A recommended “minimum data set” framework for SD‐OCT retinal image acquisition and analysis from the Atlas of Retinal Imaging in Alzheimer's Study (ARIAS)

Abstract Introduction We propose a minimum data set framework for the acquisition and analysis of retinal images for the development of retinal Alzheimer's disease (AD) biomarkers. Our goal is to describe methodology that will increase concordance across laboratories, so that the broader research community is able to cross‐validate findings in parallel, accumulate large databases with normative data across the cognitive aging spectrum, and progress the application of this technology from the discovery stage to the validation stage in the search for sensitive and specific retinal biomarkers in AD. Methods The proposed minimum data set framework is based on the Atlas of Retinal Imaging Study (ARIAS), an ongoing, longitudinal, multi‐site observational cohort study. However, the ARIAS protocol has been edited and refined with the expertise of all co‐authors, representing 16 institutions, and research groups from three countries, as a first step to address a pressing need identified by experts in neuroscience, neurology, optometry, and ophthalmology at the Retinal Imaging in Alzheimer's Disease (RIAD) conference, convened by the Alzheimer's Association and held in Washington, DC, in May 2019. Results Our framework delineates specific imaging protocols and methods of analysis for imaging structural changes in retinal neuronal layers, with optional add‐on procedures of fundus autofluorescence to examine beta‐amyloid accumulation and optical coherence tomography angiography to examine AD‐related changes in the retinal vasculature. Discussion This minimum data set represents a first step toward the standardization of retinal imaging data acquisition and analysis in cognitive aging and AD. A standardized approach is essential to move from discovery to validation, and to examine which retinal AD biomarkers may be more sensitive and specific for the different stages of the disease severity spectrum. This approach has worked for other biomarkers in the AD field, such as magnetic resonance imaging; amyloid positron emission tomography; and, more recently, blood proteomics. Potential context of use for retinal AD biomarkers is discussed.

Because the retina is an extension of the central nervous system, the development and validation of such biomarkers would allow for a non-invasive, inexpensive, and relatively rapid method for the routine screening of AD risk and progression in large populations of older adults. 2 Given the lack of preventative or disease-modifying therapies for AD, and the fact that the majority of adults over the age of 40 see an eye care professional intermittently for preventive eye health and vision examinations, correction of refractive error (including presbyopia), and/or ongoing monitoring of ocular abnormalities, retinal screening could serve as a cost-effective, widely accessible method to address a major public health need. This first-ever international conference on retinal imaging in AD identified several key action items or next steps to move retinal AD biomarker research from its current position in the discovery phase to the validation phase. Perhaps the most critical of these is the need for at least initial agreement on basic standards for data acquisition and analyses, to allow increased opportunities for data sharing across research groups. This will allow researchers in the field to compare imaging data across study cohorts, and eventually would lead to the creation of a widely available comparative reference database, along the lines of the Alzheimer's Disease Neuroimaging Initiative (ADNI [3][4][5] ) and the Australian Imaging Biomarkers and Lifestyle Study (AIBL 6 ) among other large-scale longitudinal observational trials. These landmark studies have led to massive improvements in how we acquire and interpret neuroimaging and biofluids markers for the disease.
Here, we propose a framework for a "minimum data set" for use in retinal imaging and signal processing across laboratories, based on our collective experience and an exhaustive decision-making process that led to the design of a current multi-site longitudinal observational trial, led by two of us (P.J.S. and S.S.), the Atlas of Retinal Imaging in Alzheimer's Study (ARIAS) that launched recruitment of participants in January 2020. We began with this protocol, and sought input from international experts in this area (all co-authors) to reach a consensus on a recommended minimum data set. Our aim is to propose reproducible methods that can be used across laboratories to aggregate data, cross-validate findings, and accelerate the development of sensitive and specific retinal biomarkers for the early detection of Alzheimer's pathologic change. Although we are well aware that the field is not yet ready to reach a consensus on methods, and that site-specific differences in the actual optical coherence tomography (OCT) measurements of retinal markers exist due to proprietary differences in manufacturing technologies and processing software.
While this cannot be solved with the suggestion of this framework, we are hopeful that publishing this methodology serves as a first step toward finding common approaches that will improve our ability to harmonize data and to cross-validate results across research centers dedicated to the discovery and validation of retinal biomarkers of AD.

