Development of a decentralized cohort for studying post-acute sequelae of COVID-19 in India in the Data4life Study.

BACKGROUND: Decentralized, digital health studies can provide real-world evidence of the lasting effects of COVID-19 on physical, socioeconomic, psychological, and social determinant factors of health in India. Existing research cohorts, however, are small and were not designed for longitudinal collection of comprehensive data from India's diverse population. Data4Life is a nationwide, digitally enabled, health research initiative to examine the post-acute sequelae of COVID-19 across individuals, communities, and regions. Data4Life seeks to build an ethnically and geographically diverse population of at least 100,000 participants in India. METHODS: Here we discuss the feasibility of developing a completely decentralized COVID-19 cohort in India through qualitative analysis of data collection procedures, participant characteristics, participant perspectives on recruitment and reported study motivation. RESULTS: As of June 13th, 2022, more than 6,000 participants from 17 Indian states completed baseline surveys. Friend and family referral were identified as the most common recruitment method (64.8%) across all demographic groups. Helping family and friends was the primary reason reported for joining the study (61.5%). CONCLUSIONS: Preliminary findings support the use of digital technology for rapid enrollment and data collection to develop large health research cohorts in India. This demonstrates the potential for expansion of digitally enabled health research in India. These findings also outline the value of person-to-person recruitment strategies when conducting digital health research in modern-day India. Qualitative analysis reveals opportunities to increase diversity and retention in real time. It also informs strategies for improving participant experiences in the current Data4Life initiative and future studies.

Due to the vast geographical size and ethnic diversity of the population, India represents a huge challenge for conducting research studies. The Data4Life study was set up to understand if digital tools can be an effective way to study long-term effects of COVID-19 across India. We studied different ways of collecting the relevant information from participants, the background of each participant, reasons, and motivation of each participant for joining the study. The results showed that friend and family referrals were the most common recruitment reason. Helping family and friends was reported as the main motivation for joining the study. Overall, the findings support the use of digital tools as an effective recruitment method for research studies in India.

A Digital Health Initiative (COVIDsmart) for Remote Data Collection and Study of COVID-19's Impact on the State of Virginia: Prospective Cohort Study.

BACKGROUND: The COVID-19 pandemic has affected people's lives beyond severe and long-term physical health symptoms. Social distancing and quarantine have led to adverse mental health outcomes. COVID-19-induced economic setbacks have also likely exacerbated the psychological distress affecting broader aspects of physical and mental well-being. Remote digital health studies can provide information about the pandemic's socioeconomic, mental, and physical impact. COVIDsmart was a collaborative effort to deploy a complex digital health research study to understand the impact of the pandemic on diverse populations. We describe how digital tools were used to capture the effects of the pandemic on the overall well-being of diverse communities across large geographical areas within the state of Virginia. OBJECTIVE: The aim is to describe the digital recruitment strategies and data collection tools applied in the COVIDsmart study and share the preliminary study results. METHODS: COVIDsmart conducted digital recruitment, e-Consent, and survey collection through a Health Insurance Portability and Accountability Act-compliant digital health platform. This is an alternative to the traditional in-person recruitment and onboarding method used for studies. Participants in Virginia were actively recruited over 3 months using widespread digital marketing strategies. Six months of data were collected remotely on participant demographics, COVID-19 clinical parameters, health perceptions, mental and physical health, resilience, vaccination status, education or work functioning, social or family functioning, and economic impact. Data were collected using validated questionnaires or surveys, completed in a cyclical fashion and reviewed by an expert panel. To retain a high level of engagement throughout the study, participants were incentivized to stay enrolled and complete more surveys to further their chances of receiving a monthly gift card and one of multiple grand prizes. RESULTS: Virtual recruitment demonstrated relatively high rates of interest in Virginia (N=3737), and 782 (21.1%) consented to participate in the study. The most successful recruitment technique was the effective use of newsletters or emails (n=326, 41.7%). The primary reason for contributing as a study participant was advancing research (n=625, 79.9%), followed by the need to give back to their community (n=507, 64.8%). Incentives were only reported as a reason among 21% (n=164) of the consented participants. Overall, the primary reason for contributing as a study participant was attributed to altruism at 88.6% (n=693). CONCLUSIONS: The COVID-19 pandemic has accelerated the need for digital transformation in research. COVIDsmart is a statewide prospective cohort to study the impact of COVID-19 on Virginians' social, physical, and mental health. The study design, project management, and collaborative efforts led to the development of effective digital recruitment, enrollment, and data collection strategies to evaluate the pandemic's effects on a large, diverse population. These findings may inform effective recruitment techniques across diverse communities and participants' interest in remote digital health studies.

