BACKGROUND The order and magnitude of pathologic processes in Alzheimer’s disease are not well understood, partly because the disease develops over many years. Autosomal dominant Alzheimer’s disease has a predictable age at onset and provides an opportunity to determine the sequence and magnitude of pathologic changes that culminate in symptomatic disease. METHODS In this prospective, longitudinal study, we analyzed data from 128 participants who underwent baseline clinical and cognitive assessments, brain imaging, and cerebrospinal fluid (CSF) and blood tests. We used the participant’s age at baseline assessment and the parent’s age at the onset of symptoms of Alzheimer’s disease to calculate the estimated years from expected symptom onset (age of the participant minus parent’s age at symptom onset). We conducted cross-sectional analyses of baseline data in relation to estimated years from expected symptom onset in order to determine the relative order and magnitude of pathophysiological changes. RESULTS Concentrations of amyloid-beta (Aβ)42 in the CSF appeared to decline 25 years before expected symptom onset. Aβ deposition, as measured by positron-emission tomography with the use of Pittsburgh compound B, was detected 15 years before expected symptom onset. Increased concentrations of tau protein in the CSF and an increase in brain atrophy were detected 15 years before expected symptom onset. Cerebral hypometabolism and impaired episodic memory were observed 10 years before expected symptom onset. Global cognitive impairment, as measured by the Mini–Mental State Examination and the Clinical Dementia Rating scale, was detected 5 years before expected symptom onset, and patients met diagnostic criteria for dementia at an average of 3 years after expected symptom onset. CONCLUSIONS We found that autosomal dominant Alzheimer’s disease was associated with a series of pathophysiological changes over decades in CSF biochemical markers of Alzheimer’s disease, brain amyloid deposition, and brain metabolism as well as progressive cognitive impairment. Our results require confirmation with the use of longitudinal data and may not apply to patients with sporadic Alzheimer’s disease. (Funded by the National Institute on Aging and others; DIAN ClinicalTrials.gov number, NCT00869817.)

Evidence before this study Using PubMed and Google Scholar the authors reviewed prior work on longitudinal neuroimaging markers of Alzheimer pathology with a focus on autosomal dominant Alzheimer disease (ADAD). We searched for all articles prior to October 31 st , 2017 with no language restrictions for the keywords Alzheimer's, Alzheimer, longitudinal, positron emission tomography, PET, MRI, atrophy, FDG, hypometabolism, familial, and autosomal. Theories proposed initially in 2010 by Jack and colleagues and revised in 2013 posited temporal trajectories of Alzheimer biomarkers relative to each other and clinical decline. Work by Bateman and colleagues in 2012, Benzinger and colleagues in 2013, and Fleisher and colleagues in 2015 depict such temporal ordering of biomarkers in ADAD populations derived from cross-sectional analyses. There was also a small subset of longitudinal ADAD studies, but these had one or more limitation such as small populations (n<50), examination of only one biomarker, not accounting for regional differences or correlations in the brain, or had a short duration of longitudinal followup. Added value of this studyOur study presents the first known work examining both the longitudinal temporal trajectories and spatial patterns of Alzheimer pathology in ADAD cohorts using neuroimaging. This work also presents the largest known cohort to date of ADAD individuals studied longitudinally with multiple neuroimaging biomarkers. Longitudinal analyses can provide a more accurate and powerful way to model the temporal emergence of pathology in ADAD. We find that mutation carriers first display Aβ accumulation, followed by hypometabolism, and finally structural atrophy; this is consistent with theoretical models and cross-sectional estimates from ADAD. Most importantly we consider such temporal relationships not in one singular summary measure, but characterize these trajectories throughout the brain. We found that the accrual of pathology varied throughout the brain and by modality in terms of the time of initial emergence and the rates of longitudinal change. These findings suggest region specific vulnerabilities to β-amyloidosis, metabolic decline, and atrophy that change over the course of the disease. Implications of all the available evidenceOur results build upon existing evidence characterizing biomarkers in clinical and preclinical Alzheimer disease. Our findings suggest that imaging biomarkers follow a sequential pattern, with β-amyloidosis, hypometabolism, and structural atrophy emerging more than twenty, fifteen, and ten years respectively before the expected onset of dementia. Although there is a general hierarchical pattern, there was considerable regional heterogeneity. Most commonly, regions demonstrated an increase in β-amyloidosis and structural atrophy, but there was not evidence of metabolic declines. Further, rather than being homogenous, the same biomarker often demonstrates different longitudinal trajectories across brain regions. Characterizing the temporal and regional dynamics...