1.1
A proposed framework for a minimum data set in AD biomarker research: spectral domain optical coherence tomography (SD-OCT) image collection and processing We propose a framework that encompasses structural SD-OCT imaging of the optic disc and the macula, to capture volume and thickness changes of the peripapillary retinal nerve fiber layer (pRNFL), macular retinal nerve fiber layer (mRNFL), ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer (ONL), inner and outer photoreceptor segments (IS and OS), and the retinal pigment epithelium (RPE).
Because structural changes in the retina in AD have been robustly supported by the growing literature on this topic over the past decade 2 and all SD-OCT devices support structural retinal imaging, we propose this as a core image acquisition and processing framework. We also propose some additional image acquisition and processing techniques: fundus autofluorescence imaging to examine protein changes and OCT-angiography (OCT-A) of the retinal microvasculature.
As noted above, these methods are being used in the ARIAS trial that is following a large cohort (N = 330) longitudinally for 5 years at three sites in Florida and Rhode Island (NCT# = 03862222). In addition to an extensive retinal imaging protocol, ARIAS will also collect genetic, cognitive, sleep, gait, functional, pupillometry, contrast sensitivity, brain imaging (amyloid positron emission tomography [PET] and magnetic resonance imaging [MRI], for a subset of participants), and blood proteomics data. After completion of data collection and initial analyses, we plan to make this database publicly available as a reference for other researchers, patterned after the data-sharing approach pioneered by ADNI.

Instrumental variability across centers and research groups
Multiple instrument manufacturers including, but not limited to, Zeiss, Heidelberg Engineering, Topcon, and Optovue, are all producing very high-quality SD-OCT imaging systems that are in wide clinical and research use worldwide. Although each of these systems produce very similar-looking and high-quality images, technical specifications do vary across these devices that lead to important differences in optics, in-plane resolution, the ability to map point-to-point over successive exams, and many other instrumental differences. Moreover, even

RESEARCH IN CONTEXT
1. Systematic review: The Alzheimer's Association, in partnership with this journal, hosted a first-ever international think-tank meeting on the topic of retinal imaging in Alzheimer's disease (AD) in May 2019, attended by 90 experts from nine countries 1 One of the key recommendations from the conference was to develop consensus on an initial "minimum data set approach" for future studies, to allow enhanced potential to compare, and/or to coalesce, data collected across multiple laboratories and clinics.

2.
Interpretation: This initial attempt at outlining a minimum data set, retinal imaging studies in AD, is based on a large trial that is currently in progress; but that protocol has now been edited and refined with the expertise of all co-authors, representing 16 institutions, and research groups from three countries.

Future directions:
We hope that this set of recommendations will be considered by, improved, further refined, and adopted by additional research groups and by the new Professional Interest Area (PIA) group of the Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART) that has just recently been launched as another major recommendation from the "Retinal Imaging in AD" conference referenced above. 1 within the same company and SD-OCT system, periodic improvements in their signal processing software packages result in subtle differences in segmentation algorithms, possibly creating an additional source of instrumental variability between labs and between patient visits. It is beyond the scope of this article to recommend how such sources of instrumental error should be addressed when coalescing data across labs, or even within the same center over time. However, we firmly believe that potential between-center main effect differences for vendor/imaging systems need to be thoroughly explored, with multiple research groups working cooperatively, to both collect and then share data sets that can be at least partially merged for statistical analyses, or at least used to derive mathematical equations for conversions between instruments, as has been done previously for diabetic retinopathy. 7 We are of the opinion that appropriate adjustments will be identified to allow data obtained on one manufacturer's system to be compared reliably to the same type of data obtained on other systems-and this work stream would clearly advance more rapidly with the cooperation of the manufacturers themselves. In the protocol we describe below, we have chosen to use the same Heidelberg Engineering, Inc. (Heidelberg, Germany) SPECTRALIS 2 OCT system at all three clinical sites. Hence, many of the specifications described below are specific to this particular vendor. We offer this as an example, and would encourage centers relying on imaging systems from other vendors to match the protocol below to accelerate the work of understanding these potential instrument differences.
We also acknowledge that several investigators in academia and industry are developing novel experimental technologies and engineering advances that may supersede some or all of the imaging technologies that we currently rely on, in terms of sensitivity and specificity for AD risk biomarkers (cf. Liu et al. 8 Yap et al., 9 and Braaf et al. 10 ). Here, we focus on current widely available clinical tools that can be deployed by point-of-care clinicians for validation of retinal biomarkers in the nearterm future.

