Daniel S. Marcus

Clinical and Biomarker Changes in Dominantly Inherited Alzheimer's Disease

BACKGROUND The order and magnitude of pathologic processes in Alzheimers disease are not well understood, partly because the disease develops over many years. Autosomal dominant Alzheimers 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 participants age at baseline assessment and the parents age at the onset of symptoms of Alzheimers disease to calculate the estimated years from expected symptom onset (age of the participant minus parents 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 Alzheimers disease was associated with a series of pathophysiological changes over decades in CSF biochemical markers of Alzheimers 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 Alzheimers disease. (Funded by the National Institute on Aging and others; DIAN ClinicalTrials.gov number, NCT00869817.).

A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume

Atlas normalization, as commonly used by functional data analysis, provides an automated solution to the widely encountered problem of correcting for head size variation in regional and whole-brain morphometric analyses, so long as an age- and population-appropriate target atlas is used. In the present article, we develop and validate an atlas normalization procedure for head size correction using manual total intracranial volume (TIV) measurement as a reference. The target image used for atlas transformation consisted of a merged young and old-adult template specifically created for cross age-span normalization. Automated atlas transformation generated the Atlas Scaling Factor (ASF) defined as the volume-scaling factor required to match each individual to the atlas target. Because atlas normalization equates head size, the ASF should be proportional to TIV. A validation analysis was performed on 147 subjects to evaluate ASF as a proxy for manual TIV measurement. In addition, 19 subjects were imaged on multiple days to assess test-retest reliability. Results indicated that the ASF was (1) equivalent to manual TIV normalization (r = 0.93), (2) reliable across multiple imaging sessions (r = 1.00; mean absolute percentage of difference = 0.51%), (3) able to connect between-gender head size differences, and (4) minimally biased in demented older adults with marked atrophy. Hippocampal volume differences between nondemented (n = 49) and demented (n = 50) older adults (measured manually) were equivalent whether corrected using manual TIV or automated ASF (effect sizes of 1.29 and 1.46, respectively). To provide normative values, ASF was used to automatically derive estimated TIV (eTIV) in 335 subjects aged 15-96 including both clinically characterized nondemented (n = 77) and demented (n = 90) older adults. Differences in eTIV between nondemented and demented groups were negligible, thus failing to support the hypothesis that large premorbid brain size moderates Alzheimers disease. Gender was the only robust factor that influenced eTIV. Men showed an approximately approximately 12% larger eTIV than women. These results demonstrate that atlas normalization using appropriate template images provides a robust, automated method for head size correction that is equivalent to manual TIV correction in studies of aging and dementia. Thus, atlas normalization provides a common framework for both morphometric and functional data analysis.

Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults

The Open Access Series of Imaging Studies is a series of magnetic resonance imaging data sets that is publicly available for study and analysis. The initial data set consists of a cross-sectional collection of 416 subjects aged 18 to 96 years. One hundred of the included subjects older than 60 years have been clinically diagnosed with very mild to moderate Alzheimers disease. The subjects are all right-handed and include both men and women. For each subject, three or four individual T1-weighted magnetic resonance imaging scans obtained in single imaging sessions are included. Multiple within-session acquisitions provide extremely high contrast-to-noise ratio, making the data amenable to a wide range of analytic approaches including automated computational analysis. Additionally, a reliability data set is included containing 20 subjects without dementia imaged on a subsequent visit within 90 days of their initial session. Automated calculation of whole-brain volume and estimated total intracranial volume are presented to demonstrate use of the data for measuring differences associated with normal aging and Alzheimers disease.

The Human Connectome Project: A data acquisition perspective

The Human Connectome Project (HCP) is an ambitious 5-year effort to characterize brain connectivity and function and their variability in healthy adults. This review summarizes the data acquisition plans being implemented by a consortium of HCP investigators who will study a population of 1200 subjects (twins and their non-twin siblings) using multiple imaging modalities along with extensive behavioral and genetic data. The imaging modalities will include diffusion imaging (dMRI), resting-state fMRI (R-fMRI), task-evoked fMRI (T-fMRI), T1- and T2-weighted MRI for structural and myelin mapping, plus combined magnetoencephalography and electroencephalography (MEG/EEG). Given the importance of obtaining the best possible data quality, we discuss the efforts underway during the first two years of the grant (Phase I) to refine and optimize many aspects of HCP data acquisition, including a new 7T scanner, a customized 3T scanner, and improved MR pulse sequences.

