brain MRI Contrast: Implications for Managing Large Databases.

Neuroinformatics
Neuroimaging science has seen a recent explosion in dataset size driving the need to develop database management with efficient processing pipelines. Multi-center neuroimaging databases consistently receive magnetic resonance imaging (MRI) data with ...