Quality control and quality assurance
The veracity of biomarker research, of any type, is utterly dependent on study subject selection and ensuring consistent assurance and quality control (QA/QC), across clinical sites, across technicians and research staff who are collecting data, and across time for within-subjects longitudinal studies. This important set of issues also lies beyond the scope of this article, but the ability to trust any of the retinal imaging biomarkers that we describe below is nonetheless dependent on study designs that effectively ensure proper QA/QC of the data obtained.

Structural imaging protocol
The SD-OCT procedures outlined below are currently being performed using the SPECTRALIS HRA + OCT hardware and the Eye Explorer the image quality is less than optimal for a single eye.
A common approach in retinal imaging research is to rely on a random assignment for each subject enrolled in a given study, to choose either the right eye (oculus dexter, OD) or left eye (oculus sinister, OS) for data analyses. In practice, the decision to use data from OD versus OS is dependent on the image quality for each eye, participant cooperation during the scanning process, and the presence or absence of ocular pathologies (in each eye) that might adversely impact the quality of the SD-OCT measurements. In Table 1, we provide a listing of ocular and retinal pathologies that, if of sufficient severity so as to limit the quality of imaging data, would result in choosing one eye over the other (or, in the presence of substantial bilateral ocular disease, would exclude a participant from the study altogether). In at least some cases, ophthalmic evaluation is necessary to evaluate the disease severity and to develop reproducible inclusion and exclusion criteria.
For structural OCT imaging, to avoid inter-and intra-subject thickness measurement biases due to axial length, 11 we correct using a modified Littman's formula, which is currently in use by several investigators in this field. 12 For sites unable to measure axial length, participants with <or >5.0 diopters native spherical equivalent should be excluded.
Additionally, although regulations surrounding storage of demographic information vary regionally, nationally, and internationally, we recommend collecting the following demographic variables as a minimum: age, years of education, race, sex. All of these factors can affect retinal morphology and/or cognition in AD, and may be required as covariates in analyses examining between-or within-subject comparisons.

Outcome variables and analysis
pRNFL thickness is the variable of interest with respect to optic disc imaging, due to the evidence in the literature indicating changes to the pRNFL in AD. 13

Structural SD-OCT imaging at the macula
Our standard protocol uses the preset posterior-pole imaging module on the SPECTRALIS, with fixation on the fovea. This protocol TA B L E 1 List of ocular pathologies that influences choice of either eye or exclusion of participants altogether in the event of a bilateral condition for the purpose of image analysis

Outcome variables and analysis
Most software to support the OCT systems offered by any major vendor, including Heidelberg's HEYEX software, will automatically

Retinal amyloid beta (Aβ): autofluorescence imaging
Autofluorescence imaging is available within the Heidelberg SPEC-TRALIS system to examine the presence of Aβ in the retinal neuronal layers. We use a 55˚widefield lens to acquire fundus autofluorescence (FAF) images of the central, superior, and inferior retina of all study participants using SPECTRALIS confocal scanning laser ophthalomoscopy (cSLO). The wider lens is used to capture a larger surface area and to search for putative evidence of amyloidosis in the periphery.
If this lens is not available, use of the standard lens is appropriate for this data, as long as the lens and field of view are specified at the time of dissemination of results. The composite image is then evaluated for number and surface area of inclusion bodies suspected to contain amyloid proteins. 21,22 Retinal inclusion bodies have been previously found in the retina of preclinical AD research participants, with the number and surface area of these inclusion bodies being moderately correlated with a standard index of amyloid aggregation on PET brain imaging. 23 In symptomatic AD patients, fundus autofluorescence with SLO has been used to quantify retinal amyloid in vivo, and confirmed by autopsy. [24][25][26]