Early prediction of prostate cancer risk in younger men using polygenic risk scores and electronic health records.

BACKGROUND: Prostate cancer (PCa) screening is not routinely conducted in men aged 55 and younger, although this age group accounts for more than 10% of cases. Polygenic risk scores (PRSs) and patient data applied toward early prediction of PCa may lead to earlier interventions and increased survival. We have developed machine learning (ML) models to predict PCa risk in men 55 and under using PRSs combined with patient data. METHODS: We conducted a retrospective study on 91,106 male patients aged 35-55 using the UK Biobank database. Five gradient boosting models were developed and validated utilizing routine screening data, PRSs, additional clinical data, or combinations of the three. RESULTS: Combinations of PRSs and patient data outperformed models that utilized PRS or patient data only, and the highest performing models achieved an area under the receiver operating characteristic curve of 0.788. Our models demonstrated a substantially lower false positive rate (35.4%) in comparison to standard screening using prostate-specific antigen (60%-67%). CONCLUSION: This study provides the first preliminary evidence for the use of PRSs with patient data in a ML algorithm for PCa risk prediction in men aged 55 and under for whom screening is not standard practice.

Massive external validation of a machine learning algorithm to predict pulmonary embolism in hospitalized patients.

BACKGROUND: Pulmonary embolism (PE) is a life-threatening condition associated with ~10% of deaths of hospitalized patients. Machine learning algorithms (MLAs) which predict the onset of pulmonary embolism (PE) could enable earlier treatment and improve patient outcomes. However, the extent to which they generalize to broader patient populations impacts their clinical utility. OBJECTIVE: To conduct the first large-scale external validation of a machine learning-based PE prediction model which uses EHR data from the first three hours of a patient's hospital stay to predict the occurrence of PE within the next 10 days of the inpatient stay. METHODS: This retrospective study included approximately two million adult hospital admissions across 44 medical institutions in the US from 2011 to 2017. Demographics, vital signs, and lab tests from adult inpatients at 12 institutions (n = 331,268; 3.3% PE positive) were used for training an XGBoost model. External validation of the model was conducted on patient populations from each of 32 medical institutions (total n = 1,660,715; 3.7% PE positive) without retraining. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC). Backward elimination regression was used to identify correlations between characteristics of the external validation sets and AUROC. RESULTS: The model performed well (AUROC = 0.87) on the 20% hold-out subset of the training set. Despite demographic differences between the 32 external validation populations (percent PE positive: min = 1.54%, max = 6.47%), without retraining, the model had excellent discrimination, with a mean AUROC of 0.88 (min = 0.79, max = 0.93). Fixing sensitivity at 0.80, the model had a mean specificity of 0.85 (min = 0.64, max = 0.93). Backward elimination regression identified a negative association (ß = -0.015, p < 0.001) between the percentage of PE positive encounters and AUROC. CONCLUSIONS: A PE prediction model performed remarkably well across 32 different external patient populations without retraining and despite significant differences in demographic characteristics, demonstrating its generalizability and potential as a clinical decision support tool to aid PE detection and improve patient outcomes in a clinical setting.

Predicting Falls in Long-term Care Facilities: Machine Learning Study.

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.

Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm.

BACKGROUND: Strokes represent a leading cause of mortality globally. The evolution of developing new therapies is subject to safety and efficacy testing in clinical trials, which operate in a limited timeframe. To maximize the impact of these trials, patient cohorts for whom ischemic stroke is likely during that designated timeframe should be identified. Machine learning may improve upon existing candidate identification methods in order to maximize the impact of clinical trials for stroke prevention and treatment and improve patient safety. METHODS: A retrospective study was performed using 41,970 qualifying patient encounters with ischemic stroke from inpatient visits recorded from over 700 inpatient and ambulatory care sites. Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. The primary outcome of interest was the occurrence of ischemic stroke. RESULTS: After training for optimization, XGBoost obtained a specificity of 0.793, a positive predictive value (PPV) of 0.194, and a negative predictive value (NPV) of 0.985. The MLA further obtained an area under the receiver operating characteristic (AUROC) of 0.88. The Logistic Regression and multilayer perceptron models both achieved AUROCs of 0.862. Among features that significantly impacted the prediction of ischemic stroke were previous stroke history, age, and mean systolic blood pressure. CONCLUSION: MLAs have the potential to more accurately predict the near risk of ischemic stroke within a 1-year prediction window for individuals who have been hospitalized. This risk stratification tool can be used to design clinical trials to test stroke prevention treatments in high-risk populations by identifying subjects who would be more likely to benefit from treatment.

