scholarly journals Evaluation of the novel 18 F‐labeled PET tracer SMBT‐1 for imaging astrogliosis in healthy elderly controls and A+/T+/(N+) Alzheimer's disease patients

2020 ◽  
Vol 16 (S4) ◽  
Author(s):  
Victor LL Villemagne ◽  
Ryuichi Harada ◽  
Vincent Dore ◽  
Shozo Furumoto ◽  
Rachel S Mulligan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
The Novel ◽  
Healthy Elderly ◽  
Pet Tracer

Allocentric to Egocentric Spatial Switching: Impairment in aMCI and Alzheimer's Disease Patients?

2018 ◽  
Vol 15 (3) ◽  
pp. 229-236 ◽  
Author(s):  
Gennaro Ruggiero ◽  
Alessandro Iavarone ◽  
Tina Iachini
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Reference Frame ◽  
Reference Frames ◽  
Memory Task ◽  
Spatial Representations ◽  
The Novel ◽  
Spatial Memory Task ◽  
Switching Condition ◽  
Normal Controls

Objective: Deficits in egocentric (subject-to-object) and allocentric (object-to-object) spatial representations, with a mainly allocentric impairment, characterize the first stages of the Alzheimer's disease (AD). Methods: To identify early cognitive signs of AD conversion, some studies focused on amnestic-Mild Cognitive Impairment (aMCI) by reporting alterations in both reference frames, especially the allocentric ones. However, spatial environments in which we move need the cooperation of both reference frames. Such cooperating processes imply that we constantly switch from allocentric to egocentric frames and vice versa. This raises the question of whether alterations of switching abilities might also characterize an early cognitive marker of AD, potentially suitable to detect the conversion from aMCI to dementia. Here, we compared AD and aMCI patients with Normal Controls (NC) on the Ego-Allo- Switching spatial memory task. The task assessed the capacity to use switching (Ego-Allo, Allo-Ego) and non-switching (Ego-Ego, Allo-Allo) verbal judgments about relative distances between memorized stimuli. Results: The novel finding of this study is the neat impairment shown by aMCI and AD in switching from allocentric to egocentric reference frames. Interestingly, in aMCI when the first reference frame was egocentric, the allocentric deficit appeared attenuated. Conclusion: This led us to conclude that allocentric deficits are not always clinically detectable in aMCI since the impairments could be masked when the first reference frame was body-centred. Alongside, AD and aMCI also revealed allocentric deficits in the non-switching condition. These findings suggest that switching alterations would emerge from impairments in hippocampal and posteromedial areas and from concurrent dysregulations in the locus coeruleus-noradrenaline system or pre-frontal cortex.

Reproducibility of Brain Volume Changes in Longitudinal Voxel-Based Morphometry Between Non-Accelerated and Accelerated Magnetic Resonance Imaging

2021 ◽  
pp. 1-10
Author(s):  
Hidemasa Takao ◽  
Shiori Amemiya ◽  
Osamu Abe ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Gray Matter ◽  
Gray Matter Volume ◽  
Elderly Subjects ◽  
Voxel Based Morphometry ◽  
Volume Changes ◽  
Matter Volume ◽  
Healthy Elderly

Background: Scan acceleration techniques, such as parallel imaging, can reduce scan times, but reliability is essential to implement these techniques in neuroimaging. Objective: To evaluate the reproducibility of the longitudinal changes in brain morphology determined by longitudinal voxel-based morphometry (VBM) between non-accelerated and accelerated magnetic resonance images (MRI) in normal aging, mild cognitive impairment (MCI), and Alzheimer’s disease (AD). Methods: Using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) 2 database, comprising subjects who underwent non-accelerated and accelerated structural T1-weighted MRI at screening and at a 2-year follow-up on 3.0 T Philips scanners, we examined the reproducibility of longitudinal gray matter volume changes determined by longitudinal VBM processing between non-accelerated and accelerated imaging in 50 healthy elderly subjects, 54 MCI patients, and eight AD patients. Results: The intraclass correlation coefficient (ICC) maps differed among the three groups. The mean ICC was 0.72 overall (healthy elderly, 0.63; MCI, 0.75; AD, 0.63), and the ICC was good to excellent (0.6–1.0) for 81.4%of voxels (healthy elderly, 64.8%; MCI, 85.0%; AD, 65.0%). The differences in image quality (head motion) were not significant (Kruskal–Wallis test, p = 0.18) and the within-subject standard deviations of longitudinal gray matter volume changes were similar among the groups. Conclusion: The results indicate that the reproducibility of longitudinal gray matter volume changes determined by VBM between non-accelerated and accelerated MRI is good to excellent for many regions but may vary between diseases and regions.

