Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

Abstract

Introduction: The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015.

Methods: We used standard searches to find publications using ADNI data.

Results: (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers.

Discussion: Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.

Keywords: Alzheimer's disease; Amyloid; Biomarker; Disease progression; Mild cognitive impairment; Tau.

Copyright © 2017. Published by Elsevier Inc.

Figures

Fig. 1.

Applications for use of ADNI data, download activity, and the number of ADNI publications per year, 2006–2015. Abbreviation: ADNI, Alzheimer’s Disease Neuroimaging Initiative.

Fig. 2.

The correspondence among three measures of cognitive dysfunction in Alzheimer’s disease. Latent AD–related cognitive dysfunction was calculated using Item Response Theory methodologies estimated from ADAS-cog, MMSE, and CDR-SOB. Abbreviations: AD, Alzheimer’s disease; ADAS-cog, Alzheimer’s Disease Assessment Scale–cognitive subscale; CDR-SOB, Clinical Dementia Rating–Sum of Boxes; MMSE, Mini–Mental State Examination. Reproduced with permission from [21].

Fig. 3.

Steps followed to develop the European Alzheimer’s Disease Consortium–ADNI harmonized protocol for manual hippocampal segmentation (HarP). Reproduced with permission from [55].

Fig. 4.

Frequencies of different CSF and PET Aβ profiles in different diagnostic groups. Subjects were dichotomized by CSF Aβ42 or florbetapir PET and classified as concordant negative (CSF− PET−), discordant (CSF+ PET− and CSF− PET+), and concordant positive (CSF+ PET−). Abbreviations: AD, Alzheimer’s disease; CN, cognitively normal; CSF, cerebrospinal fluid; EMCI, early MCI; LMCI, late MCI; MCI, mild cognitive impairment; PET, positron emission tomography; SMC, subjective memory concern. Reproduced with permission from [141].

Fig. 5.

CSF ferritin independently predicts brain structural changes. (A–C) Longitudinal hippocampal volumetric changes based on tertiles of CSF: (A) ferritin, (B) ApoE, (C) tau/Aβ42. (D–F) Longitudinal volumetric ventricular changes based on tertiles of CSF: (D) ferritin, (E) ApoE, (F) tau/Aβ42. Abbreviations: CN, cognitively normal; CSF, cerebrospinal fluid; H, highest tertile; M, middle tertile; MCI, mild cognitive impairment. Reproduced with permission from [152].

Fig. 6.

Word cloud of genes names reported in articles using ADNI genetic data. The color and size of the gene name corresponds to the number of abstracts mentioning the gene. Abbreviation: ADNI, Alzheimer’s Disease Neuroimaging Initiative. Reproduced with permission from [172].

Fig. 7.

Converging “multi-omics” in ADNI. This figure illustrates the landscape of multiple “-omics” domains relevant to AD and how they contribute to an integrated Systems Biology approach to discovering the underlying genetic architecture of AD. *Data from ADNI-1. **Data from ADNI-GO/2. Abbreviations: AD, Alzheimer’s disease; ADNI, Alzheimer’s Disease Neuroimaging Initiative. Reproduced with permission from [171].

Fig. 8.

Sex-stratified FDG analyses. Analysis between APOE4 carriers and APOE4 noncarriers (P <.005 uncorrected) in (A) females and (B) males, covaried for age and years of education across the lateral and medial views of the cerebral cortex. As shown, female APOE4 carriers showed widespread clusters of decreased metabolism with respect to female APOE4 noncarriers (A), whereas male APOE4 carriers only showed an isolated cluster of decreased metabolism (P <.005) in the precuneus with respect male noncarriers (B). Abbreviations: FDG, [18F]-fluorodeoxyglucose; APOE4, apolipoprotein E ε4 allele. Reproduced with permission from [179].

Fig. 9.

