Although amyloid beta (Aβ) imaging is widely used for diagnosing and monitoring Alzheimer’s disease in clinical fields, paralleling comparison between ¹⁸F-flutemetamol and ¹⁸F-florbetaben was rarely attempted in AD mouse model. We performed a comparison of Aβ PET images between ¹⁸F-flutemetamol and ¹⁸F-florbetaben in a recently developed APPswe mouse model, C57BL/6-Tg (NSE-hAPPsw) Korl. After an injection (0.23 mCi) of ¹⁸F-flutemetamol and ¹⁸F-florbetaben at a time interval of 2–3 days, we compared group difference of SUVR and kinetic parameters between the AD (n = 7) and control (n = 7) mice, as well as between ¹⁸F-flutemetamol and ¹⁸F-florbetaben image. In addition, bio-distribution and histopathology were conducted. With visual image and VOI-based SUVR analysis, the AD group presented more prominent uptake than did the control group in both the ¹⁸F-florbetaben and ¹⁸F-flutemetamol images. With kinetic analysis, the ¹⁸F-florbetaben images showed differences in K1 and k4 between the AD and control groups, although ¹⁸F-flutemetamol images did not show significant difference. ¹⁸F-florbetaben images showed more prominent cortical uptake and matched well to the thioflavin S staining images than did the ¹⁸F-flutemetamol image. In contrast, ¹⁸F-flutemetamol images presented higher K1, k4, K1/k2 values than those of ¹⁸F-florbetaben images. Also, ¹⁸F-flutemetamol images presented prominent uptake in the bowel and bladder, consistent with higher bio-distribution in kidney, lung, blood and heart. Compared with ¹⁸F-flutemetamol images, ¹⁸F-florbetaben images showed prominent visual uptake intensity, SUVR, and higher correlations with the pathology. In contrast, ¹⁸F-flutemetamol was more actively metabolized than was ¹⁸F-florbetaben (Son et al. in J Nucl Med 58(Suppl 1):S278, 2017 ].

Background: Macro laser light‐sheet illuminating microscopy (Macro‐LSFM), allied with tissue clearing technologies, herald a transformative paradigm in biomedical imaging, allowing 3D visualization of neuropathologic networks in a transparent intact mouse brain. Moreover, although traditional focus of AD diagnostic has been on CNS pathology, emerging research points to peripheral amyloid‐beta deposition, specifically in the eyeball, as an avenue for investigation. Coupled with conventional [¹⁸F]flutemetamol PET/CT imaging, this study leverages the innovative imaging capabilities of Macro‐LSFM with hydrophilic tissue‐clearing technique to elucidate the 3D spatial distribution of AD‐associated neuroinflammation and neuropathologic change both in the brain and eyeball of old transgenic AD mouse.Method: For thirteen 5xFAD (43 weeks) and seven control mice, we applied a hydrophilic tissue clearing pipelines to extracted brain and eyeball samples, encompassing fixation, clearing, permeabilization, and volume immunostaining using fluorochrome‐conjugated Thioflavin S for β‐amyloid (488 nm), anti‐CD11b for microglia (561 nm), and anti‐ACSA‐2 for astrocytes (647 nm). Macro‐LSFM imaging was performed using a Lightsheet Z.1 microscope (Objective 5x NA 0.16, Zoom 0.75x, single‐mode illumination). 3D volumetric surface models for amyloid, astrocytes, and microglia mapping were generated using Imaris v9.6 software with a minimum surface grain size of 4.0 μm.Result: In Macro‐LSFM, AD showed significantly higher total surface volumes of amyloid accumulation than control (AD, 898634368 µm³ [383355488‐1324986752]; Control, 33320178 µm³ [11156785‐65390988], p=0.0006). Eyeballs of AD also showed higher amyloid deposits than control (AD, 51091002 µm³ [1002006‐97460784]; Control, 229293 µm³ [8466‐3501462], p=0.0066), showing strong positive correlation of amyloid accumulation between brain and eyeball (R=0.8, p=0.001). AD showed higher anti‐ACSA‐2 specific astrocyte (AD, 59064360 µm³ [27815500‐222619280]; Control 20272722 µm³ [9317288‐27223352], p=0.0047) and anti‐CD11b specific microglial expression (AD, 51210100 µm³ [15309118‐135532144]; Control, 23461593 µm³ [14499170‐27924110], p=0.0162) than control.Conclusion: Our synergistic amyloid PET/LSFM approach revealed that active neuroinflammation persists in old AD brain, marked by substantial amyloid deposition, challenging the traditional sequence of early neuroinflammation leading to late‐stage neurodegeneration. Our novel observation of correlated ocular amyloid deposition introduces the eye as a promising, accessible site for the early diagnosis of AD pathology.

Background: We aim to investigate how genetic and modifiable lifestyle factors influence the interindividual variability in longitudinal pathological and neurodegeneration trajectories of in ADAD, focusing on resistance—defined as slower pathological accumulation—and resilience, the capacity to sustain slower neurodegeneration and cognitive decline despite similar pathological accumulation. Method: In the Dominantly Inherited Alzheimer Network observational cohort ( n = 529, January 2009–June 2018), we analyzed longitudinal clinical, CSF, imaging, and lifestyle assessments of 189 carriers (≥2 visits). Linear mixed‐effects models estimated biomarker change by estimated years to symptom onset (EYO). Group‐based trajectory modeling (GBTM) identified latent subgroups for CSF _p‐tau181 , CSF _Aβ1‐42, PiB PET, and FDG PET, with preceding pathological stages modeled using CSF and PiB PET and subsequent progression tracked through hippocampal volume and CDR‐SOB. Subgroups with slower versus faster neurodegeneration, despite similar pathological accumulation, were compared to assess associations with genetic/lifestyles. Result: In CSF _p‐tau181 , three trajectories were identified—high‐peak, inverse‐U (15.73%), intermediate (45.51%), and stable shape (38.76%; high‐resistance), with the high‐resistance group differing from the low‐resistance groups in APOE4, family mutation and low‐intensity exercise. In CSF _Aβ1‐42 , the high‐resistance group (high‐peak, decreasing shape) differed in APOE4, family mutation, and biking. Despite comparable preceding pathological trajectories (CSF _p‐tau181 , CSF _Aβ1‐42 , PiB PET), the high‐resilience (“high, inverse‐U”) latent class identified by FDG[precuneus] exhibited a more gradual decline in hippocampal volume (F[8, 202.97]=15.551; p <0.001) and CDR‐SOB (F[8, 197.49]=34.700; p <0.001) and, after controlling for family mutation, EYO and APOE, was more likely to engage in life experiences associated with greater openness to experience (OR 0.956; 95% CI, 0.920–0.994; p = 0.025), than the low‐resilience (“rapid, decreasing shape”) group. In FDG[AD signature], the low‐resilience group was more likely to live with others (8.113; 1.834–35.894; p = 0.006). Conclusion: In ADAD, longitudinal trajectories of pathological accumulation varied by resistance, revealing considerable interindividual variability. Despite similar pathological accumulation, longitudinal trajectories of functional neurodegeneration differed by the degree of resilience, with higher resilience linked to slower neurodegeneration and cognitive deterioration. Resistance was primarily associated with genetic mutations and physical activity, while resilience was influenced by modifiable lifestyle factors, including living arrangements and greater openness to experience, highlighting distinct genetic and lifestyle contributions in determining resistance and resilience in ADAD.