Outcome variables and analysis
We use the Heidelberg Explorer (HE) region finder tool of the HEYEX software to compute the number count and total surface area of the inclusion bodies in the autofluorescence images. This tool allows the operator to place a seed in an area of interest, and the software automatically computes the boundary of the object. On visual inspection, the rater has the ability to modify the boundaries as required.
All images are read by two qualified and independent raters, and we exclude the 360-degree region within 1 disc diameter from the center of the optic disc and the 360-degree macular region within 1 disc diameter of the foveal center. This is done to avoid including the area of hypopigmentation around the optic nerve in participants with zone α or β crescents or peripapillary atrophy and to avoid inclusion of age-related macular drusen in these two regions. Inclusion bodies are only counted if they reach 100% consensus ratings in two independent raters, who are blind to participant information, including demographics, medical history, and clinical status.

RETINAL ANGIOGRAPHIC CHANGES: OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY (OCT-A) IMAGING
We collect a macular centered 20˚× 20˚(6 × 6 mm) OCTA image consisting of 512 B-scans, with 12 micron spacing between B-scans, and five frames averaged per each B-scan location for each participant. This produces the superficial vascular complex (SVC) angiogram, defined as the composite retinal vascular plexus from the inner limiting membrane to the IPL using HEYEX software. Although not exactly the same, similar scan parameters are available on other commercial platforms as shown in Table 2.

Outcome variables and analysis
The outcome variables measured from the OCT-A images include foveal avascular zone (FAZ) size, capillary density, capillary nonperfusion area, and multifractal properties as measured by the generalized dimension and singularity spectra. All of these metrics appear to be influenced by the presence of AD. 27 Retinal layers include macular retinal nerve fiber layer (mRNFL), ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer (ONL), inner and outer photoreceptor segments (IS and OS), and the retinal pigment epithelium (RPE). vascular cognitive impairment or dementia. The methodology of this study will soon be available and will likely aid in the harmonization of angiography data. Below, we offer one approach that our group is focusing on, as these methods may be reproduced with relative ease using widely available software, as described below.
The HEYEX software lasso tool is used to delineate the borders of the FAZ, to allow for surface area measurement on the SVC image.
After this, we export the SVC images as tiff files into custom programming software (Matlab, Mathworks) for further image processing and analysis. First, a vesselness filter is applied to the images 30,31 to increase the probability of resolving a vessel at a specific location in the image when it is actually present versus noise or motion artifact. Next, the Otsu thresholding method 32 is applied to the resultant image to reduce background noise. After the thresholding, we use a customized program script 33