scholarly journals Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

2021 ◽  
Author(s):  
Antonio Giovannetti ◽  
Gianluca Susi ◽  
Paola Casti ◽  
Arianna Mencattini ◽  
Sandra Pusil ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Regions ◽  
Ensemble Classification ◽  
The Novel ◽  
Ensemble Classifiers ◽  
Deep Convolutional Neural Networks ◽  
Early Signs ◽  
Data Representations ◽  
The Brain

AbstractIn this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble classifiers based on deep convolutional neural networks. For the scope of predicting the early signs of Alzheimer’s disease (AD), functional connectivity (FC) measures between the brain bio-magnetic signals originated from spatially separated brain regions are used as MEG data representations for the analysis. After stacking the FC indicators relative to different frequency bands into multiple images, a deep transfer learning model is used to extract different sets of deep features and to derive improved classification ensembles. The proposed Deep-MEG architectures were tested on a set of resting-state MEG recordings and their corresponding magnetic resonance imaging scans, from a longitudinal study involving 87 subjects. Accuracy values of 89% and 87% were obtained, respectively, for the early prediction of AD conversion in a sample of 54 mild cognitive impairment subjects and in a sample of 87 subjects, including 33 healthy controls. These results indicate that the proposed Deep-MEG approach is a powerful tool for detecting early alterations in the spectral–temporal connectivity profiles and in their spatial relationships.

An Informant Questionnaire for Detecting Alzheimer's Disease: Are Some Items Better Than Others?

2011 ◽  
Vol 17 (4) ◽  
pp. 674-681 ◽  
Author(s):  
Sietske A.M. Sikkes ◽  
Dirk L. Knol ◽  
Mark T. van den Berg ◽  
Elly S.M. de Lange-de Klerk ◽  
Philip Scheltens ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Equation ◽  
Structural Model ◽  
The Elderly ◽  
Equation Modeling ◽  
Or Education ◽  
Healthy Elderly ◽  
Patient Groups ◽  
Better Than

AbstractA decline in everyday cognitive functioning is important for diagnosing dementia. Informant questionnaires, such as the informant questionnaire on cognitive decline in the elderly (IQCODE), are used to measure this. Previously, conflicting results on the IQCODEs ability to discriminate between Alzheimer's disease (AD), mild cognitive impairment (MCI), and cognitively healthy elderly were found. We aim to investigate whether specific groups of items are more useful than others in discriminating between these patient groups. Informants of 180 AD, 59 MCI, and 89 patients with subjective memory complaints (SMC) completed the IQCODE. To investigate the grouping of questionnaire items, we used a two-dimensional graded response model (GRM).The association between IQCODE, age, gender, education, and diagnosis was modeled using structural equation modeling. The GRM with two groups of items fitted better than the unidimensional model. However, the high correlation between the dimensions (r=.90) suggested unidimensionality. The structural model showed that the IQCODE was able to differentiate between all patient groups. The IQCODE can be considered as unidimensional and as a useful addition to diagnostic screening in a memory clinic setting, as it was able to distinguish between AD, MCI, and SMC and was not influenced by gender or education. (JINS, 2011, 17, 674–681)

scholarly journals Smell and taste recognition in early stages of late-onset Alzheimer’s disease

SURG Journal ◽  
2010 ◽  
Vol 4 (1) ◽  
pp. 57-64 ◽  
Author(s):  
Nila Ilhamto ◽  
Lisa M Duizer
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Health Status ◽  
Healthy Aging ◽  
Late Onset ◽  
Word Recall ◽  
Early Stages ◽  
Predictive Variables ◽  
Healthy Elderly ◽  
Inadequate Nutrition