Significance maps displaying the associations between cortical Aβ binding (Pittsburgh compound B) and plasma ApoE protein. Plasma apoE levels were associated with Pittsburgh compound B SUVR in the pooled sample in all brain regions apart from the sensorimotor and entorhinal cortex (top panel). Plasma apoE levels were associated with Pittsburgh compound B SUVR in BIN1 rs 744373 minor allele carriers (second panel) and in CD2AP rs 9349407 and CR1 rs 38118361 minor allele noncarriers (third and fourth panel, respectively). Abbreviation: SUVR, standardized uptake value ratio. Reproduced with permission from [181].

Fig. 10.

Effect of interactions between CR1 or EPHA1 and cardiovascular disease risk factors on hippocampal volume. The estimated interaction effect on hippocampal volume for both risk genes is dominated by high cardiovascular disease risk. High genetic risk appears to reduce the interaction effect in the presence of high cardiovascular disease risk, suggesting that cardiovascular disease risk factors are more detrimental under low genetic risk. Abbreviations: CVD, cardiovascular risk; G, genetic risk. Reproduced with permission from [189].

Fig. 11.

The effects of APOE ε4 and rs509208 (BCHE) on cortical Aβ levels. The APOE ε4 allele and the minor allele (G) of rs509208 of BCHE exerted independent and additive effects on cortical Aβ burden. Bars represent mean cortical Aβ levels ± standard errors. Abbreviations: BCHE, butyrylcholinesterase; SUVR, standardized uptake value ratio. Reproduced with permission from [218].

Fig. 12.

Genome-wide CSF Aβ42 associations. GeneMANIA networks showing the interaction results of (A) associated genes and (B) highly associated genes. (C) Novel polymorphisms identified in study. Abbreviation: CSF, cerebrospinal fluid. Reproduced with permission from [219].

Fig. 13.

Association and the effect of ILRAP rs12053868-G on longitudinal change in cortical Aβ PET burden. The minor G allele of rs12053868 in ILRAP was associated with higher rates of amyloid accumulation compared to the major A allele. Mean annualized percent change and global cortical 18F-florbetapir SUVR ± standard error. Abbreviation: SUVR, standardized uptake value ratio. Reproduced with permission from [221].

Fig. 14.

Hypothetical signaling network integrating top genes identified through Rasch analysis. A Rasch model was applied to the genes of ADNI GWAS data and supports APOE as a major susceptibility gene for AD, and functionally links other top genes (AEN, ADANTS12, PSMA5, FXN, NTRN, LARP1, WDTC1, SEMA7A, VKORC1L1, COL5A3) to AD. A hypothetical signaling network was generated from a pathway analysis of these genes based on known proteinprotein, functional, and phenomenological interactions. Abbreviations: AD, Alzheimer’s disease; ADNI, Alzheimer’s Disease Neuroimaging Initiative; ARC, activity-regulated cytoskeleton-associated protein; EEF2K, eukaryotic elongation factor-2 kinase, activated by GRM5 receptor, regulates global protein synthesis; HDAC3, histone deacetylase; MDM2, negative modulator of TP53 tumor suppression gene; PLXNC1, plexin C1 receptor for semaphorins; PTK2: FAK, kinase implicated in integrin signaling; FYN, src family tyrosine kinase, downstream target of GRM5 receptor; RPTOR, regulatory protein associated with MTORC1 complex. Reproduced with permission from [202].

Fig. 15.

