1,722,830 research outputs found
Accelerated atrophy in dopaminergic targets and medial temporo-parietal regions precedes the onset of delusions in patients with Alzheimer’s disease
Full text linkAdditional information: *Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.Supplementary Information: Below is the link to the electronic supplementary material. Supplementary file1 available at https://static-content.springer.com/esm/art%3A10.1007%2Fs00406-022-01417-5/MediaObjects/406_2022_1417_MOESM1_ESM.docx (DOCX 21 KB).Copyright © 2022 The Author(s). People with Alzheimer’s disease (AD) and delusions have worse quality of life and prognosis. However, early markers of delusions have not been identified yet. The present study investigated whether there are any detectable differences in grey matter (GM) volume and cognitive changes in the year before symptom onset between patients with AD who did and did not develop delusions. Two matched samples of AD patients, 63 who did (PT-D) and 63 who did not develop delusions (PT-ND) over 1 year, were identified from the Alzheimer’s Disease Neuroimaging Initiative database. The Neuropsychiatric Inventory (NPI) was used to assess the presence of delusions. Sixty-three additional matched healthy controls (HC) were selected. Repeated-measures ANCOVA models were used to investigate group-by-time effects on the volume of selected GM regions of interest and on cognitive performance. No neurocognitive differences were observed between patient groups prior to symptom onset. Greater episodic memory decline and GM loss in bilateral caudate nuclei, medio-temporal and midline cingulo-parietal regions were found in the PT-D compared with the PT-ND group. A pattern of faster GM loss in brain areas typically affected by AD and in cortical and subcortical targets of dopaminergic pathways, paralleled by worsening of episodic memory and behavioural symptoms, may explain the emergence of delusions in patients with AD.Not applicable. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson and Johnson Pharmaceutical Research and Development LLC.; Lumosity; Lundbeck; Merck and Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. RM is supported by a Brunel University London research fellowship. JMVB is funded by a scholarship by the Consejo Nacional de Ciencia y Tecnología (CONACYT), Mexico
Imputation-Based Variable Selection Method for Block-Wise Missing Data When Integrating Multiple Longitudinal Studies
No full textWhen integrating data from multiple sources, a common challenge is block-wise missing. Most existing methods address this issue only in cross-sectional studies. In this paper, we propose a method for variable selection when combining datasets from multiple sources in longitudinal studies. To account for block-wise missing in covariates, we impute the missing values multiple times based on combinations of samples from different missing pattern and predictors from different data sources. We then use these imputed data to construct estimating equations, and aggregate the information across subjects and sources with the generalized method of moments. We employ the smoothly clipped absolute deviation penalty in variable selection and use the extended Bayesian Information Criterion criteria for tuning parameter selection. We establish the asymptotic properties of the proposed estimator, and demonstrate the superior performance of the proposed method through numerical experiments. Furthermore, we apply the proposed method in the Alzheimer’s Disease Neuroimaging Initiative study to identify sensitive early-stage biomarkers of Alzheimer’s Disease, which is crucial for early disease detection and personalized treatment
Polygenic Risk Scoring is an Effective Approach to Predict Those Individuals Most Likely to Decline Cognitively Due to Alzheimer’s Disease
Full text linkThis is the final version. Available on open access from Springer Nature via the DOI in this recordBACKGROUND: There is a clear need for simple and effective tests to identify individuals
who are most likely to develop Alzheimer’s Disease (AD) both for the purposes of clinical
trial recruitment but also for improved management of patients who may be experiencing
early pre-clinical symptoms or who have clinical concerns.
OBJECTIVES: To predict individuals at greatest risk of progression of cognitive
impairment due to Alzheimer’s Disease in individuals from the Alzheimer’s Disease
Neuroimaging Initiative (ADNI) using a polygenic risk scoring algorithm. To compare the
performance of a PRS algorithm in predicting cognitive decline against that of using the
pTau/Aẞ1-42 ratio CSF biomarker profile.
