Virginia Research Day 2021

NOVEL MRI TECHNIQUES IDENTIFYING VASCULAR LEAK AND PARAVASCULAR FLOW REDUCTION IN EARLY ALZHEIMER DISEASE McNichols, Courtney, 2023; Hall, Colton, 2023; Orciuolo, Jason, 2023; Young, Amelia, 2023; Trenton, Judd, 2023; Daugherty, Daniel, 2023; Joseph, Charles MD, LUCOM, Lynchburg, VA

Abstract

Going Forward

These risk factors lead to a breakdown in the BBB, resulting in a slow vascular leak of fluid into the interstitial space. This slow leak causes impaired paravascular fluid clearance leading to accumulation of toxic biomarkers. Impaired clearance is in part due to injured pericytes and retraction of aquaporin-4 channels from the astrocyte foot process. In combination these pathologies lead to accumulation of toxins and production of misfolded proteins, resulting in neurodegeneration (Figure 1.b). Our aim is to use PASL MRI as a screening technique to compare paravascular fluid clearance in high-risk and control patients. PASL MRI will provide a means for clinicians to identify and monitor the results of clinical trials in preclinical AD patients.

There is a need to develop a treatment in early stages of AD, during the preclinical stages when A β and Hpt may not be detectable using serologic or PET imaging (Table 3). By the time these are detected by current methods, treatment is mostly ineffective. PASL MRI may be a viable method for identifying preclinical disease state, although further validation is needed to determine its efficacy as a screening technique for AD. PASL is a novel non-invasive MRI technique (no contrast agent) with a relatively quick procedural time (15-20 mins). Whereas DCI is invasive, uses contrast dye, and is more time consuming. PASL MRI is a less invasive method that will help maintain patient participation and a high rate of compliance. Future studies should screen high-risk populations to find a clearance rate that is indicative of progressing neurologic disorder by comparing clearance rates with cognitive interviews and testing. Data from our pilot study suggests that AD patients have an impaired glymphatic flow. This PASL MRI methodology may identify preclinical AD and can serially monitor disease progression. This will allow a longitudinal study to be developed testing early treatment modalities and their effect on disease progression. Disease progression can then be monitored with PASL MRI plus the existing “A-T-N” methodology. 1. Jack, C. R., Bennett, D. A., Blennow, K., Carrillo, M. C., Feldman, H. H., Frisoni, G. B., … Dubois, B. (2016, August 2). A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology , Vol. 87, pp. 539–547. https://doi.org/10.1212/WNL.0000000000002923 2. Montagne, A., Zhao, Z., & Zlokovic, B. V. (2017, November 1). Alzheimer’s disease: A matter of blood-brain barrier dysfunction? Journal of Experimental Medicine , Vol. 214, pp. 3151–3169. https://doi.org/10.1084/jem.20171406 3. Barnes, S. R., Ng, T. S. C., Santa-Maria, N., Montagne, A., Zlokovic, B. V., & Jacobs, R. E. (2015). ROCKETSHIP: a flexible and modular software tool for the planning, processing and analysis of dynamic MRI studies. BMC Medical Imaging , 15 (1), 19. https://doi.org/10.1186/s12880-015-0062-3 4. Joseph, C. R., Benhatzel, C. M., Stern, L. J., Hopper, O. M., & Lockwood, M. D. (2020). Pilot study utilizing MRI 3D TGSE PASL (arterial spin labeling) differentiating clearance rates of labeled protons in the CNS of patients with early Alzheimer disease from normal subjects. Magnetic Resonance Materials in Physics, Biology and Medicine , 1–10. https://doi.org/10.1007/s10334-019-00818-3 5. Louveau, A., Plog, B. A., Antila, S., Alitalo, K., Nedergaard, M., & Kipnis, J. (2017). Understanding the functions and relationships of the glymphatic system and meningeal lymphatics. Journal of Clinical Investigation , 127 (9), 3210–3219. https://doi.org/10.1172/JCI90603 6. Khoury, R., & Ghossoub, E. (2019). Diagnostic biomarkers of Alzheimer’s disease: A state-of-the-art review. Biomarkers in Neuropsychiatry , 1 , 100005. https://doi.org/10.1016/j.bionps.2019.100005 7. Qiu, C., Kivipelto, M., & von Strauss, E. (2009). Epidemiology of Alzheimer's disease: occurrence, determinants, and strategies toward intervention. Dialogues in clinical neuroscience , 11 (2), 111–128. Conclusion Successful future AD treatment will require intervention in the preclinical state before neurodegeneration develops. Addressing the BBB leak and diminished glymphatic clearance may circumvent the disease process altogether. 3D PASL MRI and/or DCI demonstrating their occurrence could be added to the existing “A-T-N” pathologic criterion for assessing disease progression longitudinally in future targeted treatment trials. Note the progression of disease process from preclinical to clinically definite AD and the presence of associated biologic markers. + : positive test, - : negative test, ? : unknown at this time Table 3: References