Schematic outlining of the current understanding of the hypothetical timeline for the onset and progression of AD neurodegeneration and cognitive impairments. Age is indicated at the bottom, whereas the green, blue, and red bars indicate the time at which preventive, disease-modifying, and symptomatic interventions, respectively, are likely to be most effective. Within the aqua bar, milestones are shown in the pathobiology of AD that culminate in death and autopsy confirmation of AD. The proposed ADNI model of the temporal ordering of biomarkers of AD pathology relative to stages in the clinical onset and progression of AD is shown in the insert at the upper right based on Jack et al. [258], whereas the insert at the left illustrates the defining plaque and tangle pathologies of AD and common comorbid pathologies including Lewy body pathology (SYN), TDP-43, and hippocampal sclerosis. In the insert on the right, clinical disease is on the horizontal axis and it is divided into three stages: CN, MCI, and dementia. The vertical axis indicates the range from normal to abnormal for each of the biomarkers and the measures of memory and functional impairments. Abbreviations: AD, Alzheimer’s disease; ADNI, Alzheimer’s Disease Neuroimaging Initiative; CN, cognitively normal; CSF, cerebrospinal fluid; FDG, [18F]-fluorodeoxyglucose; MCI, mild cognitive impairment; MRI, magnetic resonance imaging; PET, positron emission tomography. Reproduced with permission from [129].

Fig. 16.

Model for Aβ status in disease progression. Illustrating the view of progression from presymptomatic Aβ negative to presymptomatic Aβ positive to MCI Aβ positive to AD Aβ positive as the primary pathway to AD, with the switch to Aβ positivity also occurring anywhere in the progression. A small percentage of clinically diagnosed AD patients lack Aβ pathology at autopsy. Abbreviations: AD, Alzheimer’s disease; MCI, mild cognitive impairment.

Fig. 17.

Differential trajectories of CSF biomarkers in Aβ+ and Aβ− subjects across disease progression. The means (±2 standard error of the mean) in ng/mL of (A) CSFAβ42; (B) CSF p-tau; (C) CSF p-tau181 are shown. In Aβ− groups, the levels of all three CSF biomarkers did not significantly increase across disease stage, whereas in Aβ+ subjects, CSF p-tau and t-tau, but not Aβ, increased across disease stages. Abbreviations: a-MCI, (advanced) mild cognitive impairment; Aβ+, subjects with abnormal brain Aβ; Aβ−, subjects without abnormal brain Aβ; CN, cognitively normal; CSF, cerebrospinal fluid; i-Dem, (incipient) dementia; iMCI, (incipient) mild cognitive impairment; m-Dem, (mild) dementia. Reproduced with permission from [261].

Fig. 18.

Hippocampal atrophy rate in Aβ+ and Aβ− CN and MCI subjects. The percentage of hippocampal atrophy rate attributable to Aβ status was calculated from the difference in hippocampal atrophy rate between Aβ+ and Aβ− subgroups. Aβ was measured using florbetapir PET. *P < .01, **P < .001, and ***P < .0001. Abbreviations: NC, normal cognition; MCI, mild cognitive impairment; PET, positron emission tomography. Reproduced with permission from [128].

Fig. 19.

Regions of Aβ-related atrophy ordered by acceleration and stabilization points. Regions, including the insula, posterior cingulate, amygdala, putamen, and precuneus, show early signs of atrophy before the hippocampus and entorhinal cortex. Parietal regions appear to have a shorter transition compared to temporal lobe regions with respect to Aβ. Red, yellow, and black dots represent significant (P <.05), marginally significant (.10 > P <.05), and nonsignificant (P >.10) acceleration or deceleration, respectively. Scale is pg/mL CSF Aβ42. Abbreviation: CSF, cerebrospinal fluid. Reproduced with permission from [266].

Fig. 20.

Hypometabolism originates earlier than atrophy in Aβ+ subjects. CN Aβ+ subjects displayed significant hypometabolism in medial parietal and bilateral parietal temporal regions compared to Aβ− subjects, whereas there was no difference in GM volume between these two groups, indicating that hypometabolism precedes atrophy in Aβ+ subjects (A) hypometabolism (red) and (B) atrophy (blue). Abbreviations: Aβ+, subjects with abnormal brain Aβ deposition; Aβ−, subjects without abnormal brain Aβ deposition; CN, cognitively normal; EMCI, early mild cognitive impairment; GM, gray matter; LMCI, late mild cognitive impairment. Reproduced with permission from [133].