Problems of inadequate nutrition and energy intake are common in the aging population. Smell and taste deficits associated with Late-Onset Alzheimer’s Disease (LOAD) may accentuate the decline in nutritional status of elderly individuals and indirectly enhance progression of cognitive problems in LOAD. The objective of this study was to explore and characterize smell and taste recognition abilities in early stages of LOAD, beyond that of normal healthy aging. A total of 29 healthy-younger subjects aged 18-40 (HY), 13 healthy-elderly (HA) and six elderly adults diagnosed with LOAD (AD) aged 60-85, were recruited from the Guelph community. The Sniffin’ Sticks Screening Test (SSST) and Taste Strips were used to test olfactory and gustatory functions, respectively. Participants also completed the mini-mental state examination (MMSE), clock test and word recall tests to assess cognitive/memory skills. Compared to HA individuals, people with AD had significant odour recognition impairment. Correlation analysis also revealed an age-associated decline in overall taste ability. When specific tastes were examined, impairments in sour and bitter identification were observed with increasing age. However, no significant differences in specific taste abilities were found between HA and AD individuals. In predicting health status (ie. presence or absence of LOAD), an assessment of all variables in this study was conducted using Generalized Linear Model (GLM). Results showed that sweet recognition and clock test scores were the best predictive variables of health status. However, this is a preliminary model that needs refinement through further research using more individuals.

Hippocampal Metabolic Subregions and Networks: Behavioral, Molecular and Pathological Aging Profiles

2021 ◽  
Author(s):  
Somayeh Maleki Balajoo ◽  
Simon B. Eickhoff ◽  
Shahrzad Kharabian Masouleh ◽  
Anna Plachti ◽  
Laura Waite ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Metabolic Networks ◽  
Cortical Atrophy ◽  
Healthy Elderly ◽  
Cornu Ammonis ◽  
Data Driven Approach ◽  
Elderly Cohort ◽  
Individual Profiles ◽  
Local Metabolism

Abstract Purpose: Hippocampal dysfunction happens across many neuropsychiatric disorders and is the hallmark of Alzheimer’s disease with evidenced metabolic alterations. However, while metabolic changes are a key aspect of Alzheimer’s disease, hippocampal metabolic networks, as defined by metabolic covariance, haven’t been identified in healthy populations. As the hippocampus portrays cytoarchitectural, connectional, and functional heterogeneity, heterogeneous patterns of metabolic covariance could be expected. Methods: We first characterized this heterogeneity with a data-driven approach by identifying the spatial pattern of hippocampus differentiation based on metabolic covariance with the rest of the brain in FDG-PET data of large healthy elderly cohort (n=362). Then, we characterized the metabolic networks of the robustly defined subregions. In the following, we characterized the disentangled hippocampal metabolic networks with regards to behavioral and neurotransmitter systems using quantitative decoding. Finally, we examined how the local metabolism in the hippocampal subregions is influenced by Alzheimer’s disease pathology in a cohort of ADNI participants (n = 580). Results: Based on hippocampal-brain metabolic covariance in a healthy elderly cohort, we found a differentiation into primarily anterior vs. posterior and secondarily Cornu Ammonis (CA) vs. subiculum subregions. Characterizing the associated metabolic networks revealed that the anterior-subiculum network including temporal-pole and orbitofrontal regions relates to self, motivation and mentalizing behavior and is influenced by dopaminergic systems. In contrast, the posterior-subiculum shows a wide cortical network engaged in action- and world-oriented cognition targeted by serotoninergic systems. The anterior- and posterior-CA, connected respectively to amygdala and broader subcortical networks, are associated to several transporters release. Local metabolism comparison between Alzheimer’s disease-related diagnosis groups revealed early CA’s alterations while posterior subicular alterations appear at advanced stages in line with broader cortical atrophy and behavioral dysfunctions.Conclusion: Future studies should delineate patients’ individual profiles according to hippocampal subregions and networks.

Sign in / Sign up

Export Citation Format

Share Document