Preliminary Evidence That Cortical Amyloid Burden Predicts Poor Response to Antidepressant Medication Treatment in Cognitively Intact Individuals With Late-Life Depression.

OBJECTIVE: Amyloid accumulation, the pathological hallmark of Alzheimer's disease, may predispose some older adults to depression and cognitive decline. Deposition of amyloid also occurs prior to the development of cognitive decline. It is unclear whether amyloid influences antidepressant outcomes in cognitively intact depressed elders. DESIGN: A pharmacoimaging trial utilizing florbetapir (18F) PET scanning followed by 2 sequential 8-week antidepressant medication trials. PARTICIPANTS: Twenty-seven depressed elders who were cognitively intact on screening. MEASUREMENTS AND INTERVENTIONS: After screening, diagnostic testing, assessment of depression severity and neuropsychological assessment, participants completed florbetapir (18F) PET scanning. They were then randomized to receive escitalopram or placebo for 8 weeks in a double-blinded two-to-one allocation rate. Individuals who did not respond to initial treatment transitioned to a second open-label trial of bupropion for another 8 weeks. RESULTS: Compared with 22 amyloid-negative participants, 5 amyloid-positive participants exhibited significantly less change in depression severity and a lower likelihood of remission. In the initial blinded trial, 4 of 5 amyloid-positive participants were nonremitters (80%), while only 18% (4 of 22) of amyloid-negative participants did not remit (p = 0.017; Fisher's Exact test). In separate models adjusting for key covariates, both positive amyloid status (t = 3.07, 21 df, p = 0.003) and higher cortical amyloid binding by standard uptake value ratio (t = 2.62, 21 df, p = 0.010) were associated with less improvement in depression severity. Similar findings were observed when examining change in depression status across both antidepressant trials. CONCLUSIONS: In this preliminary study, amyloid status predicted poor antidepressant response to sequential antidepressant treatment. Alternative treatment approaches may be needed for amyloid-positive depressed elders.

Cognitive complaints are associated with smaller right medial temporal gray-matter volume in younger postmenopausal women.

OBJECTIVE: Menopause is associated with increasing cognitive complaints and older women are at increased risk of developing Alzheimer disease compared to men. However, there is difficulty in early markers of risk using objective performance measures. We investigated the impact of subjective cognitive complaints on the cortical structure in a sample of younger postmenopausal women. METHODS: Data for this cross-sectional study were drawn from the baseline visit of a longer double-blind study examining estrogen-cholinergic interactions in normal postmenopausal women. Structural Magnetic Resonance Imaging was acquired on 44 women, aged 50-60 years and gray-matter volume was defined by voxel-based morphometry. Subjective measures of cognitive complaints and postmenopausal symptoms were obtained as well as tests of verbal episodic and working memory performance. RESULTS: Increased levels of cognitive complaints were associated with lower gray-matter volume in the right medial temporal lobe (r = -0.445, P < 0.002, R = 0.2). Increased depressive symptoms and somatic complaints were also related to increased cognitive complaints and smaller medial temporal volumes but did not mediate the effect of cognitive complaints. In contrast, there was no association between performance on the memory tasks and subjective cognitive ratings, or medial temporal lobe volume. CONCLUSIONS: The findings of the present study indicate that the level of reported cognitive complaints in postmenopausal women may be associated with reduced gray-matter volume which may be associated with cortical changes that may increase risk of future cognitive decline. : Video Summary:http://links.lww.com/MENO/A626.

Video Summary:http://links.lww.com/MENO/A626.

Accelerated brain aging predicts impaired cognitive performance and greater disability in geriatric but not midlife adult depression.