DESIGN: A longitudinal analysis of data from the Alzheimer’s Disease Neuroimaging
Initiative study conducted across over 50 sites in the US and Canada
SETTING: Multi-center genetics study
PARTICPANTS: 515 subjects who upon entry to the study were diagnosed as cognitively
normal or with mild cognitive impairment
MEASUREMENTS: Use of genotyping and/or whole genome sequencing data to calculate
polygenic risk scores and assess ability to predict subsequent cognitive decline as measured
by CDR-SB and ADAS-Cog13 over 4 years
RESULTS: The overall performance for predicting those individuals who would decline by
at least 15 ADAS-Cog13 points from a baseline mild cognitive impairment in 4 years was
72.8% (CI:67.9-77.7) AUC increasing to 79.1% (CI: 75.6-82.6) when also including
cognitively normal participants. Assessing mild cognitive impaired subjects only and using a
threshold of greater than 0.6, the high genetic risk participant group declined, on average, by
1.4 points (CDR-SB) more than the low risk group over 4 years. The performance of the
PRS algorithm tested was similar to that of the pTau/Aẞ1-42 ratio CSF biomarker profile in
predicting cognitive decline.
CONCLUSION: Calculating polygenic risk scores offers a simple and effective way, using
DNA extracted from a simple mouth swab, to select mild cognitively impaired patients who
are most likely to decline cognitively over the next four yearsNational Institutes of Health (NIH)Department of DefenseInnovate U
The Influence of birth cohorts on future cognitive decline
No full textBackground: Slowed rates of cognitive decline have been reported in individuals with higher cognitive reserve (CR), but interindividual discrepancies remain unexplained. Few studies have reported a birth cohort effect, favoring later-born individuals, but these studies remain scarce. Objective: We aimed to predict cognitive decline in older adults using birth cohorts and CR. Methods:Within the Alzheimer’s Disease Neuroimaging Initiative, 1,041 dementia-free participants were assessed on four cognitive domains (verbal episodic memory; language and semantic memory; attention; executive functions) at each follow-up visit up to 14 years. Four birth cohorts were formed according to the major historical events of the 20th century (1916–1928; 1929–1938; 1939–1945; 1946–1962). CR was operationalized by merging education, complexity of occupation, and verbal IQ. We used linear mixed-effect models to evaluate the effects of CR and birth cohorts on rate of performance change over time. Age at baseline, baseline structural brain health (total brain and total white matter hyperintensities volumes), and baseline vascular risk factors burden were used as covariates. Results: CR was only associated with slower decline in verbal episodic memory. However, more recent birth cohorts predicted slower annual cognitive decline in all domains, except for executive functions. This effect increased as the birth cohort became more recent. Conclusion: We found that both CR and birth cohorts influence future cognitive decline, which has strong public policy implications
Predicting brain atrophy from tau pathology: a summary of clinical findings and their translation into personalized models
Full text linkFor more than 25 years, the amyloid hypothesis–the paradigm that amyloid is the primary cause of Alzheimer’s disease–has dominated the Alzheimer’s community. Now, increasing evidence suggests that tissue atrophy and cognitive decline in Alzheimer’s disease are more closely linked to the amount and location of misfolded tau protein than to amyloid plaques. However, the precise correlation between tau pathology and tissue atrophy remains unknown. Here we integrate multiphysics modeling and Bayesian inference to create personalized tau-atrophy models using longitudinal clinical images from the Alzheimer’s Disease Neuroimaging Initiative. For each subject, we infer three personalized parameters, the tau misfolding rate, the tau transport coefficient, and the tau-induced atrophy rate from four consecutive annual tau positron emission tomography scans and structural magnetic resonance images. Strikingly, the tau-induced atrophy coefficient of 0.13/year (95% CI: 0.097-0.189) was fairly consistent across all subjects suggesting a strong correlation between tau pathology and tissue atrophy. Our personalized whole brain atrophy rates of 0.68-1.68%/year (95% CI: 0.5-2.0) are elevated compared to healthy subjects and agree well with the atrophy rates of 1-3%/year reported for Alzheimer’s patients in the literature. Once comprehensively calibrated with a larger set of longitudinal images, our model has the potential to serve as a diagnostic and predictive tool to estimate future atrophy progression from clinical tau images on a personalized basis
Association of the fibronectin type III domain–containing protein 5 rs1746661 single nucleotide polymorphism with reduced brain glucose metabolism in elderly humans