Fig. 21.

Hippocampal volume and hypometabolism mediate the effect of Aβ on longitudinal change in Logical Memory Delayed Recall. Path analysis showing how hippocampal volume and angular FDG PET mediate the effect Aβ of on longitudinal change in Logical Memory delayed recall. (A) The direct effects of Aβ on memory; (B) hippocampal volume mediating the effects of Aβ on memory; (C) angular FDG PET mediating the effects of Aβ on memory; and (D) the combination of hippocampal volume and FDG PET mediating effects of Aβ on memory. The figure includes the following standardized regression coefficients: a, the effects of Aβ on hippocampal volume or FDG PET; b, the effects of hippocampal volume or FDG PET on the memory when adjusting for Aβ; c, the direct association between Aβ and memory (without adjusting for hippocampal volume or FDG PET); c′, the association between Aβ and memory when adjusting for hippocampal volume and/or FDG PET; and c-c′, the mediated effect on memory (with % mediation). *P < .05. Abbreviations: FDG, [18F]-fluorodeoxyglucose; PET, positron emission tomography. Reproduced with permission from [271].

Fig. 22.

Longitudinal changes in cognition in subtypes of Aβ+ cognitively normal subjects. Identified subtypes of cognitively normal subjects consisted of those with predominantly hippocampal atrophy, predominantly cortical atrophy, hippocampal and cortical atrophy combined, or neither type of atrophy. Baseline, 1-year, and 2-year follow-up data on (A) MMSE indicating global cognition, (B) ADNI-Mem indicating memory function, and (C) ADNI-EF indicating executive function are plotted, with means and standard errors. Abbreviations: ADNI, Alzheimer’s Disease Neuroimaging Initiative; BI, both impaired; BS, both spared; CA, cortical atrophy only; EF, executive function; HA, hippocampal atrophy only; Mem, memory domain; MMSE, Mini–Mental State Examination. Reproduced with permission from [272].

Fig. 23.

Heterogeneity of the subjective memory concern cohort. Cluster analysis identified three distinct subgroups in both the normal cognition (NC) and subjective memory concern (SMC) groups. The first subgroup (1) had elevated brain amyloid, decreased CSF Aβ, and substantially reduced hippocampal volume; the second subgroup (2) was similar to group 1 but with less hippocampal atrophy and was thought to correspond to the Jack sequence for early signs of AD, and the third group (3) was normal by all measures. Abbreviations: AD, Alzheimer’s disease; CSF, cerebrospinal fluid; ICV, intracranial volume; SUVR, standardized uptake value ratio. Reproduced with permission from [259].

Fig. 24.

Biomarker abnormality in A− N−, A+ N−, SNAP, and A+ N+ MCI patient groups, disaggregated by progressive cognitive deterioration. MCI subjects were grouped on the basis of absence or presence of abnormal levels of amyloid and neurodegeneration. SNAP subjects were neurodegeneration positive but amyloid negative. All four groups significantly differed in CSF Aβ42 concentrations, hypometabolism on FDG PET, and hippocampal volume. SNAP subjects were characterized by more severe hippocampal atrophy than other groups in the absence of abnormal amyloid. Triangles denote progressors, whereas circles denote nonprogressors. Abbreviations: AD, Alzheimer’s disease; A− N−, amyloid negative neurodegeneration negative; A+ N−, amyloid positive neurodegeneration negative; A+ N+, amyloid positive neurodegeneration positive; CSF, cerebrospinal fluid; FDG, [18F]-fluorodeoxyglucose; MCI, mild cognitive impairment; PET, positron emission tomography; SNAP, suspected non-Alzheimer’s pathology. Reproduced with permission from [291].

Fig. 25.

