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.

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.

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.

BDNF Val66Met in preclinical Alzheimer's disease is associated with short-term changes in episodic memory and hippocampal volume but not serum mBDNF

2017 ◽  
Vol 29 (11) ◽  
pp. 1825-1834 ◽  
Author(s):  
Yen Ying Lim ◽  
Stephanie Rainey-Smith ◽  
Yoon Lim ◽  
Simon M. Laws ◽  
Veer Gupta ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Episodic Memory ◽  
Hippocampal Volume ◽  
Significant Decline ◽  
Preclinical Alzheimer’S Disease ◽  
Short Term ◽  
Val66met Polymorphism ◽  
Preclinical Ad

ABSTRACTBackground:The brain-derived neurotrophic factor (BDNF) Val66Met polymorphism Met allele exacerbates amyloid (Aβ) related decline in episodic memory (EM) and hippocampal volume (HV) over 36–54 months in preclinical Alzheimer's disease (AD). However, the extent to which Aβ+ and BDNF Val66Met is related to circulating markers of BDNF (e.g. serum) is unknown. We aimed to determine the effect of Aβ and the BDNF Val66Met polymorphism on levels of serum mBDNF, EM, and HV at baseline and over 18-months.Methods:Non-demented older adults (n = 446) underwent Aβ neuroimaging and BDNF Val66Met genotyping. EM and HV were assessed at baseline and 18 months later. Fasted blood samples were obtained from each participant at baseline and at 18-month follow-up. Aβ PET neuroimaging was used to classify participants as Aβ– or Aβ+.Results:At baseline, Aβ+ adults showed worse EM impairment and lower serum mBDNF levels relative to Aβ- adults. BDNF Val66Met polymorphism did not affect serum mBDNF, EM, or HV at baseline. When considered over 18-months, compared to Aβ– Val homozygotes, Aβ+ Val homozygotes showed significant decline in EM and HV but not serum mBDNF. Similarly, compared to Aβ+ Val homozygotes, Aβ+ Met carriers showed significant decline in EM and HV over 18-months but showed no change in serum mBDNF.Conclusion:While allelic variation in BDNF Val66Met may influence Aβ+ related neurodegeneration and memory loss over the short term, this is not related to serum mBDNF. Longer follow-up intervals may be required to further determine any relationships between serum mBDNF, EM, and HV in preclinical AD.

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.

Cohort-Specific Optimization of Models Predicting Preclinical Alzheimer’s Disease, to Enhance Screening Performance in the Middle of Preclinical Alzheimer’s Disease Clinical Studies

2021 ◽  
pp. 1-10
Author(s):  
K. Sato ◽  
T. Mano ◽  
R. Ihara ◽  
K. Suzuki ◽  
Y. Niimi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sex Education ◽  
Apoe Genotype ◽  
Standardized Uptake Value ◽  
Asian Population ◽  
Amyloid Pet ◽  
Preclinical Alzheimer’S Disease ◽  
Clinical Dementia Rating ◽  
Preclinical Ad

BACKGROUND: Models that can predict brain amyloid beta (Aβ) status more accurately have been desired to identify participants for clinical trials of preclinical Alzheimer’s disease (AD). However, potential heterogeneity between different cohorts and the limited cohort size have been the reasons preventing the development of reliable models applicable to the Asian population, including Japan. Objectives: We aim to propose a novel approach to predict preclinical AD while overcoming these constraints, by building models specifically optimized for ADNI or for J-ADNI, based on the larger samples from A4 study data. Design & Participants: This is a retrospective study including cognitive normal participants (CDR-global = 0) from A4 study, Alzheimer Disease Neuroimaging Initiative (ADNI), and Japanese-ADNI (J-ADNI) cohorts. Measurements: The model is made up of age, sex, education years, history of AD, Clinical Dementia Rating-Sum of Boxes, Preclinical Alzheimer Cognitive Composite score, and APOE genotype, to predict the degree of amyloid accumulation in amyloid PET as Standardized Uptake Value ratio (SUVr). The model was at first built based on A4 data, and we can choose at which SUVr threshold configuration the A4-based model may achieve the best performance area under the curve (AUC) when applied to the random-split half ADNI or J-ADNI subset. We then evaluated whether the selected model may also achieve better performance in the remaining ADNI or J-ADNI subsets. Result: When compared to the results without optimization, this procedure showed efficacy of AUC improvement of up to approximately 0.10 when applied to the models “without APOE;” the degree of AUC improvement was larger in the ADNI cohort than in the J-ADNI cohort. Conclusions: The obtained AUC had improved mildly when compared to the AUC in case of literature-based predetermined SUVr threshold configuration. This means our procedure allowed us to predict preclinical AD among ADNI or J-ADNI second-half samples with slightly better predictive performance. Our optimizing method may be practically useful in the middle of the ongoing clinical study of preclinical AD, as a screening to further increase the prior probability of preclinical AD before amyloid testing.

