Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease

AbstractWe employed a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxelwise structural magnetic resonance imaging (MRI) of late-onset Alzheimer’s disease (AD) dementia patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus and amygdala), a subcortical atrophy factor (striatum, thalamus and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal and lateral occipital cortices). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloid-positive (Aβ+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in Aβ+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, while the cortical factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared to temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Code from this manuscript is publicly available at link_to_be_added.

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Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1611073113 ◽

2016 ◽

Vol 113 (42) ◽

pp. E6535-E6544 ◽

Cited By ~ 68

Author(s):

Xiuming Zhang ◽

Elizabeth C. Mormino ◽

Nanbo Sun ◽

Reisa A. Sperling ◽

Mert R. Sabuncu ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Executive Function ◽

Bayesian Model ◽

Late Onset ◽

Temporal Cortex ◽

Cortical Atrophy ◽

Future Research ◽

Dementia Patients ◽

With Memory

We used a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxelwise structural MRIs of late-onset Alzheimer’s disease (AD) dementia patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus, and amygdala), a subcortical atrophy factor (striatum, thalamus, and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal, and lateral occipital cortices). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloid–positive (Aβ+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in Aβ+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, whereas the cortical factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared with temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Factor compositions of participants and code used in this article are publicly available for future research.

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Diﬃculties in diagnosing atypical variants of Alzheimer’s disease

Russian neurological Journal ◽

10.30629/2658-7947-2021-26-5-16-23 ◽

2021 ◽

Vol 26 (5) ◽

pp. 16-23

Author(s):

A. A. Tappakhov ◽

T. Ya. Nikolaeva ◽

T. E. Popova ◽

N. A. Shnayder

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Clinical Picture ◽

Short Term Memory ◽

Late Onset ◽

Early Stage ◽

Primary Progressive Aphasia ◽

Cortical Atrophy ◽

Posterior Cortical Atrophy ◽

Short Term

Alzheimer’s disease (AD) is the most common cause of dementia in the population. Late onset AD has a classic clinical picture with short-term memory deficit, apraxia and agnosia. Patients with early-onset AD may have an atypical clinical picture which complicates diagnosis. Atypical AD variants include the logopenic variant of primary progressive aphasia, posterior cortical atrophy, behavioral, biparietal, and cortico-basal variants. These variants have pathomorphological signs similar to classical AD, but at an early stage they are characterized by focal atrophy which explains their clinical polymorphism. This article provides a review of the current literature on atypical types of AD and presents a clinical case of a 62-year-old patient in whom the disease debuted with prosopagnosia due to focal atrophy of the temporo-occipital regions of the non-dominant hemisphere.

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O4-01-05: Genetic variants in 14q chromosomal region are associated with memory in late-onset Alzheimer's disease

Alzheimer s & Dementia ◽

10.1016/j.jalz.2009.05.517 ◽

2009 ◽

Vol 5 (4S_Part_5) ◽

pp. P150-P150

Author(s):

Sandra Barral ◽

Joseph H. Lee ◽

Rong Cheng ◽

Christiane Reitz ◽

Vincent Santana ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Genetic Variants ◽

Chromosomal Region ◽

Late Onset ◽

With Memory

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Association of butyrylcholinesterase K variant with cholinesterase-positive neuritic plaques in the temporal cortex in late-onset Alzheimer's disease

Human Genetics ◽

10.1007/s004390000277 ◽

2000 ◽

Vol 106 (4) ◽

pp. 447-452 ◽

Cited By ~ 38

Author(s):

Donald J. Lehmann ◽

Zsuzsanna Nagy ◽

Suzanne Litchfield ◽

Mario Cortina Borja ◽

A. David Smith

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Late Onset ◽

Temporal Cortex ◽

Neuritic Plaques ◽

K Variant

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IC-P-009: Different patterns of cortical atrophy in early- and late-onset Alzheimer's disease

Alzheimer s & Dementia ◽

10.1016/j.jalz.2012.05.041 ◽

2012 ◽

Vol 8 (4S_Part_1) ◽

pp. P14-P14

Author(s):

Christiane Möller ◽

Hugo Vrenken ◽

Lize Jiskoot ◽

Adriaan Versteeg ◽

Frederik Barkhof ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Late Onset ◽

Cortical Atrophy

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Towards harmonizing subtyping methods for neuroimaging studies in Alzheimer’s disease

10.1101/2020.04.19.20064881 ◽

2020 ◽

Author(s):