Decreased cerebrospinal fluid Aβ42 correlates with brain atrophy in cognitively normal elderly

For therapies for Alzheimers disease (AD) to have the greatest impact, it will likely be necessary to treat individuals in the “preclinical” (presymptomatic) stage. Fluid and neuroimaging measures are being explored as possible biomarkers of AD pathology that could aid in identifying individuals in this stage to target them for clinical trials and to direct and monitor therapy. The objective of this study was to determine whether cerebrospinal fluid (CSF) biomarkers for AD suggest the presence of brain damage in the preclinical stage of AD.

The Extensible Neuroimaging Archive Toolkit: an informatics platform for managing, exploring, and sharing neuroimaging data.

The Extensible Neuroimaging Archive Toolkit (XNAT) is a software platform designed to facilitate common management and productivity tasks for neuroimaging and associated data. In particular, XNAT enables qualitycontrol procedures and provides secure access to and storage of data. XNAT follows a threetiered architecture that includes a data archive, user interface, and middleware engine. Data can be entered into the archive as XML or through data entry forms. Newly added data are stored in a virtual quarantine until an authorized user has validated it. XNAT subsequently maintains a history profile to track all changes made to the managed data. User access to the archive is provided by a secure web application. The web application provides a number of quality control and productivity features, including data entry forms, data-type-specific searches, searches that combine across data types, detailed reports, and listings of experimental data, upload/download tools, access to standard laboratory workflows, and administration and security tools. XNAT also includes an online image viewer that supports a number of common neuroimaging formats, including DICOM and Analyze. The viewer can be extended to support additional formats and to generate custom displays. By managing data with XNAT, laboratories are prepared to better maintain the long-term integrity of their data, to explore emergent relations across data types, and to share their data with the broader neuroimaging community.

Cerebrospinal fluid tau and ptau181 increase with cortical amyloid deposition in cognitively normal individuals: Implications for future clinical trials of Alzheimer's disease

Alzheimers disease (AD) pathology is estimated to develop many years before detectable cognitive decline. Fluid and imaging biomarkers may identify people in early symptomatic and even preclinical stages, possibly when potential treatments can best preserve cognitive function. We previously reported that cerebrospinal fluid (CSF) levels of amyloid‐β42 (Aβ42) serve as an excellent marker for brain amyloid as detected by the amyloid tracer, Pittsburgh compound B (PIB). Using data from 189 cognitively normal participants, we now report a positive linear relationship between CSF tau/ptau181 (primary constituents of neurofibrillary tangles) with the amount of cortical amyloid. We observe a strong inverse relationship of cortical PIB binding with CSF Aβ42 but not for plasma Aβ species. Some individuals have low CSF Aβ42 but no cortical PIB binding. Together, these data suggest that changes in brain Aβ42 metabolism and amyloid formation are early pathogenic events in AD, and that significant disruptions in CSF tau metabolism likely occur after Aβ42 initially aggregates and increases as amyloid accumulates. These findings have important implications for preclinical AD diagnosis and treatment.

Regional variability of imaging biomarkers in autosomal dominant Alzheimer’s disease

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, [18F]fluro-deoxyglucose PET, and structural MRI. We found that most gray-matter structures with amyloid plaques later have hypometabolism followed by atrophy. Critically, however, not all regions lose metabolic function, and not all regions atrophy, even when there is significant amyloid deposition. These regional disparities have important implications for clinical trials of disease-modifying therapies. Major imaging biomarkers of Alzheimer’s disease include amyloid deposition [imaged with [11C]Pittsburgh compound B (PiB) PET], altered glucose metabolism (imaged with [18F]fluro-deoxyglucose PET), 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, whole-brain 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.