scholarly journals Cognitively normal women with Alzheimer’s disease proteinopathy show relative preservation of memory but not of hippocampal volume

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Jessica Z. K. Caldwell ◽  
Jeffrey L. Cummings ◽  
Sarah J. Banks ◽  
Sebastian Palmqvist ◽  
Oskar Hansson
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Verbal Memory ◽  
Interactive Effect ◽  
Hippocampal Volume ◽  
Word Recall ◽  
Subjective Cognitive Decline ◽  
Cognitively Normal ◽  
Sex Diagnosis ◽  
Preclinical Ad

Abstract Background We examined interactive effects of sex, diagnosis, and cerebrospinal fluid (CSF) amyloid beta/phosphorylated tau ratio (Aβ/P-tau) on verbal memory and hippocampal volumes. Methods We assessed 682 participants (350 women) from BioFINDER (250 cognitively normal [CN]; and 432 symptomatic: 186 subjective cognitive decline [SCD], 246 mild cognitive impairment [MCI]). General linear models evaluated effects of Alzheimer’s disease (AD) proteinopathy (CSF Aß/p-tau ratio), diagnosis, and sex on verbal memory (ADAS-cog 10-word recall), semantic fluency (animal naming fluency), visuospatial skills (cube copy), processing speed/attention functions (Symbol Digit Modalities Test and Trail Making Part A), and hippocampal volumes. Results Amyloid-positive (Aβ/P-tau+) CN women (women with preclinical AD) showed memory equivalent to amyloid-negative (Aβ/P-tau−) CN women. In contrast, Aβ/P-tau+ CN men (men with preclinical AD) showed poorer memory than Aβ/P-tau− CN men. Symptomatic groups showed no sex differences in effect of AD proteinopathy on memory. There was no interactive effect of sex, diagnosis, and Aβ/P-tau on other measures of cognition or on hippocampal volume. Conclusions CN women show relatively preserved verbal memory, but not general cognitive reserve or preserved hippocampal volume in the presence of Aβ/P-tau+. Results have implications for diagnosing AD in women, and for clinical trials.

C-13 Sex Differences in Cognitive and Neurobiological Markers of Alzheimer's Disease

2019 ◽  
Vol 34 (6) ◽  
pp. 1041-1042
Author(s):  
J O'Hara ◽  
D Norton ◽  
R Koscik ◽  
N Lambrou ◽  
M Wyman ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sex Differences ◽  
Group Performance ◽  
Verbal Learning ◽  
Hippocampal Volume ◽  
Individual Performance ◽  
Preclinical Ad ◽  
Z Scores ◽  
Naming Test

