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Neuroimaging

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A deep learning-based multisite neuroimage harmonization framework established with a traveling-subject dataset.

NeuroImage
The accumulation of multisite large-sample MRI datasets collected during large brain research projects in the last decade has provided critical resources for understanding the neurobiological mechanisms underlying cognitive functions and brain disord...

Predicting brain structural network using functional connectivity.

Medical image analysis
Uncovering the non-trivial brain structure-function relationship is fundamentally important for revealing organizational principles of human brain. However, it is challenging to infer a reliable relationship between individual brain structure and fun...

Deep neural networks learn general and clinically relevant representations of the ageing brain.

NeuroImage
The discrepancy between chronological age and the apparent age of the brain based on neuroimaging data - the brain age delta - has emerged as a reliable marker of brain health. With an increasing wealth of data, approaches to tackle heterogeneity in ...

ClinicaDL: An open-source deep learning software for reproducible neuroimaging processing.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: As deep learning faces a reproducibility crisis and studies on deep learning applied to neuroimaging are contaminated by methodological flaws, there is an urgent need to provide a safe environment for deep learning users to ...

Implementing Critical Machine Learning (ML) Approaches for Generating Robust Discriminative Neuroimaging Representations Using Structural Equation Model (SEM).

Computational and mathematical methods in medicine
Critical ML or CML is a critical approach development of the standard ML (SML) procedure. Conventional ML (ML) is being used in radiology departments where complex neuroimages are discriminated using ML technology. Radiologists and researchers found ...

A Single Model Deep Learning Approach for Alzheimer's Disease Diagnosis.

Neuroscience
Early and accurate diagnosis of Alzheimer's disease (AD) and its prodromal period mild cognitive impairment (MCI) is essential for the delayed disease progression and the improved quality of patients' life. The emerging computer-aided diagnostic meth...

A deep learning MRI approach outperforms other biomarkers of prodromal Alzheimer's disease.

Alzheimer's research & therapy
BACKGROUND: The three core pathologies of Alzheimer's disease (AD) are amyloid pathology, tau pathology, and neurodegeneration. Biomarkers exist for each. Neurodegeneration is often detected by neuroimaging, and we hypothesized that a voxel-based dee...

How Machine Learning is Powering Neuroimaging to Improve Brain Health.

Neuroinformatics
This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we areshar...

Deep-learning two-photon fiberscopy for video-rate brain imaging in freely-behaving mice.

Nature communications
Scanning two-photon (2P) fiberscopes (also termed endomicroscopes) have the potential to transform our understanding of how discrete neural activity patterns result in distinct behaviors, as they are capable of high resolution, sub cellular imaging y...

Charting the potential of brain computed tomography deep learning systems.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Brain computed tomography (CTB) scans are widely used to evaluate intracranial pathology. The implementation and adoption of CTB has led to clinical improvements. However, interpretation errors occur and may have substantial morbidity and mortality i...