SUMMARY AND DISCUSSION
Here, we suggest a reproducible methodology to serve as a recom- We added additional methodology and outcome measures for the assessment of retinal amyloid, and retinal angiography. Although these latter imaging approaches currently require more specialized equipment that is less widely available, several laboratories are currently exploring both protein-related and angiographic retinal changes across the AD spectrum. The techniques used by laboratories in these areas vary, sometimes considerably, across laboratory groups. Our hope is this recommended minimum data set represent a first step toward galvanizing researchers to align protocols for retinal imaging biomarker research in AD.
To generate this recommended minimum data set, we began with a protocol developed in consultation with industrial and academic experts for and currently used in the ARIAS study (PIs: Snyder, Sinoff).
We consulted with national and international experts to expand upon and modify this protocol, resulting in the above recommended minimum data set for retinal imaging biomarker discovery research in AD.
Our goal is to increase methodological concordance across laboratories, so that the broader research community is able to cross-validate findings in parallel, accumulate large databases with normative data across the cognitive aging spectrum, and progress from the discovery stage to the validation stage in terms of retinal biomarker development for AD.
Although we believe this framework makes a start at filling an unmet need in the field, we understand that this is a first step in working toward reliable methodological harmonization across laboratories, and that many additional steps must be taken to achieve true methodological concordance across labs in academia, the clinic, and in industry. We acknowledge that this minimum data set will not necessarily be applicable across all devices and manufacturers, and that solving technological and technical variations, even subtle ones due to software variations in the same device, will require co-operation and data-sharing across federal and regulatory agencies, experts in academia and industry, and device manufacturers. Some of the software described in this minimum data set is not part of the "standard" Heidelberg software package (NSite Analytics, OCT-A, Widefield Imaging Module, HE Region Finder tool, see Table 2), and acquisition of this software incurs additional costs, which may not be feasible for all research laboratories. One potential strategy to expedite data sharing is providing access to real raw image data, without any post-processing. Image J, to decouple reliance on proprietary software and pre-/postprocessing. These platforms will help to advance the field toward sharing raw, unprocessed SD-OCT data and retinal images, especially in cases in which vendor specific software is not available or not affordable.
The field will need to address additional methodological barriers to produce validated, clinically meaningful and applicable biomarkers.
Both AD and cancer have seen success in public-private partnerships, which can be leveraged for the advancement of retinal biomarker validation in AD. As one example, O'Bryant et al. 41 proposed a pathway for the advancement of plasma biomarkers from discovery to clinic, including public-private partnerships, which could be applied to retinal biomarkers in the future. Finally, we expect future retinal biomarker discovery work in academia to modify our recommendations for a minimum data set framework as the pace and popularity of this work in AD increases. This framework is not meant to be exhaustive, but rather a starting point from which to move forward as a field. For example, methodological factors, such as lighting, 39,40 may influence retinal morphology. As such, standardization of lighting conditions may require standardization in the future, although whether this makes a significant difference in AD measurements remains to be systematically investigated.
Looking to the future, use of this minimum data set could spur efficient data accumulation and analysis in the field, including data examining within subjects, longitudinal change in cognitive aging, and AD. These data are essential to move from discovery to validation, and to examine which retinal AD biomarkers may be sensitive and specific for the different stages of the cognitive aging continuum. This approach has been successful in validating other biomarkers for AD, such as MRI, amyloid PET, and blood proteomics. Moreover, designating an appropriate context of use for structural, protein-related, and microvascular retinal biomarkers will be essential to move the field forward. There are many potential contexts of use for these biomarkers, including, but not limited to, point-of-care screening, risk for pre-clinical AD or risk of progression from MCI to AD, monitoring, diagnosis, or prognosis. Clinical applicability of these biomarkers will depend on context of use; for example, structural retinal biomarkers have been studied extensively in AD patients versus age-matched cognitively normal older adults; however, investigations of these biomarkers in the preclinical AD population remains limited. It is essential to determine at which stage of the cognitive aging continuum these retinal biomarkers are most reliable and accurate, and how they would most efficiently be deployed in clinical practice. In the future, retinal imaging could be deployed as a tool for large-scale screening of cognitively normal older adults.
As a field, our goals should center on precision medicine approaches for AD diagnosis and monitoring by minimizing clinician burden; maximizing clinical utility; and identifying individuals at risk for AD for further specialist evaluation and more invasive, expensive biomarker testing. Retinal biomarkers could play a pivotal role in large-scale screening, and serve as a first step in a multi-stage biomarker testing process, similar to other disease states such as cancer, cardiovascular disease, and diabetes. It is important to note that to truly validate retinal imaging as an AD screening tool, we will have to develop biomarkers that are specific to AD pathology. As health co-morbidities, specifically cardiovascular disease and diabetes, increase with age, it is not uncommon for older adults to present with multiple pathologies that affect the retina. However, aiming for a screening tool with high negative predictive value that can rule out low-risk older adults, will be a valuable first screening stage. Retinal biomarkers have the potential to limit use of invasive and costly tests, such as brain imaging and cerebrospinal fluid sampling, and will help to precisely identify those at high risk for AD when a disease-modifying therapy becomes available.

CONFLICTS OF INTEREST
Dr. A. Kashani is a consultant to Carl Zeiss Meditec, and he has received both consulting fees and grant support from that company. Dr. D.C.
DeBruc, with the University of Miami, has filed a provisional patent for a novel technology that is related to the topic of this article, although her specific invention is not reviewed herein. The authors have no other financial or material conflicts of interest to disclose.