Full text linkFibronectin type III domain–containing protein 5 (FNDC5) and its derived hormone, irisin, have been associated with metabolic control in humans, with described FNDC5 single nucleotide polymorphisms being linked to obesity and metabolic syndrome. Decreased brain FNDC5/irisin has been reported in subjects with dementia due to Alzheimer’s disease. Since impaired brain glucose metabolism develops in ageing and is prominent in Alzheimer’s disease, here, we examined associations of a single nucleotide polymorphism in the FNDC5 gene (rs1746661) with brain glucose metabolism and amyloid-β deposition in a cohort of 240 cognitively unimpaired and 485 cognitively impaired elderly individuals from the Alzheimer’s Disease Neuroimaging Initiative. In cognitively unimpaired elderly individuals harbouring the FNDC5 rs1746661(T) allele, we observed a regional reduction in low glucose metabolism in memory-linked brain regions and increased brain amyloid-β PET load. No differences in cognition or levels of cerebrospinal fluid amyloid-β42, phosphorylated tau and total tau were observed between FNDC5 rs1746661(T) allele carriers and non-carriers. Our results indicate that a genetic variant of FNDC5 is associated with low brain glucose metabolism in elderly individuals and suggest that FNDC5 may participate in the regulation of brain metabolism in brain regions vulnerable to Alzheimer’s disease pathophysiology. Understanding the associations between genetic variants in metabolism-linked genes and metabolic brain signatures may contribute to elucidating genetic modulators of brain metabolism in humans
Dynamic amyloid and metabolic signatures of delayed recall performance within the clinical spectrum of Alzheimer’s disease
Full text linkAssociations between pathophysiological events and cognitive measures provide insights regarding brain networks affected during the clinical progression of Alzheimer’s disease (AD). In this study, we assessed patients’ scores in two delayed episodic memory tests, and investigated their associations with regional amyloid deposition and brain metabolism across the clinical spectrum of AD. We assessed the clinical, neuropsychological, structural, and positron emission tomography (PET) baseline measures of participants from the Alzheimer’s Disease Neuroimaging Initiative. Subjects were classified as cognitively normal (CN), or with early (EMCI) or late (LMCI) mild cognitive impairment, or AD dementia. The memory outcome measures of interest were logical memory 30 min delayed recall (LM30) and Rey Auditory Verbal Learning Test 30 min delayed recall (RAVLT30). Voxel-based [18F]florbetapir and [18F]FDG uptake-ratio maps were constructed and correlations between PET images and cognitive scores were calculated. We found that EMCI individuals had LM30 scores negatively correlated with [18F]florbetapir uptake on the right parieto-occipital region. LMCI individuals had LM30 scores positively associated with left lateral temporal lobe [18F]FDG uptake, and RAVLT30 scores positively associated with [18F]FDG uptake in the left parietal lobe and in the right enthorhinal cortex. Additionally, LMCI individuals had LM30 scores negatively correlated with [18F]florbetapir uptake in the right frontal lobe. For the AD group, [18F]FDG uptake was positively correlated with LM30 in the left temporal lobe and with RAVLT30 in the right frontal lobe, and [18F]florbetapir uptake was negatively correlated with LM30 scores in the right parietal and left frontal lobes. The results show that the association between regional brain metabolism and the severity of episodic memory deficits is dependent on the clinical disease stage, suggesting a dynamic relationship between verbal episodic memory deficits, AD pathophysiology, and clinical disease stages
Item-Level Scores on the Boston Naming Test as an Independent Predictor of Perirhinal Volume in Individuals with Mild Cognitive Impairment
Full text linkData Availability Statement:
All data are publicly available at https://adni.loni.usc.edu/.Supplementary Materials:
The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/brainsci13050806/s1: Figure S1: Structural covariance maps of mediotemporal regions of interest (ROIs).Copyright © 2023 by the authors. We explored the methodological value of an item-level scoring procedure applied to the Boston Naming Test (BNT), and the extent to which this scoring approach predicts grey matter (GM) variability in regions that sustain semantic memory. Twenty-seven BNT items administered as part of the Alzheimer’s Disease Neuroimaging Initiative were scored according to their “sensorimotor interaction” (SMI) value. Quantitative scores (i.e., the count of correctly named items) and qualitative scores (i.e., the average of SMI scores for correctly named items) were used as independent predictors of neuroanatomical GM maps in two sub-cohorts of 197 healthy adults and 350 mild cognitive impairment (MCI) participants. Quantitative scores predicted clusters of temporal and mediotemporal GM in both sub-cohorts. After accounting for quantitative scores, the qualitative scores predicted mediotemporal GM clusters in the MCI sub-cohort; clusters extended to the anterior parahippocampal gyrus and encompassed the perirhinal cortex. This was confirmed by a significant yet modest association between qualitative