Subjects

All protocols were approved by the Duke University Medical Center institutional review board and IRBs at each site (full list of all sites and IRBs is available at www.adni-info.org), and written informed consent was obtained from all subjects prior to enrollment. Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI was launched in 2003 as a public-private partnership with a primary goal to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). ADNI (ADNI ClinicalTrials.gov identifier: NCT00106899) is the result of efforts of many coinvestigators from a broad range of academic institutions and private corporations, with subjects recruited from over 50 sites across the United States and Canada. Details of the ADNI-1 and ADNI-2 protocol, timelines, study procedures and biomarkers can be found in the ADNI-1 and ADNI-2 procedures manual [http://www.adni-info.org/]. For up-to-date information, see www.adni-info.org.

All ADNI-1 and ADNI-2 late MCI subjects with at least one post-baseline visit data were eligible for inclusion. The criteria for classification as late MCI in ADNI-1 and ADNI-2 are identical and are as follows: subjective memory complaint, objective evidence of impaired memory calculated by scores of the Wechsler Memory Scale Logical Memory II adjusted for education, absence of significant confounding conditions such as current major depressive episode, normal, or near normal daily activities, absence of clinical dementia, an inclusive mini-mental state examination (MMSE) score from 24–30, and a score of 0.5 on the global CDR. For a detailed list of all selection criteria, refer to the ADNI procedures manual [http://www.adni-info.org/]. In addition to demographic data, for subject inclusion, data for all the following parameters were required: Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-cog) for at least two different time points, genotyping results, and biomarker data at baseline. The term “baseline” is used to indicate data collected first at either screening or baseline. Additional details are provided in the ADNI procedures manual. Early MCI (EMCI) subjects were not included in this analysis.

Clinical and Genetic Variables

Demographic variables included were age, gender, education level. APOE allele genotyping of all subjects was completed using DNA extracted from peripheral blood cells, with details provided elsewhere [http://www.adni-info.org]. In total, 378 MCI subjects from ADNI-1 were included. Cognitive and functional variables included were Alzheimer’s Disease Assessment Scale (ADAS-Cog 11 and 13), Mini Mental Scale Examination (MMSE), Montreal Cognitive Assessment (MOCA), and the Rey Auditory Verbal Learning (including subtests). Disease staging and activities of daily living scales included were the Clinical Dementia Rating (CDR-SB) and Functional Assessment Questionnaire (FAQ). Details of these tests can be found in the ADNI procedures manual [http://www.adni-info.org/].

Pathological and Neuronal Loss Biomarkers

Imaging and spinal fluid data were downloaded from the ADNI dataset.

MRI Measures

Structural MRI brain scans were acquired using 3 T MRI scanners with a standardized protocol. Quantification was performed in an automated pipeline using FreeSurfer software package version 5.1 (http://surfer.nmr.mgh.harvard.edu/fswiki). Detailed descriptions can be found at www.adni-info.org. Volumetric or thickness data on whole brain, lateral ventricles, hippocampus, entorhinal cortex, fusiform and medial temporal lobe were included. These served as surrogate markers for neuronal loss. Intracranial volume was also included as a covariate. For more details of MR imaging procedure, readers are referred to http://adni.loni.usc.edu. Each brain volume indicated is a summation of right and left hemispheric region and the unit is in mm﻿3﻿.

FDG-PET Measures

18F-FDG-PET standardized protocols, acquisition and analyses methods are described at http://adni.loni.usc.eduqw/methods/pet-analysis/pre-processing/ and at http://www.adni-info.org/Scientists/ADNIStudyProcedures.html. Cerebral metabolic rate for glucose (CMRgl) values were analyzed. We classified FDG-PET as a metabolic marker rather than as a pathological marker by convention but acknowledge it can also mark neuonal injury and pathological changes.

Amyloid PET (Pathological biomarker)

18F-florbetapir brain PET (referred to as AV45 PET) measures fibrillary cortical amyloid deposition and global SUVr values were used for our analyses. The global summary measures relative cortical Aβ deposition in frontal, cingulate, lateral parietal, and temporal cortices. Methods used to acquire and process ADNI florbetapir PET image data can be found at http://adni.loni.usc.edu/methods/.

