AIMC Topic: Neuroimaging

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The Decision Decoding ToolBOX (DDTBOX) - A Multivariate Pattern Analysis Toolbox for Event-Related Potentials.

Neuroinformatics
In recent years, neuroimaging research in cognitive neuroscience has increasingly used multivariate pattern analysis (MVPA) to investigate higher cognitive functions. Here we present DDTBOX, an open-source MVPA toolbox for electroencephalography (EEG...

Machine learning applications for the differentiation of primary central nervous system lymphoma from glioblastoma on imaging: a systematic review and meta-analysis.

Neurosurgical focus
OBJECTIVEGlioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are common intracranial pathologies encountered by neurosurgeons. They often may have similar radiological findings, making diagnosis difficult without surgical biopsy; h...

Multisite Machine Learning Analysis Provides a Robust Structural Imaging Signature of Schizophrenia Detectable Across Diverse Patient Populations and Within Individuals.

Schizophrenia bulletin
Past work on relatively small, single-site studies using regional volumetry, and more recently machine learning methods, has shown that widespread structural brain abnormalities are prominent in schizophrenia. However, to be clinically useful, struct...

The Changing Face of Technologically Integrated Neurosurgery: Today's High-Tech Operating Room.

World neurosurgery
Over the last decade, surgical technology in planning, mapping, optics, robotics, devices, and minimally invasive techniques has changed the face of modern neurosurgery. We explore the current advances in clinical technology across all neurosurgical ...

A Pathological Brain Detection System based on Extreme Learning Machine Optimized by Bat Algorithm.

CNS & neurological disorders drug targets
AIM: It is beneficial to classify brain images as healthy or pathological automatically, because 3D brain images can generate so much information which is time consuming and tedious for manual analysis. Among various 3D brain imaging techniques, magn...

Learning-based 3T brain MRI segmentation with guidance from 7T MRI labeling.

Medical physics
PURPOSE: Segmentation of brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is crucial for brain structural measurement and disease diagnosis. Learning-based segmentation methods depend largel...

Label-aligned multi-task feature learning for multimodal classification of Alzheimer's disease and mild cognitive impairment.

Brain imaging and behavior
Multimodal classification methods using different modalities of imaging and non-imaging data have recently shown great advantages over traditional single-modality-based ones for diagnosis and prognosis of Alzheimer's disease (AD), as well as its prod...

Learning-based subject-specific estimation of dynamic maps of cortical morphology at missing time points in longitudinal infant studies.

Human brain mapping
Longitudinal neuroimaging analysis of the dynamic brain development in infants has received increasing attention recently. Many studies expect a complete longitudinal dataset in order to accurately chart the brain developmental trajectories. However,...

Addressing Confounding in Predictive Models with an Application to Neuroimaging.

The international journal of biostatistics
Understanding structural changes in the brain that are caused by a particular disease is a major goal of neuroimaging research. Multivariate pattern analysis (MVPA) comprises a collection of tools that can be used to understand complex disease efxcfe...