Evaluating the symptomatic progression of mild cognitive impairment (MCI) caused by Alzheimer disease (AD) is practically accomplished by tracking performance on cognitive tasks, such as the Alzheimer Disease Assessment Scale’s cognitive subscale (ADAS_cog), the Mini-Mental Status Examination (MMSE), and the Functional Activities Questionnaire (FAQ). The longitudinal relationships between cognitive decline and metabolic function as assessed using ¹⁸F-FDG PET are needed to address both the cognitive and the biologic progression of disease state in individual subjects. We conducted an exploratory investigation to evaluate longitudinal changes in brain glucose metabolism of individual subjects and their relationship to the subject’s changes of cognitive status. Methods: We describe a method to determine correlations in ¹⁸F-FDG spatial distribution over time. This parameter is termed the regional ¹⁸F-FDG time correlation coefficient (rFTC). By using linear mixed-effects models, we determined the difference in the rFTC decline rate between controls and subjects at high risk of developing AD, such as individuals with MCI or the presence of apolipoprotein E (APOE)–ε4 allele. The association between each subject’s rFTC and performance on cognitive tests (ADAS_cog, MMSE, and FAQ) was determined with 2 different correlation methods. All subject data were downloaded from the Alzheimer Disease Neuroimaging Initiative. Results: The rFTC values of controls remained fairly constant over time (−0.003 annual change; 95% confidence interval, −0.010–0.004). In MCI patients, the rFTC declined faster than in controls by an additional annual change of −0.02 (95% confidence interval, −0.030 to −0.010). In MCI patients, the decline in rFTC was associated with cognitive decline (ADAS_cog, P = 0.011; FAQ, P = 0.0016; MMSE, P = 0.004). After a linear effect of time was accounted for, visit-to-visit changes in rFTC correlated with visit-to-visit changes in all 3 cognitive tests. Conclusion: Longitudinal changes in rFTC detect subtle metabolic changes in individuals associated with variations in their cognition. This analytic tool may be useful for a patient-based monitoring of cognitive decline.

Background: Short-term fall prediction models that use electronic health records (EHRs) may enable the implementation of dynamic care practices that specifically address changes in individualized fall risk within senior care facilities. Objective: The aim of this study is to implement machine learning (ML) algorithms that use EHR data to predict a 3-month fall risk in residents from a variety of senior care facilities providing different levels of care. Methods: This retrospective study obtained EHR data (2007-2021) from Juniper Communities’ proprietary database of 2785 individuals primarily residing in skilled nursing facilities, independent living facilities, and assisted living facilities across the United States. We assessed the performance of 3 ML-based fall prediction models and the Juniper Communities’ fall risk assessment. Additional analyses were conducted to examine how changes in the input features, training data sets, and prediction windows affected the performance of these models. Results: The Extreme Gradient Boosting model exhibited the highest performance, with an area under the receiver operating characteristic curve of 0.846 (95% CI 0.794-0.894), specificity of 0.848, diagnostic odds ratio of 13.40, and sensitivity of 0.706, while achieving the best trade-off in balancing true positive and negative rates. The number of active medications was the most significant feature associated with fall risk, followed by a resident’s number of active diseases and several variables associated with vital signs, including diastolic blood pressure and changes in weight and respiratory rates. The combination of vital signs with traditional risk factors as input features achieved higher prediction accuracy than using either group of features alone. Conclusions: This study shows that the Extreme Gradient Boosting technique can use a large number of features from EHR data to make short-term fall predictions with a better performance than that of conventional fall risk assessments and other ML models. The integration of routinely collected EHR data, particularly vital signs, into fall prediction models may generate more accurate fall risk surveillance than models without vital signs. Our data support the use of ML models for dynamic, cost-effective, and automated fall predictions in different types of senior care facilities.

Background: Semiquantitative methods such as the standardized uptake value ratio (SUVR) require normalization of the radiotracer activity to a reference tissue to monitor changes in the accumulation of amyloid-β (Aβ) plaques measured with positron emission tomography (PET). The objective of this study was to evaluate the effect of reference tissue normalization in a test–retest ¹⁸F-florbetapir SUVR study using cerebellar gray matter, white matter (two different segmentation masks), brainstem, and corpus callosum as reference regions. Methods: We calculated the correlation between ¹⁸F-florbetapir PET and concurrent cerebrospinal fluid (CSF) Aβ_1–42 levels in a late mild cognitive impairment cohort with longitudinal PET and CSF data over the course of 2 years. In addition to conventional SUVR analysis using mean and median values of normalized brain radiotracer activity, we investigated a new image analysis technique—the weighted two-point correlation function (wS₂)—to capture potentially more subtle changes in Aβ-PET data. Results: Compared with the SUVRs normalized to cerebellar gray matter, all cerebral-to-white matter normalization schemes resulted in a higher inverse correlation between PET and CSF Aβ_1–42, while the brainstem normalization gave the best results (high and most stable correlation). Compared with the SUVR mean and median values, the wS₂ values were associated with the lowest coefficient of variation and highest inverse correlation to CSF Aβ_1–42 levels across all time points and reference regions, including the cerebellar gray matter. Conclusions: The selection of reference tissue for normalization and the choice of image analysis method can affect changes in cortical ¹⁸F-florbetapir uptake in longitudinal studies.

Introduction We examined networks of tau connectivity between brain regions based on correlations of their [¹⁸F]flortaucipir positron emission tomography (PET) uptake to evaluate sex‐specific differences in brain‐wide tau propagation. Methods PET data of clinically normal and mild cognitive impairment (MCI) subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to examine differences in network architectures across the groups. Results The tau‐based network architecture resembled progression of tauopathy from Braak stage I to VI regions. Compared to men, women had higher network density and an increased number of direct regional connections in co‐occurrence with increased brain‐wide tau burden, particularly at MCI. Several regions, including superior parietal lobe and parahippocampus served as connecting bridges between communities at different Braak stages. Discussion Network characteristics in women may favor an accelerated brain‐wide tau spread leading to a higher tau burden in women than men with MCI with implications for the greater female preponderance in Alzheimer's disease diagnosis.

We have designed a multi-pinhole collimator for a dual-headed, stationary SPECT system that incorporates high-resolution silicon double-sided strip detectors. The compact camera design of our system enables imaging at source-collimator distances between 20 and 30 mm. Our analytical calculations show that using knife-edge pinholes with small-opening angles or cylindrically shaped pinholes in a focused, multi-pinhole configuration in combination with this camera geometry can generate narrow sensitivity profiles across the field of view that can be useful for imaging small objects at high sensitivity and resolution. The current prototype system uses two collimators each containing 127 cylindrically shaped pinholes that are focused toward a target volume. Our goal is imaging objects such as a mouse brain, which could find potential applications in molecular imaging.