AI Medical Compendium Journal:
Neurobiology of aging

Showing 1 to 10 of 10 articles

Brain age of rhesus macaques over the lifespan.

Neurobiology of aging
Through the application of machine learning algorithms to neuroimaging data the brain age methodology was shown to provide a useful individual-level biological age prediction and identify key brain regions responsible for the prediction. In this stud...

A machine learning approach to screen for preclinical Alzheimer's disease.

Neurobiology of aging
Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We included 304 cognitively normal individuals from the INSIGHT-preAD cohort. Amyloid and neurodegeneration were assessed on F-florbetapir and F-fluorodeox...

Prediction of brain age from routine T2-weighted spin-echo brain magnetic resonance images with a deep convolutional neural network.

Neurobiology of aging
Our study investigated the feasibility and clinical relevance of brain age prediction using axial T2-weighted images (T2-WIs) with a deep convolutional neural network (CNN) algorithm. The CNN model was trained by 1,530 scans in our institution. The p...

Improving brain age estimates with deep learning leads to identification of novel genetic factors associated with brain aging.

Neurobiology of aging
To study genetic factors associated with brain aging, we first need to quantify brain aging. Statistical models have been created for estimating the apparent age of the brain, or predicted brain age (PBA), using imaging data. Recent studies have refi...

Transfer learning for predicting conversion from mild cognitive impairment to dementia of Alzheimer's type based on a three-dimensional convolutional neural network.

Neurobiology of aging
Dementia of Alzheimer's type (DAT) is associated with devastating and irreversible cognitive decline. Predicting which patients with mild cognitive impairment (MCI) will progress to DAT is an ongoing challenge in the field. We developed a deep learni...

Estimating brain age based on a uniform healthy population with deep learning and structural magnetic resonance imaging.

Neurobiology of aging
Numerous studies have established that estimated brain age constitutes a valuable biomarker that is predictive of cognitive decline and various neurological diseases. In this work, we curate a large-scale brain MRI data set of healthy individuals, on...

Identifying incipient dementia individuals using machine learning and amyloid imaging.

Neurobiology of aging
Identifying individuals destined to develop Alzheimer's dementia within time frames acceptable for clinical trials constitutes an important challenge to design studies to test emerging disease-modifying therapies. Although amyloid-β protein is the co...

Longitudinal clinical score prediction in Alzheimer's disease with soft-split sparse regression based random forest.

Neurobiology of aging
Alzheimer's disease (AD) is an irreversible neurodegenerative disease and affects a large population in the world. Cognitive scores at multiple time points can be reliably used to evaluate the progression of the disease clinically. In recent studies,...

Cognitively impaired elderly exhibit insulin resistance and no memory improvement with infused insulin.

Neurobiology of aging
Insulin resistance is a risk factor for Alzheimer's disease (AD), although its role in AD etiology is unclear. We assessed insulin resistance using fasting and insulin-stimulated measures in 51 elderly subjects with no dementia (ND; n = 37) and with ...

CSF YKL-40 and pTau181 are related to different cerebral morphometric patterns in early AD.

Neurobiology of aging
Cerebrospinal fluid (CSF) concentrations of YKL-40 that serve as biomarker of neuroinflammation are known to be altered along the clinico-biological continuum of Alzheimer's disease (AD). The specific structural cerebral correlates of CSF YKL-40 were...