Baseline Neuroimaging Predicts Decline to Dementia From Amnestic Mild Cognitive Impairment

Abstract

Background and Objectives: Prediction of decline to dementia using objective biomarkers in high-risk patients with amnestic mild cognitive impairment (aMCI) has immense utility. Our objective was to use multimodal MRI to (1) determine whether accurate and precise prediction of dementia conversion could be achieved using baseline data alone, and (2) generate a map of the brain regions implicated in longitudinal decline to dementia.Methods: Participants meeting criteria for aMCI at baseline (N = 55) were classified at follow-up as remaining stable/improved in their diagnosis (N = 41) or declined to dementia (N = 14). Baseline T1 structural MRI and resting-state fMRI (rsfMRI) were combined and a semi-supervised support vector machine (SVM) which separated stable participants from those who decline at follow-up with maximal margin. Cross-validated model performance metrics and MRI feature weights were calculated to include the strength of each brain voxel in its ability to distinguish the two groups.Results: Total model accuracy for predicting diagnostic change at follow-up was 92.7% using baseline T1 imaging alone, 83.5% using rsfMRI alone, and 94.5% when combining T1 and rsfMRI modalities. Feature weights that survived the p < 0.01 threshold for separation of the two groups revealed the strongest margin in the combined structural and functional regions underlying the medial temporal lobes in the limbic system.Discussion: An MRI-driven SVM model demonstrates accurate and precise prediction of later dementia conversion in aMCI patients. The multi-modal regions driving this prediction were the strongest in the medial temporal regions of the limbic system, consistent with literature on the progression of Alzheimer’s disease.

Biomarkers for dementia in Latin American countries: Gaps and opportunities

“…Even respondents who do not use it are interested and see this as a useful tool supporting clinical and research settings. This result may be explained by the growing body of evidence supporting the use of neuroimaging biomarkers throughout the dementia continuum (MCI 75 ) and care pathway including diagnosis, 76 progression, 77 and predicting treatment response. 78 Neither peripheral nor genetic biomarkers are currently reported as being used for clinical purposes.…”

Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine

“… Patel et al (2019) developed two XGBoost classification models to classify AD and healthy control (HC). Studies have proved that AD was closely related to brain atrophy and that brain atrophy was mainly reflected in the reduction of cortical surface area, thickness, and gray matter volume and, therefore, gray matter volume, cortical surface area, and average thickness contributed to the pathology of AD patients ( Gullett et al, 2021 ; Lorenzo et al, 2021 ; Piersson et al, 2021 ; Talwar et al, 2021 ).…”

Artificial intelligence in dementia

“…Jang et al [17 ▪ ] fused eye movement data and speech data in their multimodal classification model of Alzheimer's disease to obtain a competitive AUC of 0.83. Eye movement data are the promising pathway because it can be captured without sophisticated equipment, possibly even by an untrained individual [18 ▪ ,19 ▪▪ ,20–29,30 ▪▪ ,31,32 ▪ ,33 ▪ ,34,35].…”