Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification

Early Detection of Dementia using Risk Classification in MCI: Outcomes of Shanghai Mild Cognitive Impairment Cohort Study

INTRODUCTION

The purpose of this study is to identify the risk factors and risk classification associated with the conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) dementia to facilitate early intervention and the design of clinical trials for AD.

METHODS

The study comprised a prospective cohort study of 400 subjects with MCI who had annual follow-ups for 3 years.

RESULTS

During an average follow-up period of 3.5 years, 109 subjects were diagnosed with all cause of dementia, of whom 104 subjects converted to Alzheimer's dementia and 5 subjects converted to other types of dementia. The cumulative conversion rate was 5.5% (95% CI: 3.4, 8.6), 16.3% (95% CI: 12.9, 21.1), and 31.0% (95% CI: 25.4, 36.5) in each of the first 3 follow-up years, respectively. The factors associated with a greater risk of conversion from MCI to AD included smoking status, ApoE4 carrier status, right hippocampal volume (rt. HV), left temporal lobe volume, and scores on the Revised Chinese version of the Alzheimer's Disease Assessment Scale-Cognitive Subscale 13 (ADAS-Cog-C). The risk classification of the ADAS-Cog-C or Preclinical Alzheimer Cognitive Composite (PACC) score combined with the rt. HV showed a conversion difference among the groups at every annual follow-up.

CONCLUSION

A simple risk classification using the rt. HV and neuropsychological test scores, including those from the ADAS-Cog-C and PACC, could be a practicable and efficient approach to indentify individuals at risk of all-cause dementia.

Artificial intelligence in brain MRI analysis of Alzheimer's disease over the past 12 years: A systematic review

INTRODUCTION

Multiple structural brain changes in Alzheimer's disease (AD) and mild cognitive impairment (MCI) have been revealed on magnetic resonance imaging (MRI). There is a fast-growing effort in applying artificial intelligence (AI) to analyze these data. Here, we review and evaluate the AI studies in brain MRI analysis with synthesis.

METHODS

A systematic review of the literature, spanning the years from 2009 to 2020, was completed using the PubMed database. AI studies using MRI imaging to investigate normal aging, mild cognitive impairment, and AD-dementia were retrieved for review. Bias assessment was completed using the PROBAST criteria.

RESULTS

97 relevant studies were included in the review. The studies were typically focused on the classification of AD, MCI, and normal aging (71% of the reported studies) and the prediction of MCI conversion to AD (25%). The best performance was achieved by using the deep learning-based convolution neural network algorithms (weighted average accuracy 89%), in contrast to 76-86% using Logistic Regression, Support Vector Machines, and other AI methods.

DISCUSSION

The synthesized evidence is paramount to developing sophisticated AI approaches to reliably capture and quantify multiple subtle MRI changes in the whole brain that exemplify the complexity and heterogeneity of AD and brain aging.

Imaging biomarkers in neurodegeneration: current and future practices

There is an increasing role for biological markers (biomarkers) in the understanding and diagnosis of neurodegenerative disorders. The application of imaging biomarkers specifically for the in vivo investigation of neurodegenerative disorders has increased substantially over the past decades and continues to provide further benefits both to the diagnosis and understanding of these diseases. This review forms part of a series of articles which stem from the University College London/University of Gothenburg course "Biomarkers in neurodegenerative diseases". In this review, we focus on neuroimaging, specifically positron emission tomography (PET) and magnetic resonance imaging (MRI), giving an overview of the current established practices clinically and in research as well as new techniques being developed. We will also discuss the use of machine learning (ML) techniques within these fields to provide additional insights to early diagnosis and multimodal analysis.

Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification

Early and accurate mild cognitive impairment (MCI) detection within a heterogeneous, nonclinical population is needed to improve care for persons at risk of developing dementia. Magnetic resonance imaging (MRI)-based classification may aid early diagnosis of MCI, but has only been applied within clinical cohorts. We aimed to determine the generalizability of MRI-based classification probability scores to detect MCI on an individual basis within a general population. To determine classification probability scores, an AD, mild-AD, and moderate-AD detection model were created with anatomical and diffusion MRI measures calculated from a clinical Alzheimer's Disease (AD) cohort and subsequently applied to a population-based cohort with 48 MCI and 617 normal aging subjects. Each model's ability to detect MCI was quantified using area under the receiver operating characteristic curve (AUC) and compared with an MCI detection model trained and applied to the population-based cohort. The AD-model and mild-AD identified MCI from controls better than chance level (AUC = 0.600, p = 0.025; AUC = 0.619, p = 0.008). In contrast, the moderate-AD-model was not able to separate MCI from normal aging (AUC = 0.567, p = 0.147). The MCI-model was able to separate MCI from controls better than chance (p = 0.014) with mean AUC values comparable with the AD-model (AUC = 0.611, p = 1.0). Within our population-based cohort, classification models detected MCI better than chance. Nevertheless, classification performance rates were moderate and may be insufficient to facilitate robust MRI-based MCI detection on an individual basis. Our data indicate that multiparametric MRI-based classification algorithms, that are effective in clinical cohorts, may not straightforwardly translate to applications in a general population.