Depression is associated with markers of accelerated aging, but it is unclear how this relationship changes across the lifespan. We examined whether a brain-based measure of accelerated aging differed between depressed and never-depressed subjects across the adult lifespan and whether it was related to cognitive performance and disability. We applied a machine-learning approach that estimated brain age from structural MRI data in two depressed cohorts, respectively 170 midlife adults and 154 older adults enrolled in studies with common entry criteria. Both cohorts completed broad cognitive batteries and the older subgroup completed a disability assessment. The machine-learning model estimated brain age from MRI data, which was compared to chronological age to determine the brain-age gap (BAG; estimated age-chronological age). BAG did not differ between midlife depressed and nondepressed adults. Older depressed adults exhibited significantly higher BAG than nondepressed elders (Wald χ2 = 8.84, p = 0.0029), indicating a higher estimated brain age than chronological age. BAG was not associated with midlife cognitive performance. In the older cohort, higher BAG was associated with poorer episodic memory performance (Wald χ2 = 4.10, p = 0.0430) and, in the older depressed group alone, slower processing speed (Wald χ2 = 4.43, p = 0.0354). We also observed a statistical interaction where greater depressive symptom severity in context of higher BAG was associated with poorer executive function (Wald χ2 = 5.89, p = 0.0152) and working memory performance (Wald χ2 = 4.47, p = 0.0346). Increased BAG was associated with greater disability (Wald χ2 = 6.00, p = 0.0143). Unlike midlife depression, geriatric depression exhibits accelerated brain aging, which in turn is associated with cognitive and functional deficits.

Texture Analysis of F-18 Fluciclovine PET/CT to Predict Biochemically Recurrent Prostate Cancer: Initial Results.

Predicting biochemical recurrence of prostate cancer is imperative for initiating early treatment, which can improve the outcome of cancer treatment. However, because of inter- and intrareader variability in interpretation of F-18 fluciclovine positron emission tomography/computed tomography (PET/CT), it is difficult to reliably discern between necrotic tissue owing to radiation therapy and tumor tissue. Our goal is to develop a computational methodology using Haralick texture analysis that can be used as an adjunct tool to improve and standardize the interpretation of F-18 fluciclovine PET/CT to identify biochemical recurrence of prostate cancer. Four main textural features were chosen by variable selection procedure using least absolute shrinkage and selection operator logistic regression and bootstrapping, and then included as predictors in subsequent logistic ridge regression model for prediction (n = 28). Age at prostatectomy, prostate-specific antigen (PSA) level before the PET/CT imaging, and number of days between the prostate-specific antigen measurement and PET/CT imaging were also included in the prediction model. The overfitting-corrected area under the curve and Brier score of the proposed model were 0.94 (95% CI: 0.81, 1.00) and 0.12 (95% CI: 0.03, 0.23), respectively. Compared with a model with textural features (TI model) and that with only clinical information (CI model), the proposed model achieved 2% and 32% increase in AUC and 8% and 48% reduction in Brier score, respectively. Combining Haralick textural features based on the PET/CT imaging data with clinical information shows a high potential of enhanced prediction of the biochemical recurrence of prostate cancer.

A bayesian approach to examining default mode network functional connectivity and cognitive performance in major depressive disorder.

To reconcile the inconsistency of the association between the resting-state functional connectivity (RSFC) and cognitive performance in healthy and depressed groups due to high variance of both measures, we proposed a Bayesian spatio-temporal model to precisely and accurately estimate the RSFC in depressed and nondepressed participants. This model was employed to estimate spatially-adjusted functional connectivity (saFC) in the extended default mode network (DMN) that was hypothesized to correlate with cognitive performance in both depressed and nondepressed. Multiple linear regression models were used to study the relationship between DMN saFC and cognitive performance scores measured in the following four cognitive domains while adjusting for age, sex, and education. In ROI pairs including the posterior cingulate (PCC) and anterior cingulate (ACC) cortex regions, the relationship between connectivity and cognition was found only with the Bayesian approach. Moreover, only the Bayesian approach was able to detect a significant diagnostic difference in the association in ROI pairs, including both PCC and ACC regions, due to smaller variance for the saFC estimator. The results confirm that a reliable and precise saFC estimator, based on the Bayesian model, can foster scientific discovery that may not be feasible with the conventional ROI-based FC estimator (denoted as 'AVG-FC').

In vivo network models identify sex differences in the spread of tau pathology across the brain.

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.

Persistent Intrinsic Functional Network Connectivity Alterations in Middle-Aged and Older Women With Remitted Depression.

