Deficits in Mitochondrial Spare Respiratory Capacity Contribute to the Neuropsychological Changes of Alzheimer’s Disease

Alzheimer’s disease (AD) is diagnosed using neuropsychological testing, supported by amyloid and tau biomarkers and neuroimaging abnormalities. The cause of neuropsychological changes is not clear since they do not correlate with biomarkers. This study investigated if changes in cellular metabolism in AD correlate with neuropsychological changes. Fibroblasts were taken from 10 AD patients and 10 controls. Metabolic assessment included measuring total cellular ATP, extracellular lactate, mitochondrial membrane potential (MMP), mitochondrial respiration and glycolytic function. All participants were assessed with neuropsychological testing and brain structural MRI. AD patients had significantly lower scores in delayed and immediate recall, semantic memory, phonemic fluency and Mini Mental State Examination (MMSE). AD patients also had significantly smaller left hippocampal, left parietal, right parietal and anterior medial prefrontal cortical grey matter volumes. Fibroblast MMP, mitochondrial spare respiratory capacity (MSRC), glycolytic reserve, and extracellular lactate were found to be lower in AD patients. MSRC/MMP correlated significantly with semantic memory, immediate and delayed episodic recall. Correlations between MSRC and delayed episodic recall remained significant after controlling for age, education and brain reserve. Grey matter volumes did not correlate with MRSC/MMP. AD fibroblast metabolic assessment may represent an emergent disease biomarker of AD.

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Do deficits in mitochondrial spare respiratory capacity contribute to neuropsychological changes seen in Alzheimer’s disease?

Alzheimer s & Dementia ◽

10.1002/alz.037527 ◽

2020 ◽

Vol 16 (S6) ◽

Author(s):

Simon M. Bell ◽

Matteo De Marco ◽

Katy Barnes ◽

Pamela Shaw ◽

Laura Ferraiuolo ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Respiratory Capacity ◽

Neuropsychological Changes

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A simple classification framework for predicting Alzheimer’s disease from region-based grey matter volume and APOE genotype status

Artificial Intelligence Research ◽

10.5430/air.v8n2p15 ◽

2020 ◽

Vol 8 (2) ◽

pp. 15

Author(s):

Reyhaneh Ghoreishiamiri ◽

Graham Little ◽

Matthew R. G. Brown ◽

Russell Greiner

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Grey Matter ◽

Learning Algorithm ◽

Apoe Genotype ◽

Structural Mri ◽

Grey Matter Volume ◽

Healthy Controls ◽

Diagnostic Systems ◽

Initial Dataset

Alzheimer’s Disease (AD) is a prevalent neurodegenerative disease currently affecting more than 47 million people in the world. There are now many complex classifiers that can accurately distinguish AD patients from healthy controls, based on the subject’s structural magnetic resonance imaging (MRI) brain scan. Most such automated diagnostic systems are blackboxes: While their predictions are accurate, it is difficult for clinicians to interpret those predictions, due to the large number of features used by the classifier, and/or by the complexity of that classifier. This work demonstrates that an automated learning algorithm can produce a simple classifier that can correctly distinguish AD patients from healthy controls (HC) similar to its more-complex counterparts. Here we buildthis classifier from the data in the Alzheimer’s Disease Neuroimaging Initiative database, using a fairly small set of features, including grey matter volumes of 33 regions of interest derived from structural MRI, as well as the APOE genotype. We first considered three simple base-learners that each produce a classifier that is simple and interpretable. Running our overall learner, involving standard feature selection processes and these simple base-learners, on these features, produced a 7-feature elastic net model, EN7, that achieved accuracy of 89.28% on the test set. Next, we ran the same overall learner using two more-complex base-learners over the same initial dataset. The accuracy of the best model here was 90.47%, which was not statistically different from the performance of our much simpler EN7 model.

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Hippocampal volume and retention in Alzheimer's disease

Journal of the International Neuropsychological Society ◽

10.1017/s1355617704104050 ◽

2004 ◽

Vol 10 (4) ◽

pp. 639-643 ◽

Cited By ~ 47

Author(s):

JOEL H. KRAMER ◽

NORBERT SCHUFF ◽

BRUCE R. REED ◽

DAN MUNGAS ◽

AN-TAO DU ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Free Recall ◽

Verbal Memory ◽

Memory Task ◽

Structural Mri ◽

Hippocampal Volume ◽

Immediate Recall ◽

Cortical Gray Matter

This study tested the hypothesis that the hippocampus has a relatively specific role in retaining information over delays. Thirty-seven subjects with probable Alzheimer's disease were evaluated with a verbal memory task and structural MRI. Cortical gray matter but not hippocampal volume predicted immediate free recall. In contrast, hippocampal volume was the best predictor of how well information was retained over a delay, even after controlling for levels of immediate recall. Results suggest that the role of the hippocampus is relatively specific to the consolidation of new memories. (JINS, 2004, 10, 639–643.)