Abstract Objective Previous work has demonstrated that intra-individual cognitive variability (IICV) has predictive power similar to traditional Alzheimer’s disease (AD) biomarkers, such as CSF or hippocampal volume (HV) loss. Genetic factors, such as sex, have been identified as predictors of cognitive decline. Analysis of sex differences in IICV and other biomarkers may elucidate additional dimensions of this metric. Method Baseline neurocognitive test and neuroimaging data from 335 participants with ≥2 visits enrolled in the Wisconsin Alzheimer’s Disease Research Center Clinical Core were included. Z-scores were calculated comparing individual performance to group performance by test (Rey Auditory Verbal Learning Test (Learning and Delayed Recall), Trail Making Test (A and B), and either Boston Naming Test (BNT) or Multilingual Naming Test (MINT)). MINT scores were converted to BNT scores using the NACC Crosswalk Study. The standard deviation of z-scores across tests was calculated to determine IICV. Characteristics by sex were compared using Mann-Whitney and Fisher’s Exact tests. Spearman’s Rho was calculated to compare IICV and HV (relative to intercranial volume). Results At baseline (Table 1): (1) Males had more education than females; (2) females had both higher relative HV and IICV; and (3) in females, relative HV demonstrated a weak positive correlation with baseline IICV (Figure 1). Conclusions IICV has previously demonstrated potential as a cost-effective non-invasive marker of preclinical AD. In females, larger relative HV and its correlation with IICV may be due to differences in metabolic brain age or concurrent progression of HV and IICV through the AD process. Analyses of other biopsychosocial factors are needed.

MRI-Based Alzheimer’s Disease-Resemblance Atrophy Index in the Detection of Preclinical and Prodromal Alzheimer’s Disease

2020 ◽  
Author(s):  
Wanting Liu ◽  
Lisa Wing Chi Au ◽  
Jill Abrigo ◽  
Yishan Luo ◽  
Adrian Wong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Hippocampal Volume ◽  
Prodromal Stage ◽  
Preclinical Ad ◽  
Volume Sensitivity ◽  
Prodromal Ad ◽  
Prodromal Alzheimer’S Disease ◽  
Higher Sensitivity

Abstract Background: We aimed to validate the performance of an MRI-based machine learning derived Alzheimer’s Disease-resemblance atrophy index (AD-RAI) in detecting preclinical and prodromal AD. Methods: A total of 62 subjects (mild cognitive impairment [MCI]=25, cognitively unimpaired [CU]=37) underwent MRI, 11C- PIB, and 18F-T807 PET. We investigated the performance of AD-RAI at the pre-specified cutoff of ≥ 0.5 in detecting preclinical and prodromal AD and compared its performance with that of visual and volumetric hippocampal measures. Results: AD-RAI achieved the best metrics among all subjects (sensitivity 0.73, specificity 0.91, accuracy 87.10%) and among MCI subgroup (sensitivity 0.91, specificity 0.79, accuracy 84.00%) in detecting A+T+ subjects over other measures. Among CU subgroup, hippocampal volume (sensitivity 0.75, specificity 0.88, accuracy 86.49%) achieved a higher sensitivity than AD-RAI (sensitivity 0.25, specificity 0.97, accuracy 89.19%) in detecting preclinical AD.Conclusions: AD-RAI aids the detection of early AD, in particular at the prodromal stage.

scholarly journals Preclinical diagnosis of Alzheimer's disease: Prevention or prediction?

2010 ◽  
Vol 4 (4) ◽  
pp. 259-261 ◽  
Author(s):  
Ricardo Nitrini
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Preventive Treatment ◽  
New Drugs ◽  
Good News ◽  
Definitive Treatment ◽  
Preclinical Ad ◽  
Preclinical Diagnosis ◽  
Receive Treatment ◽  
The Individual

Abstract The diagnosis of Alzheimer's disease (AD) for cases with dementia may be too late to allow effective treatment. Criteria for diagnosis of preclinical AD suggested by the Alzheimer's Association include the use of molecular and structural biomarkers. Preclinical diagnosis will enable testing of new drugs and forms of treatment toward achieving successful preventive treatment. But what are the advantages for the individual? To know that someone who is cognitively normal is probably going to develop AD's dementia when there is no effective preventive treatment is definitely not good news. A research method whereby volunteers are assigned to receive treatment or placebo without knowing whether they are in the control or at-risk arm of a trial would overcome this potential problem. If these new criteria are used wisely they may represent a relevant milestone in the search for a definitive treatment for AD.

Sign in / Sign up

Export Citation Format

Share Document