BACKGROUND: In younger adults, residual alterations in functional neural networks persist during remitted depression. However, there are fewer data for midlife and older adults at risk of recurrence. Such residual network alterations may contribute to vulnerability to recurrence. This study examined intrinsic network functional connectivity in midlife and older women with remitted depression. METHODS: A total of 69 women (24 with a history of depression, 45 with no psychiatric history) over 50 years of age completed 3T fMRI with resting-state acquisition. Participants with remitted depression met DSM-IV-TR criteria for an episode in the last 10 years but not the prior year. Whole-brain seed-to-voxel resting-state functional connectivity analyses examined the default mode network (DMN), executive control network (ECN), and salience network (SN), plus bilateral hippocampal seeds. All analyses were adjusted for age and used cluster-level correction for multiple comparisons with FDR < 0.05 and a height threshold of p < 0.001, uncorrected. RESULTS: Women with a history of depression exhibited decreased functional connectivity between the SN (right insula seed) and ECN regions, specifically the left superior frontal gyrus. They also exhibited increased functional connectivity between the left hippocampus and the left postcentral gyrus. We did not observe any group differences in functional connectivity for DMN or ECN seeds. CONCLUSIONS: Remitted depression in women is associated with connectivity differences between the SN and ECN and between the hippocampus and the postcentral gyrus, a region involved in interoception. Further work is needed to determine whether these findings are related to functional alterations or are predictive of recurrence.

Associations of informant-based sleep reports with Alzheimer's disease pathologies.

PURPOSE: Recent studies have found associations of increased brain amyloid beta (Aß) accumulation and several abnormal sleep-wake patterns, including shorter latency and increased fragmentation in preclinical Alzheimer's disease (AD). There is little known about the relationship between sleep and tau. The objective of this study was to understand the associations of both tau and Aß with early signs of sleep and night-time behavior changes in clinically normal elderly adults. Specifically, we have addressed the question of how informant-based subjective sleep reports are linked to regional [18F]flortaucipir and [18F]florbetapir uptake. METHODS: Imaging and behavioral data from 35 subjects were obtained from the Alzheimer's Disease Neuroimaging Initiative. The Neuropsychiatric Inventory Sleep (NPI-sleep) Questionnaire was used to assess the sleep and night-time behavior changes. Regional tau-positron emission tomography (PET) (entorhinal, brainstem) and Aß-PET (posterior cingulate, precuneus, medial orbitofrontal) uptake values were calculated. A series of linear regression analyses were used to determine the combination of sleep symptoms that built the best models to predict each pathology. RESULTS: Informant-based reports of abnormal night-time behavior (NPI questions k3, k5, and k8) were significantly associated with increased entorhinal tau and Aß (all regions) accumulation. Interestingly, informant-based reports of sleep deficiencies without abnormal nigh-time activity (NPI questions k1, k2, and k6) were negatively associated with entorhinal tau burden. CONCLUSION: Detection of abnormal night-time behaviors (wandering, pacing, other inappropriate activities) by family members indicates early signs of both AD pathologies and may encourage the affected individuals to seek help by health care providers for detailed cognitive/neurobehavioral assessments.

The relationship between domain-specific subjective cognitive decline and Alzheimer's pathology in normal elderly adults.

We evaluated the associations of subjective (self-reported everyday cognition [ECog]) and objective cognitive measures with regional amyloid-ß (Aß) and tau accumulation in 86 clinically normal elderly subjects from the Alzheimer's Disease Neuroimaging Initiative. Regression analyses were conducted to identify whether individual ECog domains (Memory, Language, Organization, Planning, Visuospatial, and Divided Attention) were equally or differentially associated with regional [18F]florbetapir and [18F]flortaucipir uptake and how these associations compared to those obtained with objective cognitive measures. A texture analysis, the weighted 2-point correlation, was used as an additional approach for estimating the whole-brain tau burden without positron emission tomography intensity normalization. Although the strongest models for ECog domains included either tau (planning and visuospatial) or Aß (memory and organization), the strongest models for all objective measures included Aß. In Aß-negative participants, the strongest models for all ECog domains of executive functioning included tau. Our results indicate differential associations of individual subjective cognitive domains with Aß and tau in clinically normal adults. Detailed characterization of ECog may render a valuable prescreening tool for pathological prediction.