Longitudinal Change in CSF Biomarkers in Autosomal-Dominant Alzheimer’s Disease

Longitudinal cerebrospinal fluid biomarker analyses reveal decreases in neuronal injury markers in later stages of autosomal-dominant Alzheimer’s disease. Biphasic Changes in CSF Biomarkers in AD Data from clinicopathological and biomarker studies of Alzheimer’s disease (AD) have converged to support the existence of a long “preclinical” (asymptomatic) stage during which pathologies develop before the appearance of cognitive symptoms. Substantiating the longitudinal change in biomarkers over time will advance our basic understanding of the disease and provide information critical for the design and interpretation of disease-modifying clinical trials that use biomarkers for subject enrollment, for proof of target engagement, or as outcome measures. Biomarkers are required to identify individuals in the preclinical stage to target them for secondary prevention trials designed to preserve normal cognitive function. Study of families with autosomal-dominant AD (ADAD) mutations permits characterization of biomarker changes during the full range of the disease process because of the certainty of eventual dementia in mutation carriers and the relatively predictable ages at symptom onset within families. Analysis of cerebrospinal fluid (CSF) collected longitudinally in research participants in the Dominantly Inherited Alzheimer Network (DIAN), a multicenter, international biomarker study of ADAD, revealed reductions in amyloid-β1–42 (indicating the presence of amyloid plaques) and increases in markers of neuronal injury (tau, ptau181, and VILIP-1) in mutation carriers during the early presymptomatic stage. However, concentrations of injury-related markers in carriers at later stages of the disease decreased over time, suggesting a slowing of acute neurodegenerative processes with symptomatic disease progression. If corroborated, this longitudinal pattern of neurodegeneration-related biomarker change will likely influence the definition and interpretation of a positive versus negative effect of a therapy on disease progression. Clinicopathological evidence suggests that the pathology of Alzheimer’s disease (AD) begins many years before the appearance of cognitive symptoms. Biomarkers are required to identify affected individuals during this asymptomatic (“preclinical”) stage to permit intervention with potential disease-modifying therapies designed to preserve normal brain function. Studies of families with autosomal-dominant AD (ADAD) mutations provide a unique and powerful means to investigate AD biomarker changes during the asymptomatic period. In this biomarker study, we collected cerebrospinal fluid (CSF), plasma, and in vivo amyloid imaging cross-sectional data at baseline in individuals from ADAD families enrolled in the Dominantly Inherited Alzheimer Network. Our study revealed reduced concentrations of CSF amyloid-β1–42 (Aβ1–42) associated with the presence of Aβ plaques, and elevated concentrations of CSF tau, ptau181 (phosphorylated tau181), and VILIP-1 (visinin-like protein-1), markers of neurofibrillary tangles and neuronal injury/death, in asymptomatic mutation carriers 10 to 20 years before their estimated age at symptom onset (EAO) and before the detection of cognitive deficits. When compared longitudinally, however, the concentrations of CSF biomarkers of neuronal injury/death within individuals decreased after their EAO, suggesting a slowing of acute neurodegenerative processes with symptomatic disease progression. These results emphasize the importance of longitudinal, within-person assessment when modeling biomarker trajectories across the course of the disease. If corroborated, this pattern may influence the definition of a positive neurodegenerative biomarker outcome in clinical trials.

Data sharing in neuroimaging research

Significant resources around the world have been invested in neuroimaging studies of brain function and disease. Easier access to this large body of work should have profound impact on research in cognitive neuroscience and psychiatry, leading to advances in the diagnosis and treatment of psychiatric and neurological disease. A trend toward increased sharing of neuroimaging data has emerged in recent years. Nevertheless, a number of barriers continue to impede momentum. Many researchers and institutions remain uncertain about how to share data or lack the tools and expertise to participate in data sharing. The use of electronic data capture (EDC) methods for neuroimaging greatly simplifies the task of data collection and has the potential to help standardize many aspects of data sharing. We review here the motivations for sharing neuroimaging data, the current data sharing landscape, and the sociological or technical barriers that still need to be addressed. The INCF Task Force on Neuroimaging Datasharing, in conjunction with several collaborative groups around the world, has started work on several tools to ease and eventually automate the practice of data sharing. It is hoped that such tools will allow researchers to easily share raw, processed, and derived neuroimaging data, with appropriate metadata and provenance records, and will improve the reproducibility of neuroimaging studies. By providing seamless integration of data sharing and analysis tools within a commodity research environment, the Task Force seeks to identify and minimize barriers to data sharing in the field of neuroimaging.