scholarly journals An optimized MRI and PET based clinical protocol for improving the differential diagnosis of Geriatric Depression and Alzheimer’s Disease

2021 ◽  
Author(s):  
Louise Emsell ◽  
Heleen Vanhaute ◽  
Kristof Vansteelandt ◽  
François-Laurent De Winter ◽  
Danny Christiaens ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Hippocampal Volume ◽  
Amyloid Pet ◽  
Geriatric Depression ◽  
Clinical Protocol ◽  
Clinical Biomarkers ◽  
Incremental Value ◽  
Predictors Of Depression ◽  
Data Sensitivity

AbstractOBJECTIVEMRI derived hippocampal volume (HV) and amyloid PET may be useful clinical biomarkers for differentiating between geriatric depression and Alzheimer’s Disease (AD). Here we investigated the incremental value of HV and 18F-flutemetmol PET in tandem and sequentially to improve discrimination in unclassified participants.METHODTwo approaches were compared in 41 participants with geriatric depression and 27 participants with probable AD: (1) amyloid and HV combined in one model and (2) HV first and then amyloid.RESULTSBoth HV(χ2(1) = 6.46: p= 0.011) and amyloid (χ2(1) =11.03: p=0.0009) were significant diagnostic predictors of depression (sensitivity: 95%, specificity: 89%). (2) 51% of participants were correctly classified according to clinical diagnosis based on HV alone, increasing to 87% when adding amyloid data (sensitivity: 94%, specificity: 78%).CONCLUSIONHippocampal volume may be a useful gatekeeper for identifying depressed individuals at risk for AD who would benefit from additional amyloid biomarkers when available.

scholarly journals Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,125 Individuals from the ADNI and OASIS Databases

2020 ◽  
Author(s):  
Jianfeng Wu ◽  
Qunxi Dong ◽  
Jie Gui ◽  
Jie Zhang ◽  
Yi Su ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sparse Coding ◽  
Hippocampal Volume ◽  
Amyloid Pet ◽  
Individual Subject ◽  
Max Pooling ◽  
Preclinical Ad ◽  
The Individual ◽  
Low Dimensional

ABSTRACTBiomarker assisted preclinical/early detection and intervention in Alzheimer’s disease (AD) may be the key to therapeutic breakthroughs. One of the presymptomatic hallmarks of AD is the accumulation of beta-amyloid (Aβ) plaques in the human brain. However, current methods to detect Aβ pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET). Our prior studies show that MRI-based hippocampal multivariate morphometry statistics (MMS) are an effective neurodegenerative biomarker for preclinical AD. Here we attempt to use MRI-MMS to make inferences regarding brain amyloid burden at the individual subject level. As MMS data has a larger dimension than the sample size, we propose a sparse coding algorithm, Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP), to generate a low-dimensional representation of hippocampal morphometry for each individual subject. Then we apply these individual representations and a binary random forest classifier to predict brain Aβ positivity for each person. We test our method in two independent cohorts, 841 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 260 subjects from the Open Access Series of Imaging Studies (OASIS). Experimental results suggest that our proposed PASCS-MP method and MMS can discriminate Aβ positivity in people with mild cognitive impairment (MCI) (Accuracy (ACC)=0.89 (ADNI)) and in cognitively unimpaired (CU) individuals (ACC=0.79 (ADNI) and ACC=0.82 (OASIS)). These results compare favorably relative to measures derived from traditional algorithms, including hippocampal volume and surface area, shape measures based on spherical harmonics (SPHARM) and our prior Patch Analysis-based Surface Sparse-coding and Max-Pooling (PASS-MP) methods.

Incremental value of amyloid-PET versus CSF in the diagnosis of Alzheimer’s disease

2019 ◽  
Vol 47 (2) ◽  
pp. 270-280 ◽  
Author(s):  
Matteo Cotta Ramusino ◽  
Valentina Garibotto ◽  
Ruggero Bacchin ◽  
Daniele Altomare ◽  
Alessandra Dodich ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Amyloid Pet ◽  
Incremental Value

IC-P-006: THE INCREMENTAL VALUE OF AMYLOID PET VERSUS CSF BIOMARKERS FOR THE DIAGNOSIS OF ALZHEIMER'S DISEASE (INDIA-FBB STUDY)

2006 ◽  
Vol 14 (7S_Part_1) ◽  
pp. P17-P18 ◽  
Author(s):  
Matteo Cotta Ramusino ◽  
Daniele Altomare ◽  
Frederic Assal ◽  
Aline Mendes ◽  
Alfredo Costa ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Csf Biomarkers ◽  
Amyloid Pet ◽  
Incremental Value

scholarly journals Falls Associate with Neurodegenerative Changes in ATN Framework of Alzheimer’s Disease

2020 ◽  
Vol 77 (2) ◽  
pp. 745-752
Author(s):  
Audrey Keleman ◽  
Julie K. Wisch ◽  
Rebecca M. Bollinger ◽  
Elizabeth A. Grant ◽  
Tammie L. Benzinger ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Connectivity ◽  
Hippocampal Volume ◽  
Cross Sectional Study ◽  
Amyloid Pet ◽  
Resting State Functional Connectivity ◽  
Cross Sectional ◽  
Preclinical Ad ◽  
Amyloid Accumulation

