Heterogeneity of neuroanatomical patterns in prodromal Alzheimer's disease: links to cognition, progression and biomarkers

Abstract

See Coulthard and Knight (doi:10.1093/aww335) for a scientific commentary on this article.Individuals with mild cognitive impairment and Alzheimer's disease clinical diagnoses can display significant phenotypic heterogeneity. This variability likely reflects underlying genetic, environmental and neuropathological differences. Characterizing this heterogeneity is important for precision diagnostics, personalized predictions, and recruitment of relatively homogeneous sets of patients into clinical trials. In this study, we apply state-of-the-art semi-supervised machine learning methods to the Alzheimer's disease Neuroimaging cohort (ADNI) to elucidate the heterogeneity of neuroanatomical differences between subjects with mild cognitive impairment (n = 530) and Alzheimer's disease (n = 314) and cognitively normal individuals (n = 399), thereby adding to an increasing literature aiming to establish neuroanatomical and neuropathological (e.g. amyloid and tau deposition) dimensions in Alzheimer's disease and its prodromal stages. These dimensional approaches aim to provide surrogate measures of heterogeneous underlying pathologic processes leading to cognitive impairment. We relate these neuroimaging patterns to cerebrospinal fluid biomarkers, white matter hyperintensities, cognitive and clinical measures, and longitudinal trajectories. We identified four such atrophy patterns: (i) individuals with largely normal neuroanatomical profiles, who also turned out to have the least abnormal cognitive and cerebrospinal fluid biomarker profiles and the slowest clinical progression during follow-up; (ii) individuals with classical Alzheimer's disease neuroanatomical, cognitive, cerebrospinal fluid biomarkers and clinical profile, who presented the fastest clinical progression; (iii) individuals with a diffuse pattern of atrophy with relatively less pronounced involvement of the medial temporal lobe, abnormal cerebrospinal fluid amyloid-β1-42 values, and proportionally greater executive impairment; and (iv) individuals with notably focal involvement of the medial temporal lobe and a slow steady progression, likely representing in early Alzheimer's disease stages. These four atrophy patterns effectively define a 4-dimensional categorization of neuroanatomical alterations in mild cognitive impairment and Alzheimer's disease that can complement existing dimensional approaches for staging Alzheimer's disease using a variety of biomarkers, which offer the potential for enabling precision diagnostics and prognostics, as well as targeted patient recruitment of relatively homogeneous subgroups of subjects for clinical trials.

Keywords: dementia; magnetic resonance imaging; mild cognitive impairment; neuroanatomical heterogeneity; pattern analysis.

Figure 1

Venn diagram depicting number of subjects classified tightly or loosely into clusters. Subjects with a probability >0.5 were included within a single cluster, whereas subjects with a highest cluster probability <0.5 are depicted in the interphase of the two top clusters.

Figure 2

VBM between the identified clusters and the cognitively normal reference group for the ADNI-1 (A) and ADNI-GO/2 cohorts (B). Colour scale represents the effect size of grey matter RAVENS maps of each comparison between a cluster and cognitively normal individuals. Red indicates greater atrophy (lower volume). Effect size maps are thresholded at false discovery rate (FDR) adjusted P-value of 0.05.

Figure 3

Progression from MCI to Alzheimer’s disease stratified by MRI-defined clusters.

Figure 4

Longitudinal cognitive changes in ADAS-Cog13, memory and executive composite scores in MCI subjects stratified by MRI-defined clusters.

Figure 5

Prevalence of clusters as a function of age. (A) Number of subjects with 5-year brackets. (B) Relative frequency of clusters, fitted with cubic splines.