Effect of interaction of smoking history and APOE ε4 genotype on amyloid level and hypometabolism. ADNI participants were grouped on the basis of smoking history and APOE4 status. Smoking status interacted with APOE ε4 carrier status such that APOE4 smokers had higher levels of amyloid and worse hypometabolism than other groups. (A) Florbetapir retention level across groups. Higher values indicate greater Aβ level. Levels above the horizontal line indicate Aβ positivity. Mean ± standard error of the mean. (B) Composite glucose uptake level across groups. Higher values indicate greater glucose metabolism. Mean ± standard error of the mean. Abbreviations: ADNI, Alzheimer’s Disease Neuroimaging Initiative; FDG, [18F]-fluorodeoxyglucose; SUVR, standardized uptake value ratio. Reproduced with permission from [302].

Fig. 26.

The effect of stroke risk on hippocampal volume and memory performance in Aβ-positive and Aβ-negative subjects. Stroke risk, assessed by the Framingham Stroke Risk Profile, was associated with decreased baseline hippocampal volume and decreased memory performance in both Aβ+ and Aβ− subjects. Worst performance on both measurements was observed in subjects with both abnormal amyloid and high stroke risk. Error bars represent 95% confidence intervals. Abbreviations: ADNI-MEM, Alzheimer’s Disease Neuroimaging Initiative–memory domain; CSF, cerebrospinal fluid; ICV, intracranial volume. Reproduced with permission from [306].

Fig. 27.

Cumulative survival of individuals based on their high entorhinal cortex volume (ECV) and level of white-matter hyperintensities (WMHs). ADNI MCI subjects were dichotomized according to the median split of their ECV and levels of WMH. Individuals with high ECV and low WMH had low likelihood of rapid decline, whereas subjects with low ECV and low WMH or low ECV and high WMH appear to progress most rapidly. Solid line indicates high ECV, low WMH; dashed line indicates high ECV, high WMH; dotted line indicates low ECV, low WMH; dash-dotted line indicates low ECV, high WMH. Abbreviations: ADNI, Alzheimer’s Disease Neuroimaging Initiative; MCI, mild cognitive impairment. Reproduced with permission from [297].

Fig. 28.

The effect of hypertension and Aβ status on estimated trends of white-matter hyperintensities (WMHs) volume as a function of age. WMH volumes were predicted for the population average intracranial volume by age, exposure to elevated blood pressure, and CSF Aβ42 burden. High blood pressure increases WMH over time in both Aβ+ and Aβ− subjects, but the greatest effect in Aβ+ subjects. Abbreviation: CSF, cerebrospinal fluid. Reproduced with permission from [300].

Fig. 29.

Conceptual model linking white-matter hyperintensities to clinical progression of AD. Regional white-matter hyperintensities (WMHs) affect tau directly (a), affect regional atrophy and clinical progression directly (b), and modify the effect of tau on disease progression (c). The interaction between regional WMHs and Aβ has yet to be elucidated. Abbreviation: AD, Alzheimer’s disease. Reproduced with permission from [313].

Fig. 30.

Visualization of neuronal fibers touching limbic system ROIs in typical early MCI (left) and late MCI (right) patients. Abbreviation: MCI, mild cognitive impairment. Reproduced with permission from [326].

Fig. 31.

Average brain networks showing common connections at 90% of healthy controls (CN), MCI, and AD participants at k = 20 nodal degree threshold. Although individual connections (red edges) erode with disease progression, centrally positioned hubs (light blue nodes) are preserved in diagnostic groups. These hubs are in the superior frontal (SF), insula (I), posterior cingulate (PC), precuneus (P), and superior parietal cingulate regions (SP). Abbreviations: AD, Alzheimer’s disease; MCI, mild cognitive impairment. Reproduced with permission from [328].

Fig. 32.

Overview of intrinsic connectivity networks. The figure shows standardized maps of seven intrinsic conductivity networks projected on the cortical surface and a midsagittal section of the reference template. This map estimates the functional conductivity architecture of the human cerebral cortex based on resting state functional conductivity projected on the cortical surface and a midsagittal section of the reference template. Abbreviations: blue, limbic network; cyan, somatomotor network; green, dorsal attention network; pink, ventral attention network; purple, visual network; red, default mode network; yellow, frontoparietal-control network. Reproduced with permission from [335].