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Manual Correction of Voxel Misclassifications in Mesiotemporal Structures Does Not Alter Brain–Behavioral Results in an Episodic Memory Task

Journal of Clinical Medicine ◽

10.3390/jcm10214869 ◽

2021 ◽

Vol 10 (21) ◽

pp. 4869

Author(s):

Francina Hartmann ◽

Julia Reinhardt ◽

Christoph Stippich ◽

Sabine Krumm

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Episodic Memory ◽

Grey Matter ◽

Memory Performance ◽

Neuropsychological Testing ◽

Memory Task ◽

Disease Spectrum ◽

Imaging Results ◽

Manual Correction

Voxel-based morphometry (VBM) is an established method for assessing grey matter volumes across the brain. The quality of magnetic resonance imaging (MRI) and the chosen data preprocessing steps can affect the outcome of VBM analyses. We recognized a lack of publicly available and commonly used protocols, which indicates that standardized and optimized preprocessing protocols are needed. This paper focuses on the time- and resource-consuming manual correction of misclassifications of grey matter voxels in cortical structures important in Alzheimer’s dementia. A total of 126 individuals, including 63 patients with very early Alzheimer’s disease and 63 cognitively normal participants, received thorough neuropsychological testing and 3-Tesla MRI. Automated preprocessing of T1 MPRAGE images was performed, and misclassifications of grey matter voxels were manually identified and corrected. In a second run, the manual correction step was skipped. Multiple regression analyses using DARTEL in SPM8 were then conducted with the manually corrected and uncorrected sample, respectively. Manual correction of voxel misclassifications did not have a major impact on the correlation between episodic memory performance and structural brain imaging results. We conclude that, although performing all preprocessing steps remains the gold standard, skipping manual correction of voxel misclassifications is permitted when investigating populations on the Alzheimer’s disease spectrum.

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Microglial activation and tau burden predict cognitive decline in Alzheimer’s Disease

10.1101/19011189 ◽

2019 ◽

Author(s):

Maura Malpetti ◽

Rogier A. Kievit ◽

Luca Passamonti ◽

P. Simon Jones ◽

Kamen A. Tsvetanov ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Decline ◽

Predictive Model ◽

Brain Atrophy ◽

Grey Matter ◽

Imaging Modality ◽

Structural Mri ◽

Tau Pathology ◽

Single Modality

AbstractTau pathology, neuroinflammation, and neurodegeneration are key aspects of Alzheimer’s disease. Understanding whether these features predict cognitive decline, alone or in combination, is crucial to develop new prognostic measures and enhanced stratification for clinical trials. Here, we studied how baseline assessments of in vivo tau pathology (measured by [18F]AV-1451 PET), neuroinflammation (indexed via [11C]PK11195 PET) and brain atrophy (derived from structural MRI) predicted longitudinal cognitive changes in patients with Alzheimer’s disease pathology. Twenty-six patients (n=12 with clinically probable Alzheimer’s dementia and n=14 with amyloid positive Mild Cognitive Impairment) and 29 healthy controls underwent baseline assessment with [18F]AV-1451 PET, [11C]PK11195 PET, and structural MRI. Cognition was examined annually over the subsequent 3 years using the revised Addenbrooke’s Cognitive Examination. Regional grey-matter volumes, [18F]AV-1451 and [11C]PK11195 binding were derived from fifteen temporo-parietal regions characteristically affected by Alzheimer’s disease pathology. A Principal Component Analysis (PCA) was used on each imaging modality separately, to identify the main spatial distributions of pathology. A Latent Growth Curve model was applied across the whole sample on longitudinal cognitive scores to estimate the rate of annual decline in each participant. We regressed the individuals’ estimated slope of cognitive decline on the neuroimaging components and examined univariable models with single-modality predictors, and a multi-modality model of prediction, to identify the independent and combined prognostic value of the different neuroimaging markers.PCA identified a single component for the grey-matter atrophy, while two components were found for each PET ligand: one weighted to the anterior temporal lobe, and another weighted to posterior temporo-parietal regions. Across the whole-sample, the single-modality models indicated significant correlations between the slope of cognitive decline and the first component of each imaging modality. In patients, both stepwise backward elimination and Bayesian model selection revealed an optimal predictive model that included both components of [18F]AV-1451 and the first (i.e., anterior temporal) component for [11C]PK11195. However, the MRI-derived atrophy component and demographic variables were excluded from the optimal predictive model of cognitive decline. We conclude that temporo-parietal tau pathology and anterior temporal neuroinflammation predict cognitive decline in patients with symptomatic Alzheimer’s disease pathology. This indicates the added value of PET biomarkers in predicting cognitive decline in Alzheimer’s disease, over and above MRI measures of brain atrophy and demographic data. Our findings also support the strategy for targeting tau and neuroinflammation in disease-modifying therapy against Alzheimer’s Disease.