Rosaleena Mohanty ◽

Gustav Mårtensson ◽

Konstantinos Poulakis ◽

J-Sebastian Muehlboeck ◽

Elena Rodriguez-Vieitez ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Reference Group ◽

Group Level ◽

Individual Level ◽

Dementia Patients ◽

Positron Emission ◽

Tau Pet ◽

The Individual

ABSTRACTBackgroundBiological subtypes in Alzheimer’s disease (AD), originally identified on neuropathological data, have been translated to in vivo biomarkers such as structural magnetic resonance imaging (sMRI) and positron emission tomography (PET), to disentangle the heterogeneity within AD. Although there is methodological variability across studies, comparable characteristics of subtypes are reported at the group level. In this study, we investigated whether group-level similarities translate to individual-level agreement across subtyping methods, in a head-to-head context.MethodsWe compared five previously published subtyping methods. Firstly, we validated the subtyping methods in 89 amyloid-beta positive (Aβ+) AD dementia patients (reference group: 70 Aβ-healthy individuals; HC) using sMRI. Secondly, we extended and applied the subtyping methods to 53 Aβ+ prodromal AD and 30 Aβ+ AD dementia patients (reference group: 200 Aβ-HC) using both sMRI and tau PET. Subtyping methods were implemented as outlined in each original study. Group-level and individual-level comparisons across methods were performed.ResultsEach individual method was replicated and the proof-of-concept was established. All methods captured subtypes with similar patterns of demographic and clinical characteristics, and with similar maps of cortical thinning and tau PET uptake, at the group level. However, large disagreements were found at the individual level.ConclusionsAlthough characteristics of subtypes may be comparable at the group level, there is a large disagreement at the individual level across subtyping methods. Therefore, there is an urgent need for consensus and harmonization across subtyping methods. We call for establishment of an open benchmarking framework to overcome this problem.

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P1-146: Different patterns of cortical atrophy in early- and late-onset Alzheimer's disease

Alzheimer s & Dementia ◽

10.1016/j.jalz.2012.05.423 ◽

2012 ◽

Vol 8 (4S_Part_4) ◽

pp. P157-P157

Author(s):

Christiane Möller ◽

Hugo Vrenken ◽

Lize Jiskoot ◽

Adriaan Versteeg ◽

Frederik Barkhof ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Late Onset ◽

Cortical Atrophy

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Comparison of subtyping methods for neuroimaging studies in Alzheimer’s disease: a call for harmonization

Brain Communications ◽

10.1093/braincomms/fcaa192 ◽

2020 ◽

Author(s):

Rosaleena Mohanty ◽

Gustav Mårtensson ◽

Konstantinos Poulakis ◽

J-Sebastian Muehlboeck ◽

Elena Rodriguez-Vieitez ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Amyloid Beta ◽

Reference Group ◽

Group Level ◽

Healthy Individuals ◽

Individual Level ◽

Dementia Patients ◽

Tau Pet ◽

The Individual

Abstract Biological subtypes in Alzheimer’s disease, originally identified on neuropathological data, have been translated to in vivo biomarkers such as structural magnetic resonance imaging (sMRI) and positron emission tomography (PET), to disentangle the heterogeneity within Alzheimer’s disease. Although there is methodological variability across studies, comparable characteristics of subtypes are reported at the group level. In this study, we investigated whether group-level similarities translate to individual-level agreement across subtyping methods, in a head-to-head context. We compared five previously published subtyping methods. Firstly, we validated the subtyping methods in 89 amyloid-beta positive Alzheimer’s disease dementia patients (reference group: 70 amyloid-beta negative healthy individuals) using sMRI. Secondly, we extended and applied the subtyping methods to 53 amyloid-beta positive prodromal Alzheimer’s disease and 30 amyloid-beta positive Alzheimer’s disease dementia patients (reference group: 200 amyloid-beta negative healthy individuals) using sMRI and tau PET. Subtyping methods were implemented as outlined in each original study. Group-level and individual-level comparisons across methods were performed. Each individual subtyping method was replicated, and the proof-of-concept was established. At the group level, all methods captured subtypes with similar patterns of demographic and clinical characteristics, and with similar cortical thinning and tau PET uptake patterns. However, at the individual level large disagreements were found in subtype assignments. Although characteristics of subtypes are comparable at the group level, there is a large disagreement at the individual level across subtyping methods. Therefore, there is an urgent need for consensus and harmonization across subtyping methods. We call for establishment of an open benchmarking framework to overcome this problem.

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