A Multiomics Approach to Heterogeneity in Alzheimer's Disease

Focused Review and Roadmap

AmanPreet Badhwar; G. Peggy McFall; Shraddha Sapkota; Sandra E. Black; Howard Chertkow; Simon Duchesne; Mario Masellis; Liang Li; Roger A. Dixon; Pierre Bellec


Brain. 2020;143(5):1315-1331. 

In This Article

Brain Subtypes

Anatomical Subtypes

The spatial distribution of brain atrophy on structural MRI is highly heterogeneous in MCI, and Alzheimer's disease dementia patients (Nettiksimmons et al., 2014; Poulakis et al., 2018). Using data-driven clustering algorithms, 11 studies have attempted to subtype and characterize this inherent heterogeneity (Supplementary Table 1). Seven studies reported at least three distinct atrophy subtypes in Alzheimer's disease dementia (Noh et al., 2014; Hwang et al., 2016; Park et al., 2017; Poulakis et al., 2018; ten Kate et al., 2018), or mixed (Alzheimer's disease dementia and cognitively normal) cohorts (Varol et al., 2017; Tam et al., 2019). Subtypes were generally consistent across studies, and can be described as diffuse, medial temporal-predominant (temporal), and temporo-occipito-parietal-predominant (posterior) (Figure 1D). They were generated by applying (i) hierarchical agglomerative clustering using Ward's clustering linkage (Noh et al., 2014; Hwang et al., 2016; Tam et al., 2019), Louvain clustering (Park et al., 2017), random forest clustering (Poulakis et al., 2018), or non-negative matrix factorization (ten Kate et al., 2018) on cortical thickness or grey matter density maps; or (ii) clustering on grey matter density maps using a novel approach called HYDRA (Varol et al., 2017). Good agreement across studies may, in part, reflect usage of the same data sample [Alzheimer's Disease Neuroimaging Initiative (ADNI)] for subtype identification in four studies (Hwang et al., 2016; Varol et al., 2017; Poulakis et al., 2018; Tam et al., 2019). Some studies report two-subtype decomposition (Dong et al., 2016; Malpas, 2016), but these lack interstudy consensus. Using model-based clustering on regional cortical thickness measures from ADNI, Malpas (2016) reported normal and atrophic-entorhinal subtypes in a sample including Alzheimer's disease dementia and cognitively normal individuals. The atrophic-entorhinal subtype demonstrated considerable heterogeneity in entorhinal thickness, suggesting the presence of additional subtypes (Malpas, 2016). Dong et al. (2016) reported limbic-insular and parietal-occipital atrophy subtypes using CHIMERA clustering on brain volume data from Alzheimer's disease dementia and cognitively normal ADNI participants. In a separate study, the same group reported four atrophy subtypes using CHIMERA: normal, temporal, and two diffuse subtypes—one with predominant temporal involvement (diffuse-temporal), and one without (diffuse) (Dong et al., 2017). Visually, the diffuse subtype from CHIMERA shared some overlap with the posterior subtype described previously. In general, the reported subtypes (Dong et al., 2017) fit better with the three subtype solution, considering that, unlike previous studies, the CHIMERA study included cognitively normal individuals. Finally, Tam et al. (2019) identified a fourth atrophy subtype involving several language-related areas.

Figure 1.

Brain morphology and connectomics Alzheimer's disease-related subtypes. Neuroimaging provides insight into the effect of neurodegeneration on brain health. There exist different tools that can capture distinct, yet complementary, aspects of brain structure and function. The most established neuroimaging marker of neurodegeneration is grey matter atrophy, measured by structural MRI. Structural MRI is a non-invasive technique widely used in both research and clinical practice. To generate structural maps, individual structural MRI scans are first spatially aligned to a reference template or atlas (A). Then for each individual and each voxel (smallest volume element in MRI data), a metric characterizing the local structure of the grey matter is generated, such as (A) grey matter volume, cortical thickness or surface area. Using these approaches, it is possible to monitor the thinning of grey matter, which likely reflects the death of neuronal cell bodies at advanced stages of neurodegeneration. Synaptic disruption is an early event in Alzheimer's disease (Sperling et al., 2011), and functional networks may have the ability to compensate the impact of neurodegeneration on cognitive symptoms (Franzmeier et al., 2017). For these reasons, intrinsic functional connectivity from resting state functional MRI is an emerging Alzheimer's disease biomarker that holds promise for early diagnosis (Sperling et al., 2011; Badhwar et al., 2017). To analyse resting state functional MRI, select regions in canonical brain networks previously established in the literature are generally considered (B). An individual resting state functional MRI connectivity map can be generated for different networks, with the default mode, limbic, and salience networks being the key components affected by Alzheimer's disease (Badhwar et al., 2017) (B). Structural and functional brain maps can enter a subtyping procedure, which identifies groups of individuals with homogeneous brain maps (C).The number of subtypes are defined a priori or through various metrics for model selection (Seghier, 2018), for example n = 3 in C. A subtype map is generated by averaging the maps within each subgroup and subtracting the grand average (i.e. demeaned) to emphasize the features of the subtype. Chi square statistics are applied to identify groups that include a greater number of Alzheimer's disease patients than expected by chance (illustrated by a '*AD' annotation for subtype 2 in C). (D) The subtyping procedure was applied on maps of grey matter density from cognitively normal and Alzheimer's disease dementia individuals in the ADNI database (n = 377). Four of seven subtypes were identified as Alzheimer's disease dementia-related (results adapted from Tam et al., 2019). Three subtypes were consistent with previous reports: posterior (or temporo-occipito-parietal-predominant), diffuse, and temporal (or medial temporal-predominant) atrophy subtypes. A novel language atrophy subtype was also identified. (E) The subtype procedure was applied to resting state functional MRI data collected on cognitively normal, MCI, and Alzheimer's disease dementia individuals in a dataset pooling ADNI2 with several independent samples (n = 130). Three subtypes were extracted for three resting state networks known to be impacted by Alzheimer's disease: default mode, salience, and limbic (Badhwar et al., 2017). One Alzheimer's disease dementia/MCI-related subtype was found for each network. The salience and default mode followed similar patterns: increased within-network connectivity, and a lower (negative) connectivity between networks. The limbic subtypes showed lower connectivity with frontal regions, and increased connectivity with occipital regions. Results adapted from Orban et al. (2017b). The section on 'Brain subtypes' compares results from the abovementioned and other studies with similar approaches and objectives. Supplementary Table 1 provides detailed characteristics of the 12 neuroimaging subtyping studies (structural MRI and resting state functional MRI) that met our search criteria. BOLD = blood oxygenation level-dependent signal.