, and structural atrophy (imaged by MRI). Recently we published the initial subset of imaging findings for specific regions in a cohort of individuals with autosomal dominant Alzheimer's disease. We now extend this work to include a larger cohort, wholebrain analyses integrating all three imaging modalities, and longitudinal data to examine regional differences in imaging biomarker dynamics. The anatomical distribution of imaging biomarkers is described in relation to estimated years from symptom onset. Autosomal dominant Alzheimer's disease mutation carrier individuals have elevated PiB levels in nearly every cortical region 15 y before the estimated age of onset. Reduced cortical glucose metabolism and cortical thinning in the medial and lateral parietal lobe appeared 10 and 5 y, respectively, before estimated age of onset. Importantly, however, a divergent pattern was observed subcortically. All subcortical gray-matter regions exhibited elevated PiB uptake, but despite this, only the hippocampus showed reduced glucose metabolism. Similarly, atrophy was not observed in the caudate and pallidum despite marked amyloid accumulation. Finally, before hypometabolism, a hypermetabolic phase was identified for some cortical regions, including the precuneus and posterior cingulate. Additional analyses of individuals in which longitudinal data were available suggested that an accelerated appearance of volumetric declines approximately coincides with the onset of the symptomatic phase of the disease.neuroimaging | aging | dementia | neurodegeneration | DIAN T he pathological mechanisms underlying nondominantly inherited late onset Alzheimer's disease (LOAD) remain an active area of investigation (1). According to the amyloid cascade hypothesis, the precipitating event in LOAD is an alteration of the balance between production and clearance of the metabolites of amyloid precursor protein (APP) (2). Abnormalities in APP metabolism then lead to β-amyloid (Aβ) deposition in the cerebral cortex, the formation of neurofibrillary tangles (NFTs) containing hyperphosphorylated tau protein, neuronal dysfunction, cell loss, and, ultimately, dementia. In vivo biomarkers of LOAD include cerebrospinal fluid (CSF) Aβ 42 , CSF tau, amyloid deposition imaged with Pittsburgh compound B PET (PiB PET) and other amyloid tracers, altered glucose metabolism imaged with fluro-deoxyglucose PET (FDG PET), and structural atrophy assessed by volumetric MRI. A theoretical model of biomarker changes has been proposed by Jack et al. (3) that links these Significance Beta-amyloid plaque accumulation, glucose hypometabolism, and neuronal atrophy are hallmarks of Alzheimer's disease. However, the regional ordering of these biomarkers prior to dementia remains untested. In a cohort with Alzheimer's disease mutations, we performed an integrated whole-brain analysis of three major imaging techniques: amyloid PET, [18 F] fluro-deoxyglucose PET, and structural MRI. We found that most gray-matter structures with amyloid plaques later have hypometabolism follo...

The objective is to demonstrate the mapping between lag structure and PCA. The illustration is not intended as a model of propagation in neural tissue. Each lag thread is also shown as a multidimensional time series with spectral content duplicated from real BOLD rs-fMRI data. B shows the superposition of the three lag threads. C shows the time-delay matrix (TD) recovered by analysis of the superposed time series in B, using the technique illustrated in SI Appendix, Fig. S1 (27).The bottom row of C shows the latency projection of TD computed as the average over each column. D illustrates the latency projection as a node diagram. This projection represents nodes that are, on average, early or late. Critically, the projection fails to capture the full lag structure. E illustrates eigendecomposition of the covariance structure of TD z , derived from TD by removing the mean of each column (see SI Appendix, Eqs. S4-S8). There are three significant eigenvalues (33), indicating the presence of three lag threads. In an ideal case, eigenvalues 4-6 would be zero; in this example, imperfect superposition leads to a small fourth nonzero eigenvalue. The eigenvectors corresponding to the first three eigenvalues are the thread topographies (shown above the eigenvalues). The lag thread sequences defined in A were accurately recovered purely by eigen-analysis of TD z . It should be noted that the lag threads in this illustration were a priori constructed to be mutually orthogonal (see SI Appendix, Eq. S7). Hence, they were neatly recovered intact by eigendecomposition of TD z . Also, although the nodes in this illustration are represented as foci, the algebra applies equally well to voxels, ROIs, or extended, possibly disjoint, topographies.www.pnas.org PNAS | December 29, 2015 | vol. 112 | no. 52 | E7307 CORRECTION