Hyperpolarizing Concentrated Metronidazole 15 NO2 Group over Six Chemical Bonds with More than 15 % Polarization and a 20†Minute Lifetime.

The NMR hyperpolarization of uniformly 15 N-labeled [15 N3 ]metronidazole is demonstrated by using SABRE-SHEATH. In this antibiotic, the 15 NO2 group is hyperpolarized through spin relays created by 15 N spins in [15 N3 ]metronidazole, and the polarization is transferred from parahydrogen-derived hydrides over six chemical bonds. In less than a minute of parahydrogen bubbling at approximately 0.4 µT, a high level of nuclear spin polarization (P15N ) of around 16 % is achieved on all three 15 N sites. This product of 15 N polarization and concentration of 15 N spins is around six-fold better than any previous value determined for 15 N SABRE-derived hyperpolarization. At 1.4 T, the hyperpolarized state persists for tens of minutes (relaxation time, T1 ≈10 min). A novel synthesis of uniformly 15 N-enriched metronidazole is reported with a yield of 15 %. This approach can potentially be used for synthesis of a wide variety of in vivo metabolic probes with potential uses ranging from hypoxia sensing to theranostic imaging.

Task-based design of a synthetic-collimator SPECT system used for small animal imaging.

PURPOSE: In traditional multipinhole SPECT systems, image multiplexing - the overlapping of pinhole projection images - may occur on the detector, which can inhibit quality image reconstructions due to photon-origin uncertainty. One proposed system to mitigate the effects of multiplexing is the synthetic-collimator SPECT system. In this system, two detectors, a silicon detector and a germanium detector, are placed at different distances behind the multipinhole aperture, allowing for image detection to occur at different magnifications and photon energies, resulting in higher overall sensitivity while maintaining high resolution. The unwanted effects of multiplexing are reduced by utilizing the additional data collected from the front silicon detector. However, determining optimal system configurations for a given imaging task requires efficient parsing of the complex parameter space, to understand how pinhole spacings and the two detector distances influence system performance. METHODS: In our simulation studies, we use the ensemble mean-squared error of the Wiener estimator (EMSEW ) as the figure of merit to determine optimum system parameters for the task of estimating the uptake of an 123 I-labeled radiotracer in three different regions of a computer-generated mouse brain phantom. The segmented phantom map is constructed by using data from the MRM NeAt database and allows for the reduction in dimensionality of the system matrix which improves the computational efficiency of scanning the system's parameter space. To contextualize our results, the Wiener estimator is also compared against a region of interest estimator using maximum-likelihood reconstructed data. RESULTS: Our results show that the synthetic-collimator SPECT system outperforms traditional multipinhole SPECT systems in this estimation task. We also find that image multiplexing plays an important role in the system design of the synthetic-collimator SPECT system, with optimal germanium detector distances occurring at maxima in the derivative of the percent multiplexing function. Furthermore, we report that improved task performance can be achieved by using an adaptive system design in which the germanium detector distance may vary with projection angle. Finally, in our comparative study, we find that the Wiener estimator outperforms the conventional region of interest estimator. CONCLUSIONS: Our work demonstrates how this optimization method has the potential to quickly and efficiently explore vast parameter spaces, providing insight into the behavior of competing factors, which are otherwise very difficult to calculate and study using other existing means.

Application of Haralick texture features in brain [18F]-florbetapir positron emission tomography without reference region normalization.

OBJECTIVES: Semi-quantitative image analysis methods in Alzheimer's Disease (AD) require normalization of positron emission tomography (PET) images. However, recent studies have found variabilities associated with reference region selection of amyloid PET images. Haralick features (HFs) generated from the Gray Level Co-occurrence Matrix (GLCM) quantify spatial characteristics of amyloid PET radiotracer uptake without the need for intensity normalization. The objective of this study is to calculate several HFs in different diagnostic groups and determine the group differences. METHODS: All image and metadata were acquired through the Alzheimer's Disease Neuroimaging Initiative database. Subjects were grouped in three ways: by clinical diagnosis, by APOE e4 allele, and by Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-Cog) score. Several GLCM matrices were calculated for different direction and distances (1-4 mm) from multiple regions on PET images. The HFs, contrast, correlation, dissimilarity, energy, entropy, and homogeneity, were calculated from these GLCMs. Wilcoxon tests and Student t-tests were performed on Haralick features and standardized uptake value ratio (SUVR) values, respectively, to determine group differences. In addition to statistical testing, receiver operating characteristic (ROC) curves were generated to determine the discrimination performance of the selected regional HFs and the SUVR values. RESULTS: Preliminary results from statistical testing indicate that HFs were capable of distinguishing groups at baseline and follow-up (false discovery rate corrected p<0.05) in particular regions at much higher occurrences than SUVR (81 of 252). Conversely, we observed nearly no significant differences between all groups within ROIs at baseline or follow-up utilizing SUVR. From the ROC analysis, we found that the Energy and Entropy offered the best performance to distinguish Normal versus mild cognitive impairment and ADAS-Cog negative versus ADAS-Cog positive groups. CONCLUSION: These results suggest that this technique could improve subject stratification in AD drug trials and help to evaluate the disease progression and treatment effects longitudinally without the disadvantages associated with intensity normalization.

