scholarly journals Effects of Moderate Intensity Exercise on the Cortical Thickness and Subcortical Volumes of Preclinical Alzheimer’s Disease Patients: A Pilot Study

2020 ◽  
Vol 17 (6) ◽  
pp. 613-619
Author(s):  
Yoo Hyun Um ◽  
Sheng-Min Wang ◽  
Nak-Young Kim ◽  
Dong Woo Kang ◽  
Hae-Ran Na ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Cortical Thickness ◽  
Exercise Group ◽  
Moderate Intensity ◽  
Preclinical Alzheimer’S Disease ◽  
Moderate Intensity Exercise ◽  
Preclinical Ad ◽  
Subcortical Volumes

Objective We aimed to explore the impact of moderate intensity exercise on the cortical thickness and subcortical volumes of preclinical Alzheimer’s disease (AD) patients.Methods Sixty-three preclinical AD patients with magnetic resonance imaging (MRI) and 18-florbetaben positron emission tomography (PET) data were enrolled in the study. Information on demographic characteristics, cognitive battery scores, self-reported exercise habits were attained. Structural magnetic resonance images were analyzed and processed using Freesurfer v6.0.Results Compared to Exercise group, Non-Exercise group demonstrated reduced cortical thickness in left parstriangularis, rostral middle frontal, entorhinal, superior frontal, lingual, superior parietal, lateral occipital, inferior parietal gyrus, temporal pole, precuneus, insula, fusiform gyrus, right precuneus, superiorparietal, lateral orbitofrontal, rostral middle frontal, medial orbitofrontal, superior frontal, lingual, middle temporal gyrus, insula, supramarginal, parahippocampal, paracentral gyrus. Volumes of right thalamus, caudate, putamen, pallidum, hippocampus, amygdala were also reduced in Non-Exercise group.Conclusion Moderate intensity exercise affects cortical and subcortical structures in preclinical AD patients. Thus, physical exercise has a potential to be an effective intervention to prevent future cognitive decline in those at high risk of AD.

scholarly journals Disease progression modelling from preclinical Alzheimer’s disease (AD) to AD dementia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Soo Hyun Cho ◽  
Sookyoung Woo ◽  
Changsoo Kim ◽  
Hee Jin Kim ◽  
Hyemin Jang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Disease Assessment ◽  
Time Interval ◽  
Disease Course ◽  
Preclinical Alzheimer’S Disease ◽  
Apoe Ε4 ◽  
Preclinical Ad ◽  
Cognitive Subscale

AbstractTo characterize the course of Alzheimer’s disease (AD) over a longer time interval, we aimed to construct a disease course model for the entire span of the disease using two separate cohorts ranging from preclinical AD to AD dementia. We modelled the progression course of 436 patients with AD continuum and investigated the effects of apolipoprotein E ε4 (APOE ε4) and sex on disease progression. To develop a model of progression from preclinical AD to AD dementia, we estimated Alzheimer’s Disease Assessment Scale-Cognitive Subscale 13 (ADAS-cog 13) scores. When calculated as the median of ADAS-cog 13 scores for each cohort, the estimated time from preclinical AD to MCI due to AD was 7.8 years and preclinical AD to AD dementia was 15.2 years. ADAS-cog 13 scores deteriorated most rapidly in women APOE ε4 carriers and most slowly in men APOE ε4 non-carriers (p < 0.001). Our results suggest that disease progression modelling from preclinical AD to AD dementia may help clinicians to estimate where patients are in the disease course and provide information on variation in the disease course by sex and APOE ε4 status.

scholarly journals Cognitive Performance and Cerebrospinal Fluid Markers in Preclinical Alzheimer’s Disease: Results from the Gothenburg H70 Birth Cohort Studies

2021 ◽  
Vol 79 (1) ◽  
pp. 225-235
Author(s):  
Maya Arvidsson Rådestig ◽  
Johan Skoog ◽  
Henrik Zetterberg ◽  
Jürgen Kern ◽  
Anna Zettergren ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Cohort Studies ◽  
Cognitive Performance ◽  
Birth Cohort ◽  
Population Based ◽  
Tau Pathology ◽  
Preclinical Alzheimer’S Disease ◽  
Preclinical Ad