Cerebrospinal fluid (CSF) measures

CSF samples were obtained by lumbar puncture and examined for total tau, phosphorylated tau (p-tau 181P ), and amyloid-beta (Aβ 1–42 ). CSF proteins were measured using the multiplex xMAP Luminex platform (Luminex Corp) with Innogenetics (INNO-BIA AlzBio3, for research use–only reagents) immunoassay kit–based reagents with details described elsewhere (www.adni-info.org).

MRI volumes, PET SUVRs and CSF protein levels were used as continuous variables.

ADNI-1 and ADNI-2 differed slightly in the numbers of subjects who had various biomarker tests. MRI was done in all subjects with at least one volumetric measure available in 561 subjects. CSF markers were available in 302 subjects (required for only a third of ADNI-1 subjects but required for all ADNI-2 subjects). FDG-PET was required for only half of ADNI-1 subjects and required for ADNI2 thus was available for 362 subjects. Florbetapir amyloid PET was done only in ADNI-2 and was available for 157 subjects. Details of imaging and spinal protein assay protocols, quality control and standardization across sites can be found on the ADNI website (http://www.adni-info.org/).

Longitudinal Cognitive, Functional and MRI Data

MCI subjects were monitored in both ADNI-1 and ADNI-2 at 6 month intervals for up to 5 years. Cognitive and biomarker tests were administered at specific intervals. From the available longitudinal data we computed slopes for 10 clinical (SCDRSB, SADAS11, SADAS13, SMMSE, SFAQ, SMOCA, SRAVLTimmediate, SRAVLTlearning, SRAVLTforgetting, SRAVLTpercForgetting) and 7 imaging descriptors (SVentricles, SHippocampus, SWholeBrain, SEntorhinal, SFusiform, SMidTemp, SICV). Slopes are identified by the name of the corresponding baseline descriptor with added starting ‘S’. For example SFAQ denotes changes of FAQ. Its value is the mean increase or decrease in a 6 month period computed for the complete period in which the patient has been monitored. In the rest of paper the computed slopes are denoted as prognostic descriptors. ADNI-1 subjects were analyzed only through the end of ADNI-1 (first 5 years) to keep it comparable to newly recruited ADNI-2 subjects who were also followed for upto 5 years.

Longitudinal Change in Diagnosis

The subject’s diagnosis was assessed at each visit by the site clinician using all available information. At each visit the MCI subject’s diagnosis could remain unchanged, or be changed to Dementia (if the subject worsened and met criteria) or be changed to Cognitively Normal (if the cognition had improved and subject no longer met MCI criteria). Subjects who met criteria for dementia were then further assessed to see if they met criteria for probable AD dementia. Details of criteria can be found in the ADNI procedures manual [http://www.adni-info.org/].

Statistical and Data Mining Methods

Summary Statistics

For initial descriptive and slope analyses, we used standard statistical methods: Man-Whitney’s test to detect descriptors for which two populations are statistically different and Spearman’s correlation to detect pairs of related descriptors. Because of the large number of variables, non-parametric test was used to avoid assumptions about distributions of variables. Simple linear regression slopes, without any covariates, were computed for clinical and MRI variable of interest using all time points available. Non-parametric methods are used in order to avoid assumptions about distributions of descriptor values.

Correlation Network

For correlation network visualization igraph package in R was used to obtain Fruchterman-Reingold force-directed layout. Edges present Spearman’s rank correlations with value rho ≥ 0.50. Distance between nodes is defined as the inverse of the correlation: dist = 1/ rho; small distance denotes large correlation. Fruchterman-Reingold force-directed graph technique was used to construct a layout in which strongly correlated concepts are next to each other and concepts that are strongly related to many other concepts are positioned in the center of the network. Baseline clinical and biomarker descriptors are denoted by squares while circles denote longitudinal slope descriptors. Green and yellow colors are used for clinical and MRI descriptors, respectively. Orange squares represent baseline 18F-FDG or 18F-florbetapir PET data while red squares are baseline spinal fluid data.

Multilayer Clustering Algorithms

A novelty of the work is application of a clustering tool for identification of homogeneous subpopulations of subjects. Although clustering is a well-known technique and many different algorithms are available, it is rarely used for insightful data analysis. The main reason is that application of different algorithms will typically result by different clusters. Each algorithm has parameters that have to be carefully adjusted by the user and whose selection also influences the final result23. In the absence of objective measures for the evaluation of the clustering results, a typical criterion for the selection of the most appropriate clustering algorithm and selection of its parameters is the usefulness of the clustering result24.

