Differential patterns of gray matter volumes and associated gene expression profiles in cognitively-defined Alzheimer’s disease subgroups

Groot, Colin; Grothe, Michel J.; Mukherjee, Shubhabrata; Jelistratova, Irina; Jansen, Iris E.; Loenhoud, Anita C. van; Risacher, Shannon L.; Saykin, Andrew J.; Donald, Christine L. Mac; Mez, Jesse; Trittschuh, Emily H.; Gryglewski, Gregor; Lanzenberger, Rupert; Pijnenburg, Yolande A.L.; Barkhof, Frederik; Scheltens, Philip; Flier, Wiesje M. van der; Crane, Paul K.; Ossenkoppele, Rik

doi:10.1016/j.nicl.2021.102660

Cited by 15 publications

(28 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our group recently confirmed these phenotypes in vivo using a multisite dataset of over 2000 tau-positron emission tomography scans, and we additionally described the existence of a posterior and an asymmetric lateral temporal pattern of tau accumulation [6] (Figure 1A). Patterns similar to these latter phenotypes have been described in studies probing neurodegenerative profiles of late-onset AD cases with impairments in specific cognitive domains [3]. In our study, we did not find one dominant tau pattern that fits the description of 'typical' AD.…”

Section: The Landscape Of Clinical Heterogeneity In Adsupporting

confidence: 85%

Subtypes of Alzheimer’s disease: questions, controversy, and meaning

Vogel

Hansson

2022

Trends in Neurosciences

View full text Add to dashboard Cite

Section: The Landscape Of Clinical Heterogeneity In Adsupporting

confidence: 85%

Subtypes of Alzheimer’s disease: questions, controversy, and meaning

Vogel

Hansson

2022

Trends in Neurosciences

View full text Add to dashboard Cite

“…Moreover, this approach carries the notion that the typical population likely represents a more homogeneous group that is presumably a more adaptive or optimal state 16 , making it the reference standard for AD research and clinical trials. This is in contrast with recent studies that have demonstrated intragroup variability even within the clinical population of 'typical AD' 17 . Making heterogeneity the object of study requires to move beyond the classic case-control approach and use techniques that address interindividual variation.…”

Section: Introductioncontrasting

confidence: 99%

Examining real-world Alzheimer’s disease heterogeneity using neuroanatomical normative modelling

Loreto

Verdi

Kia

et al. 2022

Preprint

View full text Add to dashboard Cite

Alzheimer′s disease (AD) has been traditionally associated with episodic memory impairment and medial temporal lobe atrophy. However, recent literature has highlighted the existence of atypical forms of AD, presenting with different cognitive and radiological profiles. Failure to appreciate the heterogeneity of AD in the past has led to misdiagnoses, diagnostic delays, clinical trial failures and risks limiting our understanding of the disease. AD research requires the incorporation of new analytic methods that are as free as possible from the intragroup homogeneity assumption underlying case-control approaches according to which patients belonging to the same group are comparable to each other. Neuroanatomical normative modelling is a promising technique allowing for modelling the variation in neuroimaging profiles and then assessing individual deviations from the respective distribution. Here, neuroanatomical normative modelling was applied for the first time to a real-worldclinical cohortof Alzheimer′s disease patients (n=86) who had a positive amyloid PET scan and a T1-weighted MR performed as part of their diagnostic workup. The model indexed normal cortical thickness distributions using a separate healthy reference dataset (n= 33,072), employing hierarchical Bayesian regression to predict cortical thickness per region using age and sex. Transfer learning was used to recalibrate the normative model on avalidation cohort(n=20) of scanner-matched cognitively normal individuals. Brain heterogeneity was quantified as z-scores at each of the 148 ROIs generated within each AD patient. Z-scores < -1.96 defined as outliers. Clinical features including disease severity, presenting phenotypes and comorbidities were collected from health records to explore their association with outlier profiles. Amyloid quantification was performed using an automated PET-only driven method to examine the association between amyloid burden and outliers. The total number of individual outliers (total outlier count) in biomarker-confirmed AD clinical patients ranged between 1 and 120 out of 148 (median 21.5). The superior temporal sulcus was the region with the highest count of outliers (60%) in AD patients. The mean proportion of outliers was higher in the temporal (31.5%) than in the extratemporal (19.1%) regions and up to 20% of patients had no temporal outliers. We found higher mean outlier count in patients with non-amnestic phenotypes, at more advanced disease stages and without depressive symptoms. Amyloid burden was negatively associated with outlier count. This study corroborates the heterogeneity of brain atrophy in AD and provides evidence that this approach can be used to explore anatomo-clinical correlations at an individual level.

show abstract

“…GM density and cortical thickness (CT) obtained from sMRI are two widely used morphometric features. Both GM features respectively constitute multimode features with pathological information, so they have been applied to studies on AD subtypes [ 7 , 8 ]. The GM density for each region-of-interest (ROI) is computed as the sum of GM densities of all voxels within the ROI from the voxel-based morphometry (VBM) maps using statistical parametric mapping (SPM).…”

Section: Introductionmentioning

confidence: 99%

Concordance of Alzheimer’s Disease Subtypes Produced from Different Representative Morphological Measures: A Comparative Study

et al. 2022

View full text Add to dashboard Cite

Background: Gray matter (GM) density and cortical thickness (CT) obtained from structural magnetic resonance imaging are representative GM morphological measures that have been commonly used in Alzheimer’s disease (AD) subtype research. However, how the two measures affect the definition of AD subtypes remains unclear. Methods: A total of 180 AD patients from the ADNI database were used to identify AD subgroups. The subtypes were identified via a data-driven strategy based on the density features and CT features, respectively. Then, the similarity between the two features in AD subtype definition was analyzed. Results: Four distinct subtypes were discovered by both density and CT features: diffuse atrophy AD, minimal atrophy AD (MAD), left temporal dominant atrophy AD (LTAD), and occipital sparing AD. The matched subtypes exhibited relatively high similarity in atrophy patterns and neuropsychological and neuropathological characteristics. They differed only in MAD and LTAD regarding the carrying of apolipoprotein E ε2. Conclusions: The results verified that different representative morphological GM measurement methods could produce similar AD subtypes. Meanwhile, the influences of apolipoprotein E genotype, asymmetric disease progression, and their interactions should be considered and included in the AD subtype definition. This study provides a valuable reference for selecting features in future studies of AD subtypes.

show abstract

Differential patterns of gray matter volumes and associated gene expression profiles in cognitively-defined Alzheimer’s disease subgroups

Cited by 15 publications

References 71 publications

Subtypes of Alzheimer’s disease: questions, controversy, and meaning

Subtypes of Alzheimer’s disease: questions, controversy, and meaning

Examining real-world Alzheimer’s disease heterogeneity using neuroanatomical normative modelling

Concordance of Alzheimer’s Disease Subtypes Produced from Different Representative Morphological Measures: A Comparative Study

Contact Info

Product

Resources

About