scholarly journals Baseline Neuroimaging Predicts Decline to Dementia From Amnestic Mild Cognitive Impairment

2021 ◽  
Vol 13 ◽  
Author(s):  
Joseph M. Gullett ◽  
Alejandro Albizu ◽  
Ruogu Fang ◽  
David A. Loewenstein ◽  
Ranjan Duara ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Limbic System ◽  
Performance Metrics ◽  
Brain Regions ◽  
Amnestic Mild Cognitive Impairment ◽  
Support Vector ◽  
Feature Weights ◽  
Precise Prediction

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.

Elucidating the risk factors for progression from amyloid-negative amnestic mild cognitive impairment to dementia

2020 ◽  
Vol 17 ◽  
Author(s):  
Hyung-Ji Kim ◽  
Jae-Hong Lee ◽  
E-nae Cheong ◽  
Sung-Eun Chung ◽  
Sungyang Jo ◽  
...  
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Amnestic Mild Cognitive Impairment ◽  
Amyloid Pet ◽  
Clinical Progression ◽  
Amnestic Mci

Background: Amyloid PET allows for the assessment of amyloid β status in the brain, distinguishing true Alzheimer’s disease from Alzheimer’s disease-mimicking conditions. Around 15–20% of patients with clinically probable Alzheimer’s disease have been found to have no significant Alzheimer’s pathology on amyloid PET. However, a limited number of studies had been conducted this subpopulation in terms of clinical progression. Objective: We investigated the risk factors that could affect the progression to dementia in patients with amyloid-negative amnestic mild cognitive impairment (MCI). Methods: This study was a single-institutional, retrospective cohort study of patients over the age of 50 with amyloidnegative amnestic MCI who visited the memory clinic of Asan Medical Center with a follow-up period of more than 36 months. All participants underwent brain magnetic resonance imaging (MRI), detailed neuropsychological testing, and fluorine-18[F18]-florbetaben amyloid PET. Results: During the follow-up period, 39 of 107 patients progressed to dementia from amnestic MCI. In comparison with the stationary group, the progressed group had a more severe impairment in verbal and visual episodic memory function and hippocampal atrophy, which showed an Alzheimer’s disease-like pattern despite the lack of evidence for significant Alzheimer’s disease pathology. Voxel-based morphometric MRI analysis revealed that the progressed group had a reduced gray matter volume in the bilateral cerebellar cortices, right temporal cortex, and bilateral insular cortices. Conclusion: Considering the lack of evidence of amyloid pathology, clinical progression of these subpopulation may be caused by other neuropathologies such as TDP-43, abnormal tau or alpha synuclein that lead to neurodegeneration independent of amyloid-driven pathway. Further prospective studies incorporating biomarkers of Alzheimer’s diseasemimicking dementia are warranted.

Early Microstructure Changes of White Matter Fiber Bundles in Patients with Amnestic Mild Cognitive Impairment Predicts Progression of Mild Cognitive Impairment to Alzheimer’s Disease

2021 ◽  
pp. 1-14
Author(s):  
Fangmei He ◽  
Yuchen Zhang ◽  
Xiaofeng Wu ◽  
Youjun Li ◽  
Jie Zhao ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
Brain Regions ◽  
Amnestic Mild Cognitive Impairment ◽  
Microstructure Changes ◽  
Disease Trajectory ◽  
Development Trajectory ◽  
The Right

Background: Amnestic mild cognitive impairment (aMCI) is the transitional stage between normal aging and Alzheimer’s disease (AD). Some aMCI patients will progress into AD eventually, whereas others will not. If the trajectory of aMCI can be predicted, it would enable early diagnosis and early therapy of AD. Objective: To explore the development trajectory of aMCI patients, we used diffusion tensor imaging to analyze the white matter microstructure changes of patients with different trajectories of aMCI. Methods: We included three groups of subjects:1) aMCI patients who convert to AD (MCI-P); 2) aMCI patients who remain in MCI status (MCI-S); 3) normal controls (NC). We analyzed the fractional anisotropy and mean diffusion rate of brain regions, and we adopted logistic binomial regression model to predicate the development trajectory of aMCI. Results: The fraction anisotropy value is significantly reduced, the mean diffusivity value is significantly increased in the two aMCI patient groups, and the MCI-P patients presented greater changes. Significant changes are mainly located in the cingulum, fornix, hippocampus, and uncinate fasciculus. These changed brain regions significantly correlated with the patient’s Mini-Mental State Examination scores. Conclusion: The study predicted the disease trajectory of different types of aMCI patients based on the characteristic values of the above-mentioned brain regions. The prediction accuracy rate can reach 90.2%, and the microstructure characteristics of the right cingulate band and the right hippocampus may have potential clinical application value to predict the disease trajectory.