The choice of the number of subtypes is, to some degree, arbitrary. Two studies showed that their three subtypes could be decomposed into six (Noh et al., 2014), or more (Tam et al., 2019) homogeneous groups. Finally, an additional study looking at heterogeneity with a linear mixture model, instead of a discrete cluster analysis, showed that most individuals tend to express varying levels of multiple subtypes (Zhang et al., 2016). Continuous measures of subtype similarity are thus more advisable than discrete assignment (Zhang et al., 2016; Tam et al., 2019).

We now highlight various associations between Alzheimer's disease markers/risk factors and the three atrophy subtypes consistently reported. In three studies, Alzheimer's disease dementia patients with the posterior subtype were reported to be the youngest, and had the earliest age at onset (Noh et al., 2014; Hwang et al., 2016; Park et al., 2017). They also demonstrated greater PET-detectable amyloidosis (Hwang et al., 2016), and pathological levels of CSF amyloid-β42 and tau (Noh et al., 2014; Varol et al., 2017; ten Kate et al., 2018). Differences across subtypes were reported with fluorodeoxyglucose-PET-detectable glucose hypometabolism (Hwang et al., 2016), and white matter hyperintensities (ten Kate et al., 2018). Subtype-specific associations with Alzheimer's disease-related genes were also observed, specifically, apolipoprotein E (APOE) (Noh et al., 2014; Varol et al., 2017), CD2AP (CD2-associated protein) (Varol et al., 2017), SPON1 (Spondin-1) (Varol et al., 2017), LOC390956 or PPIAP59 (peptidyl-prolyl cis-trans isomerase A pseudogene) (Varol et al., 2017), though the association with APOE was not consistently found (Hwang et al., 2016). Associations of subtypes with cognition were observed for global (Varol et al., 2017; Tam et al., 2019) and domain-specific (e.g. episodic memory) (Noh et al., 2014; Park et al., 2017; Poulakis et al., 2018; ten Kate et al., 2018) measures, but not by all studies (Hwang et al., 2016). Associations between subtypes and sex were found to be significant in two (Noh et al., 2014; Varol et al., 2017) of four (Noh et al., 2014; Hwang et al., 2016; Varol et al., 2017; Tam et al., 2019) studies.

Functional Subtypes

By coupling cluster analysis and resting state functional MRI, a preprint report by Orban et al. (2017b) investigated connectivity subtypes in cognitively normal, MCI, and Alzheimer's disease dementia patients (Supplementary Table 1). They noted associations between functional connectivity subtypes and cognitive symptoms in the default mode, limbic, and salience networks in MCI, and Alzheimer's disease dementia patients (Figure 1E). Limbic subtypes were also associated with Alzheimer's disease biomarkers (CSF amyloid-β42 levels, APOE4 genotype) in an independent cohort at increased risk for familial Alzheimer's disease, suggesting that functional connectivity subtypes may be sensitive to the presence and progression of preclinical disease (Orban et al., 2017b).


Our review found convergent evidence of distinct brain atrophy subtypes in Alzheimer's disease dementia patients, including at least three data-driven atrophy subtypes: diffuse, temporal, and posterior. These structural subtypes seem to associate with established biomarkers, risk factors, and clinical symptoms of Alzheimer's disease, as well as cognitive subtypes: e.g. temporal subtype with memory impairment, and diffuse subtype with impaired executive function (Zhang et al., 2016). While Alzheimer's disease associated resting state functional subtypes in the default mode, limbic, and salience networks were only reported by one study (Orban et al., 2017b), the finding is in line with a recent meta-analysis (Badhwar et al., 2017) reporting consistent alterations in connectivity in the same three networks in patients with Alzheimer's disease dementia, MCI, or in both groups. The picture emerging from functional MRI data is one of aberrant between-network connectivity initiating in the mesolimbic network at the preclinical stage and propagating to the salience and default mode network with Alzheimer's disease progression (Orban et al., 2017b).