Background: We have previously shown that older adults with preclinical Alzheimer’s disease (AD) pathology in cerebrospinal fluid (CSF) had slightly worse performance in Mini-Mental State Examination (MMSE) than participants without preclinical AD pathology. Objective: We therefore aimed to compare performance on neurocognitive tests in a population-based sample of 70-year-olds with and without CSF AD pathology. Methods: The sample was derived from the population-based Gothenburg H70 Birth Cohort Studies in Sweden. Participants (n = 316, 70 years old) underwent comprehensive cognitive examinations, and CSF Aβ-42, Aβ-40, T-tau, and P-tau concentrations were measured. Participants were classified according to the ATN system, and according to their Clinical Dementia Rating (CDR) score. Cognitive performance was examined in the CSF amyloid, tau, and neurodegeneration (ATN) categories. Results: Among participants with CDR 0 (n = 259), those with amyloid (A+) and/or tau pathology (T+, N+) showed similar performance on most cognitive tests compared to participants with A-T-N-. Participants with A-T-N+ performed worse in memory (Supra span (p = 0.003), object Delayed (p = 0.042) and Immediate recall (p = 0.033)). Among participants with CDR 0.5 (n = 57), those with amyloid pathology (A+) scored worse in category fluency (p = 0.003). Conclusion: Cognitively normal participants with amyloid and/or tau pathology performed similarly to those without any biomarker evidence of preclinical AD in most cognitive domains, with the exception of slightly poorer memory performance in A-T-N+. Our study suggests that preclinical AD biomarkers are altered before cognitive decline.

scholarly journals Surface-based analysis of cortical thickness and volume loss in Alzheimer’s disease

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Emily Iannopollo ◽  
Ryan Plunkett ◽  
Kara Garcia
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Cortical Thickness ◽  
Surface Matching ◽  
Cortical Surface ◽  
Volume Loss ◽  
Cortical Volume ◽  
Human Connectome Project ◽  
And Control

Background and Hypothesis: Magnetic resonance imaging (MRI) has become a useful tool in monitoring the progression of Alzheimer's disease. Previous surface-based analysis has focused on changes in cortical thickness associated with the disease1. The objective of this study is to analyze MRI-derived cortical reconstructions for patterns of atrophy in terms of both cortical thickness and cortical volume. We hypothesize that Alzheimer’s Disease progression will be associated with a more significant change in volume than thickness. Experimental Design or Project Methods: MRI data was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). All subjects with baseline and two-year 3T MRI scans were included. Segmentation of MRIs into gray and white matter was performed with FreeSurfer2,3,4,5. Subjects whose scans did not segment accurately were excluded. Surfaces were then registered to a common atlas with Ciftify6, and anatomically-constrained Multimodal Surface Matching (aMSM) was used to analyze longitudinal changes in each subject7. This produced continuous surface maps showing changes in cortical surface area and thickness. These maps were multiplied to create cortical volume maps8. Permutation Analysis of Linear Models (PALM) was used to perform two-sample t-tests comparing the maps of the Alzheimer’s and control groups9. Results: Preliminary analysis of nine Alzheimer’s subjects and nine control subjects produced surface maps displaying patterns that were expected given previous research findings10,11. There was increased volume and thickness loss in Alzheimer’s subjects relative to controls, with relatively high loss in structures of the medial temporal lobe. Future analysis of a larger sample will determine whether statistically significant differences exist between the Alzheimer’s and control groups in terms of thickness loss and volume loss. Conclusion and Potential Impact: If significant results are found, surface-based analysis of cortical volume may allow for detection of atrophy at an earlier stage in disease progression than would be possible based on cortical thickness.   References 1. Clarkson MJ, Cardoso MJ, Ridgway GR, Modat M, Leung KK, Rohrer JD, Fox NC, Ourselin S. A comparison of voxel and surface based cortical thickness estimation methods. NeuroImage. 2011 Aug 1; 57(3):856-65. 2. Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage. 1999;9:179194. 3. Fischl B, Sereno M, Dale A. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage. 1999;9:195–207.  4. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 2002;33:341-355. 5. Fischl B, Salat DH, van der Kouwe AJ, Makris N, Segonne F, Quinn BT, Dale AM. Sequence-independent segmentation of magnetic resonance images. Neuroimage 2004;23 Suppl 1:S69-84. 6. Glasser MF, Sotiropoulos SN, Wilson JA, Coalson TS, Fischl B, Andersson JL, Xu J, Jbabdi S, Webster M, Polimeni JR, Van Essen DC, Jenkinson M, WU-Minn HCP Consortium. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage. 2013 Oct 15;80:105-24. 7. Robinson EC, Garcia K, Glasser MF, Chen Z, Coalson TS, Makropoulos A, Bozek J, Wright R, Schuh A, Webster M, Hutter J, Price A, Cordero Grande L, Hughes E, Tusor N, Bayly PV, Van Essen DC, Smith SM, Edwards AD, Hajnal J, Jenkinson M, Glocker B, Rueckert D. Multimodal surface matching with higher-order smoothness constraints. Neuroimage. 2018;167:453-65. 8. Marcus DS, Harwell J, Olsen T, Hodge M, Glasser MF, Prior F, Jenkinson M, Laumann T, Curtiss SW, Van Essen DC. Informatics and data mining tools and strategies for the human connectome project. Front Neuroinform 2011;5:4. 9. Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE. Permutation inference for the general linear model. NeuroImage, 2014;92:381-397 10. Matsuda, H. MRI morphometry in Alzheimer’s disease. Ageing Research Reviews. 2016 Sep;30:17-24. 11. Risacher SL, Shen L, West JD, Kim S, McDonald BC, Beckett LA, Harvey DJ, Jack CR Jr, Weiner MW, Saykin AJ. Alzheimer's Disease Neuroimaging Initiative (ADNI). Longitudinal MRI atrophy biomarkers: relationship to conversion in the ADNI cohort. Neurobiol Aging. 2010 Aug;31(8):1401-18. 

