scholarly journals A Cross-Validation of FDG- and Amyloid-PET Biomarkers in Mild Cognitive Impairment for the Risk Prediction to Dementia due to Alzheimer’s Disease in a Clinical Setting

2017 ◽  
Vol 59 (2) ◽  
pp. 603-614 ◽  
Author(s):  
Leonardo Iaccarino ◽  
Konstantinos Chiotis ◽  
Pierpaolo Alongi ◽  
Ove Almkvist ◽  
Anders Wall ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Risk Prediction ◽  
Clinical Setting ◽  
Cross Validation ◽  
Amyloid Pet

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.

P1-377: Amyloid PET in mild cognitive impairment and Alzheimer's disease with [11 C] BF-227: Comparison to voxel-based morphometric MRI

2010 ◽  
Vol 6 ◽  
pp. S282-S282 ◽  
Author(s):  
Masaaki Waragai ◽  
Nobuyuki Okamura ◽  
Katsutoshi Furukawa ◽  
He Shao ◽  
Manabu Tashiro ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Amyloid Pet

Diagnosis of Preclinical Alzheimer's Disease in a Clinical Setting

2001 ◽  
Vol 13 (4) ◽  
pp. 411-423 ◽  
Author(s):  
Pieter Jelle Visser ◽  
Frans R. J. Verhey ◽  
Rudolf W. H. M. Ponds ◽  
Jellemer Jolles
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Clinical Setting ◽  
Global Deterioration Scale ◽  
Delayed Recall ◽  
Clinical Criteria ◽  
Preclinical Ad ◽  
State Examination

Introduction. The aim of the study was to investigate whether the preclinical stage of Alzheimer's disease (AD) can be diagnosed in a clinical setting. To this end we investigated whether subjects with preclinical AD could be differentiated from subjects with nonprogressive mild cognitive impairment and from subjects with very mild AD-type dementia. Methods. Twenty-three subjects with preclinical AD, 44 subjects with nonprogressive mild cognitive impairment, and 25 subjects with very mild AD-type dementia were selected from a memory clinic population. Variables that were used to differentiate the groups were demographic variables, the Mini-Mental State Examination score, performance on cognitive tests, measures of functional impairment, and measures of noncognitive symptomatology. Results. Age and the scores for the delayed recall task could best discriminate between subjects with preclinical AD and subjects with nonprogressive mild cognitive impairment. The overall accuracy was 87% The score on the Global Deterioration Scale and a measure of intelligence could best discriminate between subjects with preclinical AD and subjects with very mild AD-type dementia. The overall accuracy was 85% Conclusions. Subjects with preclinical AD can be distinguished from subjects with nonprogressive mild cognitive impairment and from subjects with very mild AD-type dementia. This means that preclinical AD is a diagnostic entity for which clinical criteria should be developed.

Comparison study of amyloid PET and voxel-based morphometry analysis in mild cognitive impairment and Alzheimer's disease

2009 ◽  
Vol 285 (1-2) ◽  
pp. 100-108 ◽  
Author(s):  
Masaaki Waragai ◽  
Nobuyuki Okamura ◽  
Katsutoshi Furukawa ◽  
Manabu Tashiro ◽  
Shozo Furumoto ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Amyloid Pet ◽  
Comparison Study ◽  
Voxel Based Morphometry ◽  
Morphometry Analysis

scholarly journals The Clinical Use of Alzheimer’s Biomarkers in Patients With Mild Cognitive Impairment: A European Alzheimer’s Disease Consortium Survey

2021 ◽  
Author(s):  
Camilla Caprioglio ◽  
Valentina Garibotto ◽  
Frank Jessen ◽  
Lutz Frölich ◽  
Gilles Allali ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Fdg Pet ◽  
Diagnostic Process ◽  
Amyloid Pet ◽  
Clinical Use ◽  
Tau Pet ◽  
T Tau

