Alzheimer's Disease

An ensemble learning system for a 4-way classification of Alzheimer's disease and mild cognitive impairment.

Discriminating Alzheimer's disease (AD) from its prodromal form, mild cognitive impairment (MCI), is a significant clinical problem that may facilitate early diagnosis and intervention, in which a more challenging issue is to classify MCI subtypes, i.e., those who eventually convert to AD (cMCI) versus those who do not (MCI). To solve this difficult 4-way classification problem (AD, MCI, cMCI and ...

Electroencephalographic derived network differences in Lewy body dementia compared to Alzheimer's disease patients.

Dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) require differential management despite presenting with symptomatic overlap. Currently, there is a need of inexpensive DLB biomarkers which can be fulfilled by electroencephalography (EEG). In this regard, an established electrophysiological difference in DLB is a decrease of dominant frequency (DF)-the frequency with the highest signal ...

Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer's Disease Spectrum: Development of the Classifier and Longitudinal Evaluation.

To develop a new method for measuring Alzheimer's disease (AD)-specific similarity of cortical atrophy patterns at the individual-level, we employed a...

Predication of different stages of Alzheimer's disease using neighborhood component analysis and ensemble decision tree.

BACKGROUND: There is a spectrum of the progression from healthy control (HC) to mild cognitive impairment (MCI) without conversion to Alzheimer's dise...

Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer's disease.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases with a commonly seen prodromal mild cognitive impairment (MCI) phase whe...

Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination.

BACKGROUND: The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning...

Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code.

This article provides data for five different neuropsychiatric disorders-Attention Deficit Hyperactivity Disorder, Alzheimer's Disease, Autism Spectru...

In Schizophrenia, Depression, Anxiety, and Physiosomatic Symptoms Are Strongly Related to Psychotic Symptoms and Excitation, Impairments in Episodic Memory, and Increased Production of Neurotoxic Tryptophan Catabolites: a Multivariate and Machine Learning Study.

The depression, anxiety and physiosomatic symptoms (DAPS) of schizophrenia are associated with negative symptoms and changes in tryptophan catabolite ...

Higher Serum Endocan Level Is Associated with Alzheimer Disease.

BACKGROUND: The novel molecule endocan, which is released by endothelium and is regulated by proangiogenic and proinflammatory cytokines, may have a r...

Semi-Supervised Discriminative Classification Robust to Sample-Outliers and Feature-Noises.

Discriminative methods commonly produce models with relatively good generalization abilities. However, this advantage is challenged in real-world appl...

On the Generalization Ability of Online Gradient Descent Algorithm Under the Quadratic Growth Condition.

Online learning has been successfully applied in various machine learning problems. Conventional analysis of online learning achieves a sharp generali...

Effect of a robotic seal on the motor activity and sleep patterns of older people with dementia, as measured by wearable technology: A cluster-randomised controlled trial.

OBJECTIVES: The robotic seal, PARO, has been used as an alternative to animal-assisted therapies with residents with dementia in long-term care, yet u...

Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer's disease.

Alzheimer's disease (AD) is a major neurodegenerative disease and the most common cause of dementia. Currently, no treatment exists to slow down or st...

Revealing Alzheimer's disease genes spectrum in the whole-genome by machine learning.

BACKGROUND: Alzheimer's disease (AD) is an important, progressive neurodegenerative disease, with a complex genetic architecture. A key goal of biomed...

The Cost-Effectiveness of Using PARO, a Therapeutic Robotic Seal, to Reduce Agitation and Medication Use in Dementia: Findings from a Cluster-Randomized Controlled Trial.

OBJECTIVES: To examine the within-trial costs and cost-effectiveness of using PARO, compared with a plush toy and usual care, for reducing agitation a...

A wrapped multi-label classifier for the automatic diagnosis and prognosis of Alzheimer's disease.

BACKGROUND: AD is the most frequent neurodegenerative disease, severely impacting our society. Early diagnosis and prognosis are challenging tasks in ...

Alzheimer's disease diagnostics by a 3D deeply supervised adaptable convolutional network.

Early diagnosis is playing an important role in preventing progress of the Alzheimer's disease (AD). This paper proposes to improve the prediction of ...

Using a therapeutic companion robot for dementia symptoms in long-term care: reflections from a cluster-RCT.

OBJECTIVES: We undertook a cluster-randomised controlled trial exploring the effect of a therapeutic companion robot (PARO) compared to a look-alike p...

Deep learning reveals Alzheimer's disease onset in MCI subjects: Results from an international challenge.

BACKGROUND: Early diagnosis of Alzheimer's disease (AD) and its onset in subjects affected by mild cognitive impairment (MCI) based on structural MRI ...

Robots and people with dementia: Unintended consequences and moral hazard.

The use of social robots in elder care is entering the mainstream as robots become more sophisticated and populations age. While there are many potent...