Background: Behavioral markers for Alzheimer’s disease (AD) are not included within the widely used amyloid-tau-neurodegeneration framework. Objective: To determine when falls occur among cognitively normal (CN) individuals with and without preclinical AD. Methods: This cross-sectional study recorded falls among CN participants (n = 83) over a 1-year period. Tailored calendar journals recorded falls. Biomarkers including amyloid positron emission tomography (PET) and structural and functional magnetic resonance imaging were acquired within 2 years of fall evaluations. CN participants were dichotomized by amyloid PET (using standard cutoffs). Differences in amyloid accumulation, global resting state functional connectivity (rs-fc) intra-network signature, and hippocampal volume were compared between individuals who did and did not fall using Wilcoxon rank sum tests. Among preclinical AD participants (amyloid-positive), the partial correlation between amyloid accumulation and global rs-fc intra-network signature was compared for those who did and did not fall. Results: Participants who fell had smaller hippocampal volumes (p = 0.04). Among preclinical AD participants, those who fell had a negative correlation between amyloid uptake and global rs-fc intra-network signature (R = –0.75, p = 0.012). A trend level positive correlation was observed between amyloid uptake and global rs-fc intra-network signature (R = 0.70, p = 0.081) for preclinical AD participants who did not fall. Conclusion: Falls in CN older adults correlate with neurodegeneration biomarkers. Participants without falls had lower amyloid deposition and preserved global rs-fc intra-network signature. Falls most strongly correlated with presence of amyloid and loss of brain connectivity and occurred in later stages of preclinical AD.

scholarly journals Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,101 Individuals From the ADNI and OASIS Databases

2021 ◽  
Vol 15 ◽  
Author(s):  
Jianfeng Wu ◽  
Qunxi Dong ◽  
Jie Gui ◽  
Jie Zhang ◽  
Yi Su ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sparse Coding ◽  
Hippocampal Volume ◽  
Amyloid Pet ◽  
Individual Subject ◽  
Max Pooling ◽  
Preclinical Ad ◽  
The Individual ◽  
Low Dimensional

Biomarker assisted preclinical/early detection and intervention in Alzheimer’s disease (AD) may be the key to therapeutic breakthroughs. One of the presymptomatic hallmarks of AD is the accumulation of beta-amyloid (Aβ) plaques in the human brain. However, current methods to detect Aβ pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET). Our prior studies show that magnetic resonance imaging (MRI)-based hippocampal multivariate morphometry statistics (MMS) are an effective neurodegenerative biomarker for preclinical AD. Here we attempt to use MRI-MMS to make inferences regarding brain Aβ burden at the individual subject level. As MMS data has a larger dimension than the sample size, we propose a sparse coding algorithm, Patch Analysis-based Surface Correntropy-induced Sparse-coding and Max-Pooling (PASCS-MP), to generate a low-dimensional representation of hippocampal morphometry for each individual subject. Then we apply these individual representations and a binary random forest classifier to predict brain Aβ positivity for each person. We test our method in two independent cohorts, 841 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 260 subjects from the Open Access Series of Imaging Studies (OASIS). Experimental results suggest that our proposed PASCS-MP method and MMS can discriminate Aβ positivity in people with mild cognitive impairment (MCI) [Accuracy (ACC) = 0.89 (ADNI)] and in cognitively unimpaired (CU) individuals [ACC = 0.79 (ADNI) and ACC = 0.81 (OASIS)]. These results compare favorably relative to measures derived from traditional algorithms, including hippocampal volume and surface area, shape measures based on spherical harmonics (SPHARM) and our prior Patch Analysis-based Surface Sparse-coding and Max-Pooling (PASS-MP) methods.

Relationship of Hippocampal Volume to Amyloid Burden across Diagnostic Stages of Alzheimer's Disease

2015 ◽  
Vol 41 (1-2) ◽  
pp. 68-79 ◽  
Author(s):  
Paula T. Trzepacz ◽  
Helen Hochstetler ◽  
Peng Yu ◽  
Peter Castelluccio ◽  
Michael M. Witte ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Logistic Regression ◽  
Area Under The Curve ◽  
Hippocampal Volume ◽  
Elastic Net ◽  
Amyloid Pet ◽  
Or Education ◽  
Positron Emission ◽  
Relationship Of

Aims: To assess how hippocampal volume (HV) from volumetric magnetic resonance imaging (vMRI) is related to the amyloid status at different stages of Alzheimer's disease (AD) and its relevance to patient care. Methods: We evaluated the ability of HV to predict the florbetapir positron emission tomography (PET) amyloid positive/negative status by group in healthy controls (HC, n = 170) and early/late mild cognitive impairment (EMCI, n = 252; LMCI, n = 136), and AD dementia (n = 75) subjects from the Alzheimer's Disease Neuroimaging Initiative Grand Opportunity (ADNI-GO) and ADNI2. Logistic regression analyses, including elastic net classification modeling with 10-fold cross-validation, were used with age and education as covariates. Results: HV predicted amyloid status only in LMCI using either logistic regression [area under the curve (AUC) = 0.71, p < 0.001] or elastic net classification modeling [positive predictive value (PPV) = 72.7%]. In EMCI, age (AUC = 0.70, p < 0.0001) and age and/or education (PPV = 63.1%), but not HV, predicted amyloid status. Conclusion: Using clinical neuroimaging, HV predicted amyloid status only in LMCI, suggesting that HV is not a biomarker surrogate for amyloid PET in clinical applications across the full diagnostic spectrum.

P1-393: THE INCREMENTAL VALUE OF AMYLOID PET VERSUS CSF BIOMARKERS FOR THE DIAGNOSIS OF ALZHEIMER'S DISEASE (INDIA-FBB STUDY)

2006 ◽  
Vol 14 (7S_Part_8) ◽  
pp. P452-P453
Author(s):  
Matteo Cotta Ramusino ◽  
Daniele Altomare ◽  
Frederic Assal ◽  
Aline Mendes ◽  
Alfredo Costa ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Csf Biomarkers ◽  
Amyloid Pet ◽  
Incremental Value

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.

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