A new data analysis approach for measuring longitudinal changes of metabolism in cognitively normal elderly adults.

INTRODUCTION: Previously, we discussed several critical barriers in including [18F] fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) imaging of preclinical Alzheimer's disease (AD) subjects. These factors included the reference region selection and intensity normalization of PET images and the within- and across-subject variability of affected brain regions. In this study, we utilized a novel FDG-PET analysis, the regional FDG time correlation coefficient, rFTC, that can address and resolve these barriers and provide a more sensitive way of monitoring longitudinal changes in metabolism of cognitively normal elderly adults. The rFTC analysis captures the within-subject similarities between baseline and follow-up regional radiotracer distributions. METHODS: The rFTC trajectories of 27 cognitively normal subjects were calculated to identify 1) trajectories of rFTC decline in individual cognitively normal subjects; 2) how these trajectories correlate with the subjects' cognitive test scores, baseline cerebrospinal fluid (CSF) levels of amyloid beta (Aß), and apolipoprotein E4 (APOE-E4) status; and 3) whether similar trajectories are observed in regional/composite standardized uptake value ratio (SUVR) values. RESULTS: While some of the subjects maintained a stable rFTC trajectory, other subjects had declining and fluctuating rFTC values. We found that the rFTC decline was significantly higher in APOE-E4 carriers compared to noncarriers (p=0.04). We also found a marginally significant association between rFTC decline and cognitive decline measured by Alzheimer's Disease Assessment Scale - cognitive subscale (ADAS_cog) decline (0.05). In comparison to the rFTC trajectories, the composite region of interest (ROI) SUVR trajectories did not change in any of the subjects. No individual/composite ROI SUVR changes contributed significantly to explaining changes in ADAS_cog, conversion to mild cognitive impairment (MCI), or any general changes in clinical symptoms. CONCLUSION: The rFTC decline may serve as a new biomarker of early metabolic changes before the MCI stage.

Longitudinal Positron Emission Tomography in Preventive Alzheimer's Disease Drug Trials, Critical Barriers from Imaging Science Perspective.

Recent Alzheimer's trials have recruited cognitively normal people at risk for Alzheimer's dementia. Due to the lack of clinical symptoms in normal population, conventional clinical outcome measures are not suitable for these early trials. While several groups are developing new composite cognitive tests that could serve as potential outcome measures by detecting subtle cognitive changes in normal people, there is a need for longitudinal brain imaging techniques that can correlate with temporal changes in these new tests and provide additional objective measures of neuropathological changes in brain. Positron emission tomography (PET) is a nuclear medicine imaging procedure based on the measurement of annihilation photons after positron emission from radiolabeled molecules that allow tracking of biological processes in body, including the brain. PET is a well-established in vivo imaging modality in Alzheimer's disease diagnosis and research due to its capability of detecting abnormalities in three major hallmarks of this disease. These include (1) amyloid beta plaques; (2) neurofibrillary tau tangles and (3) decrease in neuronal activity due to loss of nerve cell connection and death. While semiquantitative PET imaging techniques are commonly used to set discrete cut-points to stratify abnormal levels of amyloid accumulation and neurodegeneration, they are suboptimal for detecting subtle longitudinal changes. In this study, we have identified and discussed four critical barriers in conventional longitudinal PET imaging that may be particularly relevant for early Alzheimer's disease studies. These include within and across subject heterogeneity of AD-affected brain regions, PET intensity normalization, neuronal compensations in early disease stages and cerebrovascular amyloid deposition.