Abstract Background. This study aims to investigate the clinical use of the main Alzheimer’s disease (AD) biomarkers in patients with mild cognitive impairment (MCI) by examining the beliefs and preferences of clinicians and biomarker experts of the European Alzheimer’s Disease Consortium (EADC).Methods. Out of 306 contacted EADC professionals, 150 (101 clinicians, 43 biomarker experts, and 6 falling into other categories) filled in an online survey from May to September 2020. The investigated biomarkers were: medial temporal lobe atrophy score (MTA) on structural MRI, typical AD (i.e. temporoparietal and posterior cingulate) hypometabolism on FDG-PET, CSF (Aβ42, p-tau, t-tau), amyloid-PET and tau-PET.Results. Despite the abnormal accumulation of amyloid rather than tau was deemed by the majority of responders as the initial cause of AD, responders did not show a clear preference for amyloid-PET. The most widely used biomarker is MTA (77% of responders reported to use it at least frequently), followed by Aβ42, p-tau, t-tau levels in CSF (45%), typical AD hypometabolism on FDG-PET (32%), amyloid-PET (8%), and tau-PET (2%). Imaging and CSF biomarkers were found to be widely used to support the etiological diagnostic process in MCI, while APOE genotyping was performed only in a minority of patients. CSF is considered the most valuable biomarker in terms of additional diagnostic value, followed by amyloid-PET, tau-PET, and typical AD hypometabolism on FDG-PET. The combination of amyloidosis and neuronal injury biomarkers is associated with the highest diagnostic confidence in an etiological diagnosis of AD in MCI, while MTA alone was perceived as the less reliable biomarker.Conclusions. Biomarkers are widely used across Europe for the diagnosis of MCI. Overall, we observed that CSF is currently considered as the most useful biomarker, followed by amyloid-PET. Moreover, the use of molecular imaging (i.e. amyloid-PET and tau-PET) in the diagnostic work-up of MCI patients is increasing over time.

scholarly journals Prediction of clinical and biomarker conformed Alzheimer’s disease and mild cognitive impairment from multi-feature brain structural MRI

2020 ◽  
Author(s):  
Binyin Li ◽  
Miao Zhang ◽  
Joost Riphagen ◽  
Kathryn Morrison Yochim ◽  
Biao Li ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
White Matter ◽  
Cross Validation ◽  
Cognitive Change ◽  
Structural Features ◽  
Age Effects ◽  
Validation Dataset

Abstract Background: Structural neuroimaging has been applied towards identification of individuals with Alzheimer’s disease (AD) and mild cognitive impairment (MCI). However, these methods are greatly impacted by age limiting their utility for detection of preclinical pathology. Therefore, careful consideration of age effects in the modeling of AD degenerative patterns could provide more sensitive detection of the earliest stages of brain disease.Methods: We built linear models for age based on multiple combined structural features (cortical thickness, subcortical structural volumes, ratio of gray to white matter signal intensity, white matter signal abnormalities, total intracranial volume) in 272 healthy adults across a wide age range (D1: age 36-108). These models were then used to create a new support vector machine (SVM) training model with 10-fold cross validation in 136 AD and 268 control participants (D2) based on deviations from the expected age-effects found in the initial sample. Subsequent validation assessed the accuracy of the SVM model to correctly classify AD patients in a new dataset (D3). Finally, we applied the classifier to individuals with MCI to evaluate prediction for early impairment and longitudinal cognitive change.Results: Optimal cross-validation accuracy was 93.07% in the D2, compared to 91.83% without age detrending in D1. In the validation dataset (D3), the classifier obtained an accuracy of 84.85% (56/66), sensitivity of 85.36% (35/41) and specificity of 84% (21/25). In the MCI dataset, we observed significantly greater longitudinal cognitive decline in MCI who were classified as more ‘AD-like’ (MCI-AD), and this effect was pronounced in individuals who were late MCI. The top five contributive features were volumes of left hippocampus, right hippocampus, left amygdala, the thickness of left and right medial temporal & parahippocampus gyrus.Conclusions: Linear detrending for age in SVM for combined structural features resulted in good performance for classification of AD and generalization of MCI prediction. Such procedures should be employed in future work.

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