| 1. |
- Biel, D., et al.
(författare)
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Combining tau-PET and fMRI meta-analyses for patient-centered prediction of cognitive decline in Alzheimer's disease
- 2022
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Ingår i: Alzheimers Research & Therapy. - : Springer Science and Business Media LLC. - 1758-9193. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- Background Tau-PET is a prognostic marker for cognitive decline in Alzheimer's disease, and the heterogeneity of tau-PET patterns matches cognitive symptom heterogeneity. Thus, tau-PET may allow precision-medicine prediction of individual tau-related cognitive trajectories, which can be important for determining patient-specific cognitive endpoints in clinical trials. Here, we aimed to examine whether tau-PET in cognitive-domain-specific brain regions, identified via fMRI meta-analyses, allows the prediction of domain-specific cognitive decline. Further, we aimed to determine whether tau-PET-informed personalized cognitive composites capture patient-specific cognitive trajectories more sensitively than conventional cognitive measures. Methods We included Alzheimer's Disease Neuroimaging Initiative (ADNI) participants classified as controls (i.e., amyloid-negative, cognitively normal, n = 121) or Alzheimer's disease-spectrum (i.e., amyloid-positive, cognitively normal to dementia, n = 140), plus 111 AVID-1451-A05 participants for independent validation (controls/Alzheimer's disease-spectrum=46/65). All participants underwent baseline F-18-flortaucipir tau-PET, amyloid-PET, and longitudinal cognitive testing to assess annual cognitive changes (i.e., episodic memory, language, executive functioning, visuospatial). Cognitive changes were calculated using linear mixed models. Independent meta-analytical task-fMRI activation maps for each included cognitive domain were obtained from the Neurosynth database and applied to tau-PET to determine tau-PET signal in cognitive-domain-specific brain regions. In bootstrapped linear regression, we assessed the strength of the relationship (i.e., partial R-2) between cognitive-domain-specific tau-PET vs. global or temporal-lobe tau-PET and cognitive changes. Further, we used tau-PET-based prediction of domain-specific decline to compose personalized cognitive composites that were tailored to capture patient-specific cognitive decline. Results In both amyloid-positive cohorts (ADNI [age = 75.99 +/- 7.69] and A05 [age = 74.03 +/- 9.03]), cognitive-domain-specific tau-PET outperformed global and temporal-lobe tau-PET for predicting future cognitive decline in episodic memory, language, executive functioning, and visuospatial abilities. Further, a tau-PET-informed personalized cognitive composite across cognitive domains enhanced the sensitivity to assess cognitive decline in amyloid-positive subjects, yielding lower sample sizes required for detecting simulated intervention effects compared to conventional cognitive endpoints (i.e., memory composite, global cognitive composite). However, the latter effect was less strong in A05 compared to the ADNI cohort. Conclusion Combining tau-PET with task-fMRI-derived maps of major cognitive domains facilitates the prediction of domain-specific cognitive decline. This approach may help to increase the sensitivity to detect Alzheimer's disease-related cognitive decline and to determine personalized cognitive endpoints in clinical trials.
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| 2. |
- Biel, D., et al.
(författare)
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sTREM2 is associated with amyloid-related p-tau increases and glucose hypermetabolism in Alzheimer's disease
- 2023
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Ingår i: Embo Molecular Medicine. - : EMBO. - 1757-4676 .- 1757-4684. ; 15:2
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Tidskriftsartikel (refereegranskat)abstract
- Microglial activation occurs early in Alzheimer's disease (AD) and previous studies reported both detrimental and protective effects of microglia on AD progression. Here, we used CSF sTREM2 to investigate disease stage-dependent drivers of microglial activation and to determine downstream consequences on AD progression. We included 402 patients with measures of earliest beta-amyloid (CSF A beta(1-42)) and late-stage fibrillary A beta pathology (amyloid-PET centiloid), as well as sTREM2, p-tau(181), and FDG-PET. To determine disease stage, we stratified participants into early A beta-accumulators (A beta CSF+/PET-; n = 70) or late A beta-accumulators (A beta CSF+/PET+; n = 201) plus 131 controls. In early A beta-accumulators, higher centiloid was associated with cross-sectional/longitudinal sTREM2 and p-tau(181) increases. Further, higher sTREM2 mediated the association between centiloid and cross-sectional/longitudinal p-tau(181) increases and higher sTREM2 was associated with FDG-PET hypermetabolism. In late A beta-accumulators, we found no association between centiloid and sTREM2 but a cross-sectional association between higher sTREM2, higher p-tau(181) and glucose hypometabolism. Our findings suggest that a TREM2-related microglial response follows earliest A beta fibrillization, manifests in inflammatory glucose hypermetabolism and may facilitate subsequent p-tau(181) increases in earliest AD.