scholarly journals Distinct Subgroups of Amnestic Mild Cognitive Impairment as Identified by Soft Independent Modeling of Class Analogy

2019 ◽  
Author(s):  
Sookjaroen Tangwongchai ◽  
Itthipol Tawankanjanachot ◽  
Chavit Tunvirachaisakul ◽  
Thitiporn Supasitthumrong ◽  
Solaphat Hemrungrojn ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Neuropsychological Tests ◽  
Diagnostic Category ◽  
Principal Component ◽  
Amnestic Mild Cognitive Impairment ◽  
Cognitive Profile ◽  
Support Vector ◽  
Patients At Risk ◽  
Normal Controls

Amnestic mild cognitive impairment (aMCI) is a condition characterized by mild deficits in episodic and semantic memory and learning. The conversion rate of aMCI to Alzheimer disease (AD) is significantly higher in aMCI than in the general population. The aim of this study is to examine whether aMCI is a valid diagnostic category or whether aMCI comprises different subgroups based on cognitive functions. We recruited 60 aMCI patients, 60 with AD and 61 healthy controls who completed neuropsychological tests of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD-NP) and biomarkers including serum anion gap (AGAP). Principal component analysis, support vector machine and Soft Independent Modeling of Class Analogy (SIMCA) showed that AD patients and controls were highly significantly discrimanted from each other, while patients with aMCI overlap considerably with normal controls. SIMCA showed that 68.3% of the aMCI patients were assigned to the control class (named: aMCI-HC), 15% to AD (aMCI-AD), while 16.6% did not belong to either class (aMCI-strangers). aMCI-HC subjects showed sings of very mild cognitive decline and impaired recall. aMCI-strangers showed signs of mild cognitive impairment with impaired fluency and naming. aMCI-AD cases showed a cognitive profile reminiscent of AD an increased AGAP levels. In conclusion, our SIMCA model may classify subjects afforded a clinical diagnosis of aMCI according to Petersen’s criteria into three clinically relevant subgroups and help in the early detection of AD by identifying aMCI patients at risk to develop AD and those that have an AD prodrome.

scholarly journals Plasma Transthyretin as a Predictor of Amnestic Mild Cognitive Impairment Conversion to Dementia

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Yi-Ting Tien ◽  
Wei-Ju Lee ◽  
Yi-Chu Liao ◽  
Wen-Fu Wang ◽  
Kai-Ming Jhang ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Proportional Hazards ◽  
Continuous Variable ◽  
Cox Proportional Hazards ◽  
Amnestic Mild Cognitive Impairment ◽  
Teaching Hospitals ◽  
Outcome Variable ◽  
Control Group

AbstractAmnestic mild cognitive impairment (MCI) is a prodromal stage of dementia, with a higher incidence of these patients progressing to Alzheimer’s disease (AD) than normal aging people. A biomarker for the early detection and prediction for this progression is important. We recruited MCI subjects in three teaching hospitals and conducted longitudinal follow-up for 5 years at one-year intervals. Cognitively healthy controls were recruited for comparisom at baseline. Plasma transthyretin (TTR) levels were measured by ELISA. Survival analysis with time to AD conversion as an outcome variable was calculated with the multivariable Cox proportional hazards models using TTR as a continuous variable with adjustment for other covariates and bootstrapping resampling analysis. In total, 184 MCI subjects and 40 sex- and age-matched controls were recruited at baseline. At baseline, MCI patients had higher TTR levels compared with the control group. During the longitudinal follow-ups, 135 MCI patients (73.4%) completed follow-up at least once. The TTR level was an independent predictor for MCI conversion to AD when using TTR as a continuous variable (p = 0.023, 95% CI 1.001–1.007). In addition, in MCI converters, the TTR level at the point when they converted to AD was significantly lower than that at baseline (328.6 ± 66.5 vs. 381.9 ± 77.6 ug/ml, p < 0.001). Our study demonstrates the temporal relationship between the plasma TTR level and the conversion from MCI to AD.

scholarly journals Altered Volume and Structural Connectivity of the Hippocampus in Alzheimer’s Disease and Amnestic Mild Cognitive Impairment

2021 ◽  
Vol 13 ◽  
Author(s):  
Feng Feng ◽  
Weijie Huang ◽  
Qingqing Meng ◽  
Weijun Hao ◽  
Hongxiang Yao ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Hippocampal Volume ◽  
Brain Regions ◽  
Amnestic Mild Cognitive Impairment ◽  
Hippocampal Atrophy