Fig. 33.

Severity of AD-related imaging abnormalities within intrinsic connectivity networks. Plots depict means and 95% confidence intervals of averaged Z scores of Aβ deposition (top), hypometabolism (middle), and gray-matter atrophy (bottom) within the distinct intrinsic connectivity networks for each AD stage. The widespread distribution of amyloid deposition across the cerebral cortex appeared similar in all patient groups with highest amyloid load in the DMN and FPN. Hypometabolism was most pronounced in the AD group and occurred across most ICNs except the VIS and SMN. Likewise, atrophy was most pronounced in the AD group, which displayed a different relative pattern of atrophy severity across ICNs with atrophy most pronounced in the LIN followed by the DMN and relative sparing of the FPN. Abbreviations: blue, limbic network (LIN); cyan, somatomotor network (SMN); green, dorsal attention network (DAN); pink, ventral attention network (VAN); purple, visual network (VIS); red, default mode network (DMN); yellow, frontal parietal control network (FTN); AD, Alzheimer’s disease; CN, cognitively normal; EMCI, early mild cognitive impairment; LMCI, late mild cognitive impairment. Reproduced with permission from [335].

Fig. 34.

Subsystems of the default mode network. Nodes within the default mode network segregate into distinct subsystems. Abbreviations: aMPFC, anterior or medial prefrontal cortex; blue: dorsal medial prefrontal cortex system; dMPFC, dorsal medial prefrontal cortex; green, medial temporal lobe memory system; yellow, midline core regions; PCC, posterior cingulate cortex; Rsp, retrosplenial cingulate. Reproduced with permission from [336].

Fig. 35.

Schematic of the proposed cascading network failure model of Alzheimer’s disease. Phase 0: The posterior DMN (pDMN) serves as the central hub processing and integrating association cortices and is highly metabolically active. Independently, the medial temporal lobe (MTL) has accumulated age-related damage from neocortical processing of a different kind contributing to primary age-related tauopathy (PART) in these regions. Phase 1: Declining posterior DMN transfers information-processing duties to the neocortical regions including the ventral DMN and/or the anterior dorsal DMN. Aberrant betweenneocortical network synaptic activity leads to dysregulated amyloid precursor protein (APP) processing promoting Aβ plaque formation in neocortical layers. Phase 2: Given that the hippocampus is continually processing information from the same regions, noise in these cortical systems is propagated down to the hippocampus. This increased burden on the hippocampus accelerates the preexisting PART. Phase 3: Neurodegeneration expands to adjacent systems. This creates a detrimental positive feedback loop because degeneration lowers the noise-handling capacity of the system leading to further degeneration. MCI phase: Posterior brain regions supporting memory succumb to the degenerative feedback loop as hippocampal regions increases processing. Later, the frontal brain regions begin to bear the high connectivity burden. Early Alzheimer’s disease phase: The high frontal connectivity firmly establishes the neurodegenerative feedback loop in these systems before declining as Alzheimer’s disease progresses. Abbreviations: DMN, default mode network; MCI, mild cognitive impairment. Reproduced with permission from [336].

Fig. 36.

Characteristic regional Aβ deposition patterns in healthy and pathological brains. An epidemic spreading model that predicts propagation/deposition of Aβ reproduces the characteristic Aβ deposition patterns in the ADNI cohort. (A) PET-based mean regional Aβ deposition probabilities in cognitively normal healthy controls (HC), early MCI (EMCI), late MCI (LMCI), and AD groups. Nodes correspond to 78 regions covering all the brains gray matter, with node sizes proportional to the associated Aβ burden. The progressive expansion of Aβ deposition starts mainly from the DMN regions and expands to the rest of the brain. (B) Correspondence between the estimated and PET-based mean regional Aβ deposition probabilities for the different clinical groups. Abbreviations: AD, Alzheimer’s disease; ADNI, Alzheimer’s Disease Neuroimaging Initiative; DMN, default mode network; MCI, mild cognitive impairment; PET, positron emission tomography. Reproduced with permission from [346].