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Microglial activation and tau burden predict cognitive decline in Alzheimer’s disease

Brain ◽

10.1093/brain/awaa088 ◽

2020 ◽

Vol 143 (5) ◽

pp. 1588-1602 ◽

Cited By ~ 5

Author(s):

Maura Malpetti ◽

Rogier A Kievit ◽

Luca Passamonti ◽

P Simon Jones ◽

Kamen A Tsvetanov ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Decline ◽

Predictive Model ◽

Grey Matter ◽

Imaging Modality ◽

Principal Component ◽

Structural Mri ◽

Tau Pathology ◽

Single Modality

Abstract Tau pathology, neuroinflammation, and neurodegeneration are key aspects of Alzheimer’s disease. Understanding whether these features predict cognitive decline, alone or in combination, is crucial to develop new prognostic measures and enhanced stratification for clinical trials. Here, we studied how baseline assessments of in vivo tau pathology (measured by 18F-AV-1451 PET), neuroinflammation (measured by 11C-PK11195 PET) and brain atrophy (derived from structural MRI) predicted longitudinal cognitive changes in patients with Alzheimer’s disease pathology. Twenty-six patients (n = 12 with clinically probable Alzheimer’s dementia and n = 14 with amyloid-positive mild cognitive impairment) and 29 healthy control subjects underwent baseline assessment with 18F-AV-1451 PET, 11C-PK11195 PET, and structural MRI. Cognition was examined annually over the subsequent 3 years using the revised Addenbrooke’s Cognitive Examination. Regional grey matter volumes, and regional binding of 18F-AV-1451 and 11C-PK11195 were derived from 15 temporo-parietal regions characteristically affected by Alzheimer’s disease pathology. A principal component analysis was used on each imaging modality separately, to identify the main spatial distributions of pathology. A latent growth curve model was applied across the whole sample on longitudinal cognitive scores to estimate the rate of annual decline in each participant. We regressed the individuals’ estimated rate of cognitive decline on the neuroimaging components and examined univariable predictive models with single-modality predictors, and a multi-modality predictive model, to identify the independent and combined prognostic value of the different neuroimaging markers. Principal component analysis identified a single component for the grey matter atrophy, while two components were found for each PET ligand: one weighted to the anterior temporal lobe, and another weighted to posterior temporo-parietal regions. Across the whole-sample, the single-modality models indicated significant correlations between the rate of cognitive decline and the first component of each imaging modality. In patients, both stepwise backward elimination and Bayesian model selection revealed an optimal predictive model that included both components of 18F-AV-1451 and the first (i.e. anterior temporal) component for 11C-PK11195. However, the MRI-derived atrophy component and demographic variables were excluded from the optimal predictive model of cognitive decline. We conclude that temporo-parietal tau pathology and anterior temporal neuroinflammation predict cognitive decline in patients with symptomatic Alzheimer’s disease pathology. This indicates the added value of PET biomarkers in predicting cognitive decline in Alzheimer’s disease, over and above MRI measures of brain atrophy and demographic data. Our findings also support the strategy for targeting tau and neuroinflammation in disease-modifying therapy against Alzheimer’s disease.

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Tetrahedral Spectral Feature-Based Bayesian Manifold Learning for Grey Matter Morphometry: Findings from the Alzheimer’s Disease Neuroimaging Initiative

Medical Image Analysis ◽

10.1016/j.media.2021.102123 ◽

2021 ◽

pp. 102123

Author(s):

Yonghui Fan ◽

Gang Wang ◽

Qunxi Dong ◽

Yuxiang Liu ◽

Natasha Lepore ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Manifold Learning ◽

Grey Matter ◽

Spectral Feature ◽

Feature Based

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Detection of Alzheimer’s Disease-Related Cognitive Change With the Mobile Cognitive App Performance Platform

Innovation in Aging ◽

10.1093/geroni/igaa057.2907 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

pp. 801-801

Author(s):

Dawn Mechanic-Hamilton ◽

Sean Lydon ◽

Alexander Miller ◽

Kimberly Halberstadter ◽

Jacqueline Lane ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Memory Load ◽

Memory Task ◽

Structural Mri ◽

Cognitive Change ◽

Difficulty Level ◽

Trial Duration ◽

Parahippocampal Cortex ◽

Relationship Of

Abstract This study investigates the psychometric properties of the mobile cognitive app performance platform (mCAPP), designed to detect memory changes associated with preclinical Alzheimer’s Disease (AD). The mCAPP memory task includes learning and matching hidden card pairs and incorporates increasing memory load, pattern separation features, and spatial memory. Participants included 30 older adults with normal cognition. They completed the mCAPP, paper and pencil neuropsychological tests and a subset completed a high-resolution structural MRI. The majority of participants found the difficulty level of the mCAPP game to be “just right”. Accuracy on the mCAPP correlated with performance on memory and executive measures, while speed of performance on the mCAPP correlated with performance on attention and executive function measures. Longer trial duration correlated with measures of the parahippocampal cortex. The relationship of mCAPP variables with molecular biomarkers, at-home and burst testing, and development of additional cognitive measures will also be discussed.

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