{"doi":"10.1002/alz.12216","title":"MarkVCID cerebral small vessel consortium: II. Neuroimaging protocols","abstract":"Abstract The MarkVCID consortium was formed under cooperative agreements with the National Institute of Neurologic Diseases and Stroke (NINDS) and National Institute on Aging (NIA) in 2016 with the goals of developing and validating biomarkers for the cerebral small vessel diseases associated with the vascular contributions to cognitive impairment and dementia (VCID). Rigorously validated biomarkers have consistently been identified as crucial for multicenter studies to identify effective strategies to prevent and treat VCID, specifically to detect increased VCID risk, diagnose the presence of small vessel disease and its subtypes, assess prognosis for disease progression or response to treatment, demonstrate target engagement or mechanism of action for candidate interventions, and monitor disease progression during treatment. The seven project sites and central coordinating center comprising MarkVCID, working with NINDS and NIA, identified a panel of 11 candidate fluid‐ and neuroimaging‐based biomarker kits and established harmonized multicenter study protocols (see companion paper “MarkVCID cerebral small vessel consortium: I. Enrollment, clinical, fluid protocols” for full details). Here we describe the MarkVCID neuroimaging protocols with specific focus on validating their application to future multicenter trials. MarkVCID procedures for participant enrollment; clinical and cognitive evaluation; and collection, handling, and instrumental validation of fluid samples are described in detail in a companion paper. Magnetic resonance imaging (MRI) has long served as the neuroimaging modality of choice for cerebral small vessel disease and VCID because of its sensitivity to a wide range of brain properties, including small structural lesions, connectivity, and cerebrovascular physiology. Despite MRI's widespread use in the VCID field, there have been relatively scant data validating the repeatability and reproducibility of MRI‐based biomarkers across raters, scanner types, and time intervals (collectively defined as instrumental validity). The MRI protocols described here address the core MRI sequences for assessing cerebral small vessel disease in future research studies, specific sequence parameters for use across various research scanner types, and rigorous procedures for determining instrumental validity. Another candidate neuroimaging modality considered by MarkVCID is optical coherence tomography angiography (OCTA), a non‐invasive technique for directly visualizing retinal capillaries as a marker of the cerebral capillaries. OCTA has theoretical promise as a unique opportunity to visualize small vessels derived from the cerebral circulation, but at a considerably earlier stage of development than MRI. The additional OCTA protocols described here address procedures for determining OCTA instrumental validity, evaluating sources of variability such as pupil dilation, and handling data to maintain participant privacy. MRI protocol and instrumental validation The core sequences selected for the MarkVCID MRI protocol are three‐dimensional T1‐weighted multi‐echo magnetization‐prepared rapid‐acquisition‐of‐gradient‐echo (ME‐MPRAGE), three‐dimensional T2‐weighted fast spin echo fluid‐attenuated‐inversion‐recovery (FLAIR), two‐dimensional diffusion‐weighted spin‐echo echo‐planar imaging (DWI), three‐dimensional T2*‐weighted multi‐echo gradient echo (3D‐GRE), three‐dimensional T 2 ‐weighted fast spin‐echo imaging (T2w), and two‐dimensional T2*‐weighted gradient echo echo‐planar blood‐oxygenation‐level‐dependent imaging with brief periods of CO 2 inhalation (BOLD‐CVR). Harmonized parameters for each of these core sequences were developed for four 3 Tesla MRI scanner models in widespread use at academic medical centers. MarkVCID project sites are trained and certified for their instantiation of the consortium MRI protocols. Sites are required to perform image quality checks every 2 months using the Alzheimer's Disease Neuroimaging Init","journal":"Alzheimer s & Dementia","year":2021,"id":151510,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":82,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.6564,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":382741,"name":"Amir H. Kashani","orcid":"0000-0001-9767-7920","position":1,"is_corresponding":false},{"id":465094,"name":"Konstantinos Arfanakis","orcid":"0000-0001-9705-597X","position":2,"is_corresponding":false},{"id":515271,"name":"Arvind Caprihan","orcid":"0000-0002-4810-359X","position":3,"is_corresponding":false},{"id":277082,"name":"Charles DeCarli","orcid":"0000-0003-1914-2693","position":4,"is_corresponding":false},{"id":244995,"name":"Brian T. Gold","orcid":"0000-0002-6958-7095","position":5,"is_corresponding":false},{"id":644176,"name":"Yang Li","orcid":"0009-0003-4011-2816","position":6,"is_corresponding":false},{"id":277081,"name":"Pauline Maillard","orcid":"0000-0003-3516-6345","position":7,"is_corresponding":false},{"id":245362,"name":"Claudia L. Satizábal","orcid":"0000-0002-1115-4430","position":8,"is_corresponding":false},{"id":645315,"name":"Lara Stables","orcid":null,"position":9,"is_corresponding":false},{"id":244996,"name":"Danny J.J. Wang","orcid":"0000-0002-0840-7062","position":10,"is_corresponding":false},{"id":425661,"name":"Roderick A. Corriveau","orcid":"0000-0002-6954-9240","position":11,"is_corresponding":false},{"id":644177,"name":"Herpreet Singh","orcid":"0000-0003-3797-7301","position":12,"is_corresponding":false},{"id":268016,"name":"Eric E. Smith","orcid":"0000-0003-3956-1668","position":13,"is_corresponding":false},{"id":19604,"name":"Bruce Fischl","orcid":"0000-0002-2413-1115","position":14,"is_corresponding":false},{"id":63232,"name":"André van der Kouwe","orcid":"0000-0002-2754-6594","position":15,"is_corresponding":false},{"id":466210,"name":"Kristin Schwab","orcid":"0000-0001-5723-9109","position":16,"is_corresponding":false},{"id":644178,"name":"Karl G. Helmer","orcid":"0000-0002-5113-6843","position":17,"is_corresponding":false},{"id":251653,"name":"Steven M. Greenberg","orcid":"0000-0003-1792-8887","position":18,"is_corresponding":false},{"id":57482,"name":"Hanzhang Lu","orcid":"0000-0003-3871-1564","position":0,"is_corresponding":true}],"reference_count":23,"raw_metadata":null,"created_at":"2026-07-18T23:43:16.823357Z","pmid":"33480157","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}