With beta-amyloid (A β ) and Hp-tau (Hpt) antibody treatment trial failures, avenues directed to other facets of Alzheimer’s Disease (AD) pathophysiology are being explored to treat in the preclinical or early clinical state. Previous studies of the early AD process have established evidence of blood brain barrier (BBB) breakdown and impaired glymphatic (paravascular and interstitial) fluid waste clearance. These two dysfunctions, as components of AD, are reasonable candidates to explore for future treatments in high-risk patients. Ideally, human treatment trials require non or minimally invasive tools for quantifying improvements in BBB integrity and glymphatic fluid clearance, correlating with clinical outcomes. Since AD treatment trials require pathologic confirmation for diagnosis, established serologic, cerebral spinal fluid (CSF), and imaging biomarkers are utilized. Future treatment trials in longitudinal studies demand additional biomarkers to identify BBB leak and glymphatic flow reduction combined with existing pathologic and clinical biomarkers to assess results. Novel candidates for identifying BBB leak and delayed glymphatic clearance are high resolution dynamic contrast imaging (DCI) and non-invasive 3D pulsed arterial spin labeling (PASL) MRI. Dementia is a broad category of disease that affects the brain leading to a decline in cognitive function. The number of patients with dementia is on the rise annually. In 2016, the total number of patients with dementia was determined to be 40-50 million. Of patients with dementia, AD accounts for 65% of cases. Early diagnosis and treatment is crucial for these patients because irreversible damage has already occurred at the time of clinical manifestation. However, clinicians are currently limited in their ability to diagnose AD in the preclinical state. To date, researchers utilize the “A-T-N” method which include serologic and neuroimaging biomarkers to confirm AD diagnosis for entry into clinical trials (Table 1). These biomarkers alone do not indicate early onset of pathology of AD, but rather reflect pathologic manifestation of clinically apparent disease. Introduction

Figure 1: 1.a demonstrates normal anatomy and physiology. 1.b demonstrates the pathological progression to late-stage AD including the development of a BBB leak through tight junctions, the retraction of aquaporin- 4 channels with reduction in paravascular outflow, and accumulation of Aβ and Hpt.

Methods

Table 1:

Our method utilized 3D PASL MRI. Subjects consisted of four groups of cognitively healthy individuals, without chronic disease, aged 18-30 (n=6), 31- 50 (n=6), 51-70 (n=6), and 3 subjects clinically diagnosed with mild AD. Seven consecutive sequences at 200ms increments beginning at 2800ms post labeling were used. Region of interest tool standardizing volume for all sequences determined average signal, which was graphed, and linear regression analysis used with the slope representing the clearance rate (Table 2).

“A-T-N” criterion and tests currently accepted under each category for research related AD pathologic diagnosis (independent of clinical diagnosis).

Table 2:

AD patient clearance compared to 51-70 age range

Our study focuses on first screening the high-risk population for preclinical AD development using previously identified risk factors. Risk factors for AD include: • Aging

**

• APOE Ɛ4 allele and other genetic factors • Hypertension and Hypercholesterolemia • Midlife and long duration Diabetes Mellitus

• Obesity and high BMI • Traumatic Brain Injury

**All values statistically significant except for left dominant temporal lobe.

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