Multi-layer clustering algorithm has been used in this work because it enables the size and the number of clusters to be determined automatically. The algorithm consists of two steps; in the first step example similarity table (EST) is computed for each data layer and in the second step these tables are used by an agglomerative bottom-up procedure to find an optimal clustering solution. Similarity of instances is determined by execution of a supervised machine learning algorithm on an artificial classification task which is formulated so that original instances are positive class examples while randomized original instances are in the negative class19. The supervised learning algorithm constructs many rules that discriminate between original examples and randomized examples20. Similar positive examples are covered by many common rules while very different examples are rarely both covered by the same rule. EST is a symmetric NxN matrix where N is the number of original instances. Value in position x i, j represents similarity of examples i, j which is computed as a proportion of rules that cover this pair of examples.

The second step of the multi-layer algorithm is a heuristic procedure aimed at finding an optimal solution in which each instance i is clustered together with all instances with which it has high similarity while instances with low similarity should stay outside this cluster. The Clustering Related Variability (CRV) score CRV i is defined for each instance i

$$CR{V}_{i}=CR{V}_{i,wc}+CR{V}_{i,oc}$$

CRV i, wc is within cluster variability while CRV i,oc is outside cluster variability of EST values.

$$CR{V}_{i,wc}=\sum _{j\in C}{({x}_{i,j}-{x}_{mean,wc})}^{2}$$

$$CR{V}_{i,oc}=\sum _{j

otin C}{({x}_{i,j}-{x}_{mean,oc})}^{2}$$

CRV i, wc is computed from the values that are in row i and those columns corresponding to instances that are in the same cluster C as the instance i. Value x mean, wc is the mean value of x i, j in cluster C while value x mean, oc is the mean value for all other x i, j values in the row i. If example i is the only one example in cluster C then CRV i, wc = 0 and CRV i, oc is equal to the variability of all x i, j , i ≠ j. Clustering related variability for a cluster C, \(CR{V}_{C}={\sum }_{i\in C}CR{V}_{i}\) is defined as a sum of CRV i values for all instances included into the cluster. For each pair of clusters x, y the value

$$DIF{F}_{xy}=CR{V}_{x}+CR{V}_{xy}-CR{V}_{xy}$$

can be computed. DIFF xy has a positive value if merging clusters x and y enables reduction of the clustering related variability. In multi-layer clustering when two data layers are defined then EST and DIFF xy are computed independently for each data layer. In this case the joint DIFF xy is the smaller one of differences for both layers:

$$DIF{F}_{xy}=\,\min (DIF{F}_{xy,layer1},DIF{F}_{xy,layer2}).$$

The goal is to find a clustering solution so that for all constructed clusters the clustering related variability CRV C is minimal. The clustering starts with each example in its own cluster. In every iteration DIFF xy is computed for all possible pairs of clusters x, y in the current solution and the pair with maximal DIFF xy is selected. If this maximal value is positive it means that further reduction of variability is possible. Clusters x and y are merged and the next iteration starts. Otherwise, clustering procedure ends with the current solution as the optimal one. Details of the algorithm have been published previously25, 26.

The multi-layer algorithm is the substantial part of the web application called Exploratory Clustering. It is publicly available at http://rr.irb.hr/exploC/ 27. The tool can be used for various clustering tasks with up to 1000 instances and 1000 attributes. The ADNI baseline data for all MCI patients are loaded into the first data layer, and the second data layer consists of slopes of values computed from longitudinal data. In all, 26 baseline and 17 longitudinal variables were input. The tool is unbiased and clusters patients based on their variables and then based on the properties of obtained clusters the user can distinguish different clinically relevant subpopulations. The difference among experiments is that various subsets of input attributes are used for the computation of the similarity of instances.

Identifying and Validating Classifiers

Subgroup discovery technique was used to identify the best classifiers (clinical test cut-offs on ADAS, MMSE and RAVLT) to identify MCI rapid decliners as well as to compute sensitivity and specificity of constructed classifiers. The classifiers were first developed using ADNI-1 study MCI data and then replicated and validated using the ADNI-2 study MCI data. All MCI subjects were included in this analysis including slow and unclassified subgroups.

All methods were performed in accordance with the relevant ethical guidelines and regulations as as stated in the first section of Methods.