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| 3. |
- Zheng, L. K., et al.
(författare)
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Combined Connectomics, MAPT Gene Expression, and Amyloid Deposition to Explain Regional Tau Deposition in Alzheimer Disease
- 2024
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Ingår i: Annals of Neurology. - 0364-5134. ; 95:2, s. 274-287
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Tidskriftsartikel (refereegranskat)abstract
- Objective: We aimed to test whether region-specific factors, including spatial expression patterns of the tau-encoding gene MAPT and regional levels of amyloid positron emission tomography (PET), enhance connectivity-based modeling of the spatial variability in tau-PET deposition in the Alzheimer disease (AD) spectrum.Methods: We included 685 participants (395 amyloid-positive participants within AD spectrum and 290 amyloid-negative controls) with tau-PET and amyloid-PET from 3 studies (Alzheimer's Disease Neuroimaging Initiative, F-18-AV-1451-A05, and BioFINDER-1). Resting-state functional magnetic resonance imaging was obtained in healthy controls (n = 1,000) from the Human Connectome Project, and MAPT gene expression from the Allen Human Brain Atlas. Based on a brain-parcellation atlas superimposed onto all modalities, we obtained region of interest (ROI)-to-ROI functional connectivity, ROI-level PET values, and MAPT gene expression. In stepwise regression analyses, we tested connectivity, MAPT gene expression, and amyloid-PET as predictors of group-averaged and individual tau-PET ROI values in amyloid-positive participants.Results: Connectivity alone explained 21.8 to 39.2% (range across 3 studies) of the variance in tau-PET ROI values averaged across amyloid-positive participants. Stepwise addition of MAPT gene expression and amyloid-PET increased the proportion of explained variance to 30.2 to 46.0% and 45.0 to 49.9%, respectively. Similarly, for the prediction of patient-level tau-PET ROI values, combining all 3 predictors significantly improved the variability explained (mean adjusted R-2 range across studies = 0.118-0.148, 0.156-0.196, and 0.251-0.333 for connectivity alone, connectivity plus MAPT expression, and all 3 modalities combined, respectively).Interpretation: Across 3 study samples, combining the functional connectome and molecular properties substantially enhanced the explanatory power compared to single modalities, providing a valuable tool to explain regional susceptibility to tau deposition in AD.
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| 4. |
- Coenen, Mirthe, et al.
(författare)
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Spatial distributions of white matter hyperintensities on brain MRI: A pooled analysis of individual participant data from 11 memory clinic cohorts
- 2023
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Ingår i: NeuroImage. Clinical. - 2213-1582. ; 40
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Tidskriftsartikel (refereegranskat)abstract
- INTRODUCTION: The spatial distribution of white matter hyperintensities (WMH) on MRI is often considered in the diagnostic evaluation of patients with cognitive problems. In some patients, clinicians may classify WMH patterns as "unusual", but this is largely based on expert opinion, because detailed quantitative information about WMH distribution frequencies in a memory clinic setting is lacking. Here we report voxel wise 3D WMH distribution frequencies in a large multicenter dataset and also aimed to identify individuals with unusual WMH patterns. METHODS: Individual participant data (N=3525, including 777 participants with subjective cognitive decline, 1389 participants with mild cognitive impairment and 1359 patients with dementia) from eleven memory clinic cohorts, recruited through the Meta VCI Map Consortium, were used. WMH segmentations were provided by participating centers or performed in Utrecht and registered to the Montreal Neurological Institute (MNI)-152 brain template for spatial normalization. To determine WMH distribution frequencies, we calculated WMH probability maps at voxel level. To identify individuals with unusual WMH patterns, region-of-interest (ROI) based WMH probability maps, rule-based scores, and a machine learning method (Local Outlier Factor (LOF)), were implemented. RESULTS: WMH occurred in 82% of voxels from the white matter template with large variation between subjects. Only a small proportion of the white matter (1.7%), mainly in the periventricular areas, was affected by WMH in at least 20% of participants. A large portion of the total white matter was affected infrequently. Nevertheless, 93.8% of individual participants had lesions in voxels that were affected in less than 2% of the population, mainly located in subcortical areas. Only the machine learning method effectively identified individuals with unusual patterns, in particular subjects with asymmetric WMH distribution or with WMH at relatively rarely affected locations despite common locations not being affected. DISCUSSION: Aggregating data from several memory clinic cohorts, we provide a detailed 3D map of WMH lesion distribution frequencies, that informs on common as well as rare localizations. The use of data-driven analysis with LOF can be used to identify unusual patterns, which might serve as an alert that rare causes of WMH should be considered.
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