scores and region-of-interest-informed perirhinal volumes extracted post hoc. Item-level scoring of BNT performance provides complementary information to standard quantitative scores. The concurrent use of quantitative and qualitative scores may help profile lexical–semantic access more precisely, and might help detect changes in semantic memory that are typical of early-stage Alzheimer’s disease.This research was supported by an Alzheimer’s Association Research Grant (23AARG-1030190) to MDM. MB is supported by a Fellowship award from the Alzheimer’s Society, UK (AS-JF-19a-004-517). AV is supported by funding obtained under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.3—Call for tender No. 341 of 15/03/2022 of the Italian Ministry of University and Research funded by the European Union—NextGenerationEU, Project code PE0000006, Concession Decree No. 1553 of 11/10/2022 adopted by the Italian Ministry of University and Research, CUP D93C22000930002, “A multiscale integrated approach to the study of the nervous system in health and disease” (MNESYS). Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organisation is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California
Pairwise Correlation Analysis of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Dataset Reveals Significant Feature Correlation
Full text linkThe Alzheimer’s Disease Neuroimaging Initiative (ADNI) contains extensive patient measurements (e.g., magnetic resonance imaging [MRI], biometrics, RNA expression, etc.) from Alzheimer’s disease (AD) cases and controls that have recently been used by machine learning algorithms to evaluate AD onset and progression. While using a variety of biomarkers is essential to AD research, highly correlated input features can significantly decrease machine learning model generalizability and performance. Additionally, redundant features unnecessarily increase computational time and resources necessary to train predictive models. Therefore, we used 49,288 biomarkers and 793,600 extracted MRI features to assess feature correlation within the ADNI dataset to determine the extent to which this issue might impact large scale analyses using these data. We found that 93.457% of biomarkers, 92.549% of the gene expression values, and 100% of MRI features were strongly correlated with at least one other feature in ADNI based on our Bonferroni corrected α (p-value ≤ 1.40754 × 10−13). We provide a comprehensive mapping of all ADNI biomarkers to highly correlated features within the dataset. Additionally, we show that significant correlation within the ADNI dataset should be resolved before performing bulk data analyses, and we provide recommendations to address these issues. We anticipate that these recommendations and resources will help guide researchers utilizing the ADNI dataset to increase model performance and reduce the cost and complexity of their analyses
The Impact of Insulin Resistance on Grey Matter Changes Along the Alzheimer’s Disease Continuum Insulin Resistance and Grey Matter in AD
No full textAbstract Background and Objectives Insulin resistance is emerging as a modifiable risk factor for Alzheimer’s, though its impact on grey matter volume across clinical stages remains poorly understood. The objective of the research is to investigate how insulin resistance affects grey matter integrity across the Alzheimer’s disease continuum using structural MRI. Methods Imaging, clinical, and metabolic data were extracted from 374 non-diabetic participants within the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Participants were classified as cognitively impaired (CI: n=186; 137 mild cognitive impairment, 49 early-to-moderate dementia; all AD biomarker positive) or cognitively unimpaired (CU: n=188; 122 amyloid-negative, 66 amyloid-positive). Insulin resistance was assessed at the time of MRI and clinical evaluation using the dichotomized triglyceride-glucose index (TyG). The Interactions between TyG and diagnostic group on grey matter volume were investigated using both voxel-wise and region-of-interest (ROI) based analyses, adjusted for age, sex, education, vascular risk factors, and global cognitive performance across the AD continuum. Results Insulin resistance significantly impacted gray matter volume across the AD continuum, demonstrating stage-dependent effects. In early AD disease stages, insulin resistance was associated with lower grey matter volume in fronto-parietal regions, a finding that extended to several cortical areas in CI individuals. Temporal and fronto-limbic regions were particularly highlighted by the IR-diagnosis interaction. In amyloid-positive CU individuals, IR was linked to bilateral temporal atrophy, in contrast to amyloid-negative CU participants. Discussion This study underscores the impact of insulin resistance on brain structure across the AD continuum, particularly within key vulnerability areas characteristic of AD pathology. These findings highlight the need for future research into potential therapeutic strategies targeting insulin signaling to mitigate neurodegeneration in AD
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