Fig. 37.

Patterns of decline of the different classes of markers. The greatest effect sizes for MCI converters were for functional measures (Functional Activities Questionnaire [FAQ]) and for cognitive measures such as the ADAS-cog. Effect sizes for volumetric and CSF biomarker measures were much smaller. Panel 1: effect sizes for the difference in cognitive and functional measures between baseline and each one of the follow-ups from months 12–48: (A) MCI converters, (B) stable MCI. Panel 2: effect sizes in MRI morphometry, FDG PET HCI, and CSF biomarkers between baseline and months 12–36 follow-ups: (C) MCI converters, (D) stable MCI. Abbreviations: ADAS-cog, Alzheimer’s Disease Assessment Scale–cognitive; CSF, cerebrospinal fluid; FDG, [18F]-fluorodeoxyglucose; HCI, hyperbolic convergence index; MCI, mild cognitive impairment; MRI, magnetic resonance imaging; PET, positron emission tomography. Reproduced with permission from [354].

Fig. 38.

Observed versus predicted progression from amnestic MCI to AD over 3 years by Brief Clinical Index point score. The solid line shows the proportion of subjects predicted to progress from amnestic MCI to AD over 3 years as a function of the Brief Clinical Index point score. The dotted line shows the actual proportions that progressed at each point score value based on 3-year Kaplan-Meier estimates. The vertical bars showed the number of individuals at each point score value (right vertical axis). Abbreviations: AD, Alzheimer’s disease; MCI, mild cognitive impairment. Reproduced with permission from [360].

Fig. 39.

Associations between 249 variables shown by a circular visualization of correlation plot. Data from 928 patients with mild cognitive impairment were used to produce a network visualization of the complex relationships between and within variables in ADNI. Lines represent the Spearman rank correlation coefficient |(r)| = .3 between two variables. 1—sex, 2—years education, 3—age, 4—MMSE, 5—ADAS total score, 6—ADAS modified, 7—CDR composite score, 8—CDR-SOB composite score, 9—FAQ, 10—GDS, 11—Hachinski Ischemic Scale score, 12—NIQ total score, 13—brain volume, 14—intracranial volume, 15—ventricular volume, 16—hippocampal volume, 17—inferior lateral ventricular volume, 18—middle temporal volume, 19—inferior temporal volume, 20—fusiform cortical volume, 21—entorhinal cortex volume, 22—APOE4 carrier, 23—no. of APOE4 alleles, 24—TOMM40 polyT allele one, 25—TOMM40 polyT allele 2, 26—8-Iso-PGF, 27—8,12-iso-IPFa, 28—CSF white blood cell count, 29—CSF red blood cell count, 30—CSF total protein concentration, 31—CSF glucose level, 32—total plasma homocysteine level, 33—plasma Aβ40 level, 34—plasma Aβ42 level, 35—plasma Aβ40:AB42 ratio, 36—CSF Aβ42 level, 37—CSF t-tau level, 39—CSF Aβ42:t-tau ratio, 40—CSF Aβ42-p-tau ratio, 41—CSF p-tau:t-tau ratio, 42 to 115—74 CSF analytes measured by multiplex assay, 116 to 249—hundred and 34 plasma analytes measured by multiplex assay. Abbreviations: ADAS, Alzheimer’s Disease Assessment Scale; ADNI, Alzheimer’s Disease Neuroimaging Initiative; CDR-SOB, Clinical Dementia Rating–Sum of Boxes; CSF, cerebrospinal fluid; FAQ, Functional Activities Questionnaire; MMSE, Mini–Mental State Examination. Reproduced with permission from [425].