scholarly journals Significance of Normalization on Anatomical MRI Measures in Predicting Alzheimer’s Disease

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Qi Zhou ◽  
Mohammed Goryawala ◽  
Mercedes Cabrerizo ◽  
Warren Barker ◽  
Ranjan Duara ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Surface Area ◽  
Cortical Thickness ◽  
Feature Selection Method ◽  
Support Vector ◽  
Intracranial Volume ◽  
Single Measure ◽  
Subcortical Volumes ◽  
Subcortical Volume

This study establishes a new approach for combining neuroimaging and neuropsychological measures for an optimal decisional space to classify subjects with Alzheimer’s disease (AD). This approach relies on a multivariate feature selection method with different MRI normalization techniques. Subcortical volume, cortical thickness, and surface area measures are obtained using MRIs from 189 participants (129 normal controls and 60 AD patients). Statistically significant variables were selected for each combination model to construct a multidimensional space for classification. Different normalization approaches were explored to gauge the effect on classification performance using a support vector machine classifier. Results indicate that the Mini-mental state examination (MMSE) measure is most discriminative among single-measure models, while subcortical volume combined with MMSE is the most effective multivariate model for AD classification. The study demonstrates that subcortical volumes need not be normalized, whereas cortical thickness should be normalized either by intracranial volume or mean thickness, and surface area is a weak indicator of AD with and without normalization. On the significant brain regions, a nearly perfect symmetry is observed for subcortical volumes and cortical thickness, and a significant reduction in thickness is particularly seen in the temporal lobe, which is associated with brain deficits characterizing 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 Mechanistic Effects of Aerobic Exercise in Alzheimer's Disease: Imaging Findings From the Pilot FIT-AD Trial

2021 ◽  
Vol 13 ◽  
Author(s):  
Fang Yu ◽  
Michelle A. Mathiason ◽  
SeungYong Han ◽  
Jeffrey L. Gunter ◽  
David Jones ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Aerobic Exercise ◽  
Cortical Thickness ◽  
Controlled Trial ◽  
Hippocampal Volume ◽  
Community Dwelling ◽  
White Matter Hyperintensity ◽  
Moderate Intensity ◽  
Intervention Period

Despite strong evidence from animal models of Alzheimer's disease (AD) supporting aerobic exercise as a disease-modifying treatment for AD, human mechanistic studies are limited with mixed findings. The objective of this pilot randomized controlled trial was to examine the effects of 6-month aerobic exercise on hippocampal volume, temporal meta-regions of interest (ROI) cortical thickness, white matter hyperintensity (WMH) volume, and network failure quotient (NFQ), measured with MRI, in community-dwelling older adults with AD dementia. Additionally, the relationships between 6- and 12-month changes in MRI biomarkers and the AD Assessment Scale-Cognition (ADAS-Cog) were examined. Sixty participants were randomized, but one was excluded because baseline MRI failed quality control: 38 randomized to cycling and 21 to stretching. The intervention was moderate-intensity cycling for 20–50 mins, three times a week for 6 months. Control was low-intensity stretching. The study outcomes include hippocampal volume, temporal meta-ROI cortical thickness, WMH volume, and NFQ. Outcomes were measured at baseline, 6 months, and 12 months. The sample averaged 77.3 ± 6.3 years old with 15.6 ± 2.9 years of education and 53% men. Both groups experienced significant declines over 6 months in hippocampal volume (2.64% in cycling vs. 2.89% in stretching) and temporal meta-ROI cortical thickness (0.94 vs. 1.54%), and over 12 months in hippocampal volume (4.47 vs. 3.84%) and temporal meta-ROI cortical thickness (2.27 vs. 1.79%). These declines did not differ between groups. WMH volume increased significantly with the cycling group increasing less (10.9%) than stretching (24.5%) over 6 months (f = 4.47, p = 0.04) and over 12 months (12.1 vs. 27.6%, f = 5.88, p = 0.02). NFQ did not change significantly over time. Pairwise correlational analyses showed a significant negative correlation between 6-month changes in hippocampal volume and ADAS-Cog (r = −0.34, p &lt; 0.05). To conclude, aerobic exercise may reduce the decline in hippocampal volume and temporal meta-ROI cortical thickness during the intervention period, but the effect sizes are likely to be very small and dose-dependent and reverse once the intervention stops. Aerobic exercise is effective on slowing down WMH progression but has no effect on NFQ. Hippocampal atrophy was associated with cognitive decline during the intervention period.Clinical Trial Registration:www.ClinicalTrials.gov, identifier: NCT01954550.

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.

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