Background: Hippocampal atrophy is a characteristic of Alzheimer’s disease (AD). However, alterations in structural connectivity (number of connecting fibers) between the hippocampus and whole brain regions due to hippocampal atrophy remain largely unknown in AD and its prodromal stage, amnestic mild cognitive impairment (aMCI).Methods: We collected high-resolution structural MRI (sMRI) and diffusion tensor imaging (DTI) data from 36 AD patients, 30 aMCI patients, and 41 normal control (NC) subjects. First, the volume and structural connectivity of the bilateral hippocampi were compared among the three groups. Second, correlations between volume and structural connectivity in the ipsilateral hippocampus were further analyzed. Finally, classification ability by hippocampal volume, its structural connectivity, and their combination were evaluated.Results: Although the volume and structural connectivity of the bilateral hippocampi were decreased in patients with AD and aMCI, only hippocampal volume correlated with neuropsychological test scores. However, positive correlations between hippocampal volume and ipsilateral structural connectivity were displayed in patients with AD and aMCI. Furthermore, classification accuracy (ACC) was higher in AD vs. aMCI and aMCI vs. NC by the combination of hippocampal volume and structural connectivity than by a single parameter. The highest values of the area under the receiver operating characteristic (ROC) curve (AUC) in every two groups were all obtained by combining hippocampal volume and structural connectivity.Conclusions: Our results showed that the combination of hippocampal volume and structural connectivity (number of connecting fibers) is a new perspective for the discrimination of AD and aMCI.

Late-Life Depression versus Amnestic Mild Cognitive Impairment: Alzheimer’s Disease Incidence in 4 Years of Follow-Up

2018 ◽  
Vol 46 (3-4) ◽  
pp. 140-153 ◽  
Author(s):  
Michele Lauriola ◽  
Antonio Mangiacotti ◽  
Grazia D’Onofrio ◽  
Leandro Cascavilla ◽  
Francesco Paris ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Medical Condition ◽  
Disease Incidence ◽  
Late Life ◽  
Amnestic Mild Cognitive Impairment ◽  
Late Life Depression

Background/Aim: The aim of the study was to evaluate the prognostic power of late-life depression (LLD) compared with amnestic mild cognitive impairment (aMCI) for the onset of Alzheimer’s disease (AD) within 4 years of follow-up. Methods: We estimated the incidence of AD in 60 patients presenting with aMCI, 115 patients suffering of LLD treated with antidepressants with good compliance, and 66 healthy control (HC) patients, followed for 4 years. Results: The risk to develop AD, within 4 years, was 68.33% for aMCI and 49.57% for LLD. In AD patients 5.60% deteriorated without depression, and 72.20% deteriorated with depression after 4 years of follow-up (p < 0.0001). No HC patients deteriorated to AD or any other dementia type. Conclusion: In our results, aMCI was the first predictive condition that increased the risk to develop AD. Depression is a potentially preventable medical condition across the lifespan and may be a modifiable risk factor.

scholarly journals Can Clinical Data Predict Progression to Dementia in Amnestic Mild Cognitive Impairment?

2008 ◽  
Vol 35 (3) ◽  
pp. 314-322 ◽  
Author(s):  
Lesley Fellows ◽  
Howard Bergman ◽  
Christina Wolfson ◽  
Howard Chertkow
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Clinical Data ◽  
Symptom Onset ◽  
Amnestic Mild Cognitive Impairment ◽  
Elderly Subjects ◽  
Prospective Longitudinal Study ◽  
Amnestic Mci ◽  
Prospective Longitudinal

Background:To determine whether clinical data obtained by history and physical examination can predict eventual progression to dementia in a cohort of elderly people with mild cognitive impairment.Methods:A prospective, longitudinal study of a cohort of elderly subjects with amnestic Mild Cognitive Impairment (MCI). Ninety subjects meeting the criteria for amnestic MCI were recruited and followed annually for an average of 3.3 years. Main outcome measure was the development of dementia determined by clinical assessment with confirmatory neuropsychological evaluation.Results:Fifty patients (56%) developed dementia on follow-up. They were older, had lower Mini-mental status exam (MMSE) scores and a shorter duration of symptoms at the time of first assessment. Multivariate logistic regression analysis identified age at symptom onset as the only clinical parameter which distinguished the group that deteriorated to dementia from the group that did not. The odds ratio for age was 1.1 (confidence interval 1.04 - 1.18).Conclusions:Patients presenting with amnestic MCI insufficient for the diagnosis of dementia are at high risk of developing dementia on follow-up. In our cohort, 56% were diagnosed with dementia over an average period of 5.9 years from symptom onset. The only clinical predictor for the eventual development of dementia was older age at symptom onset. Clinical features alone were insufficient to predict development of dementia.

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