Fig. 40.

Prediction of progression from mild cognitive impairment to Alzheimer’s disease within 3 years. Markers in plasma or CSF predicted progression with a relatively high sensitivity compared to standard AD CSF biomarkers, regional MRI volumes, or cognitive scores. Prediction includes seven models combining different subsets of variables. Correct classification rate of the top 20 variables was estimated on the test data set after 1000-fold resampling of the learning and test data sets. Sex and age were included in all models. APOE4 indicates apolipoprotein ε4. Abbreviations: AD, Alzheimer’s disease; CSF, cerebrospinal fluid; MRI, magnetic resonance imaging. Reproduced with permission from [425].

Fig. 41.

Nonlinear disease progression model capturing longitudinal Clinical Dementia Rating–Sum of Boxes (CDR-SB) scores. Visual predictive check simulations suggest that the model describes longitudinal progression of CDR-SB scores in both late MCI and mild AD subjects. Stratification using p-tau181/Aβ42 ratio reveals a lack of disease progression in biomarker negative subgroups. The upper, middle, and lower profiles indicated by the open circles represent the 95th, 50th, and 5th percentiles of the observed data, respectively. The upper, middle, and lower curves indicated by the lines are the median model–based predictions for the 95th, 50th, and 5th percentiles, respectively. The shaded areas are the 90% confidence intervals of the corresponding percentiles of the simulations based on the model. Abbreviations: AD, Alzheimer’s disease; LMCI, late MCI; MCI, mild cognitive impairment. Reproduced with permission from [459].

Fig. 42.

Implications of hippocampal volume–based enrichment for clinical trials of amnestic MCI subjects. Estimates of trial costs and total duration for scenarios in which patients are enriched or not enriched with hippocampal volume are given for different outcome measures. (A–C) Trial cost and (D–F) trial execution time, as a function of cut point for (A and D) MMSE, (B and E) ADAS-cog, and (C and F) CDR-SB. Results are expressed as fractions of the unenriched scenario and are shown for four different hippocampal volume computational algorithms. Variance due to test-retest variability is shown as the shaded area for one of the four algorithms (LEAP). Abbreviations: ADAS-cog, Alzheimer’s Disease Assessment Scale–cognitive; CDR-SB, Clinical Dementia Rating–Sum of Boxes; MCI, mild cognitive impairment; MMSE, Mini–Mental State Examination. Reproduced with permission from [470].

Fig. 43.

Ability of cognitive end points to detect change in cognitively normal subjects selected for multiple pathologies. Composite cognitive tests were more able to capture decline in cognitively normal (CN) subjects over 7 years than any measure of a single cognitive domain or ADAS-cog alone. Enrichment with three or more pathologies optimally enhanced this effect. Groups with 0, 1, 2, or 3+ pathologies (APOE4, Aβ+, tau+, or hippocampal atrophy1) plotted for each standardized cognitive measure with 7 years of follow-up. Composite #1: ADAS-11, Trails B, and Logical Memory II. Composite #2: ADAS-11, Trails B, and dALVT. Abbreviations: ADAS, Alzheimer’s Disease Assessment Scale; ADAS-cog, Alzheimer’s Disease Assessment Scale–cognitive; dAVLT, delayed Rey Auditory Verbal Learning Test. Reproduced with permission from [478].

Fig. 44.

The effect of clinical trial enrichment using APOE4 status or brain Aβ load. After screening participants for APOE4 status or brain Aβ load, sample size requirements are around 100 subjects for a 2-year trial. Sample size estimates (n80s) after trial enrichment using APOE4 status (A), brain Aβ load at screening (B), or both combined (C). Abbreviations: AD, Alzheimer’s disease; CN, cognitively normal; EMCI, early mild cognitive impairment; LMCI, late mild cognitive impairment. Reproduced with permission from [479].