AIMC Topic: Neuroimaging

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Toward Robust Anxiety Biomarkers: A Machine Learning Approach in a Large-Scale Sample.

Biological psychiatry. Cognitive neuroscience and neuroimaging
BACKGROUND: The field of psychiatry has long sought biomarkers that can objectively diagnose patients, predict treatment response, or identify individuals at risk of illness onset. However, reliable psychiatric biomarkers have yet to emerge. The rece...

Automatic identification of atherosclerosis subjects in a heterogeneous MR brain imaging data set.

Magnetic resonance imaging
Carotid-artery atherosclerosis (CA) contributes significantly to overall morbidity and mortality in ischemic stroke. We propose a machine learning technique to automatically identify subjects with CA from a heterogeneous cohort of magnetic resonance ...

Improving human cortical sulcal curve labeling in large scale cross-sectional MRI using deep neural networks.

Journal of neuroscience methods
BACKGROUND: Human cortical primary sulci are relatively stable landmarks and commonly observed across the population. Despite their stability, the primary sulci exhibit phenotypic variability.

A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: It is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease (AD) dementia.

Quantifying performance of machine learning methods for neuroimaging data.

NeuroImage
Machine learning is increasingly being applied to neuroimaging data. However, most machine learning algorithms have not been designed to accommodate neuroimaging data, which typically has many more data points than subjects, in addition to multicolli...

Modeling longitudinal imaging biomarkers with parametric Bayesian multi-task learning.

Human brain mapping
Longitudinal imaging biomarkers are invaluable for understanding the course of neurodegeneration, promising the ability to track disease progression and to detect disease earlier than cross-sectional biomarkers. To properly realize their potential, b...

Ensemble of Convolutional Neural Networks Improves Automated Segmentation of Acute Ischemic Lesions Using Multiparametric Diffusion-Weighted MRI.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Accurate automated infarct segmentation is needed for acute ischemic stroke studies relying on infarct volumes as an imaging phenotype or biomarker that require large numbers of subjects. This study investigated whether an ens...

Deep Learning-Based Automatic Segmentation of Lumbosacral Nerves on CT for Spinal Intervention: A Translational Study.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: 3D reconstruction of a targeted area ("safe" triangle and Kambin triangle) may benefit the viability assessment of transforaminal epidural steroid injection, especially at the L5/S1 level. However, manual segmentation of lumbo...

Computational approaches and machine learning for individual-level treatment predictions.

Psychopharmacology
RATIONALE: The impact of neuroscience-based approaches for psychiatry on pragmatic clinical decision-making has been limited. Although neuroscience has provided insights into basic mechanisms of neural function, these insights have not improved the a...

A hybrid Convolutional and Recurrent Neural Network for Hippocampus Analysis in Alzheimer's Disease.

Journal of neuroscience methods
BACKGROUND: Hippocampus is one of the first structures affected by neurodegenerative diseases such as Alzheimer's disease (AD) and mild cognitive impairment (MCI). Hippocampal atrophy can be evaluated in terms of hippocampal volumes and shapes using ...