Publications -- Melissa E. Petersen

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12503/31203

This collection is limited to articles published under the terms of a creative commons license or other open access publishing agreement since 2016. It is not intended as a complete list of the author's works.

Browse

Now showing 1 - 13 of 13

Evaluation of Neighborhood-Level Disadvantage and Cognition in Mexican American and Non-Hispanic White Adults 50 Years and Older in the US
(American Medical Association, 2023-08-30) Wong, Christina G.; Miller, Justin B.; Zhang, Fan; Rissman, Robert A.; Raman, Rema; Hall, James R.; Petersen, Melissa E.; Yaffe, Kristine; Kind, Amy J.; O'Bryant, Sid E.; Team, HABS-HD Study
IMPORTANCE: Understanding how socioeconomic factors are associated with cognitive aging is important for addressing health disparities in Alzheimer disease. OBJECTIVE: To examine the association of neighborhood disadvantage with cognition among a multiethnic cohort of older adults. DESIGN, SETTING, AND PARTICIPANTS: In this cross-sectional study, data were collected between September 1, 2017, and May 31, 2022. Participants were from the Health and Aging Brain Study-Health Disparities, which is a community-based single-center study in the Dallas/Fort Worth area of Texas. A total of 1614 Mexican American and non-Hispanic White adults 50 years and older were included. EXPOSURE: Neighborhood disadvantage for participants' current residence was measured by the validated Area Deprivation Index (ADI); ADI Texas state deciles were converted to quintiles, with quintile 1 representing the least disadvantaged area and quintile 5 the most disadvantaged area. Covariates included age, sex, and educational level. MAIN OUTCOMES AND MEASURES: Performance on cognitive tests assessing memory, language, attention, processing speed, and executive functioning; measures included the Spanish-English Verbal Learning Test (SEVLT) Learning and Delayed Recall subscales; Wechsler Memory Scale, third edition (WMS-III) Digit Span Forward, Digit Span Backward, and Logical Memory 1 and 2 subscales; Trail Making Test (TMT) parts A and B; Digit Symbol Substitution Test (DSST); Letter Fluency; and Animal Naming. Raw scores were used for analyses. Associations between neighborhood disadvantage and neuropsychological performance were examined via demographically adjusted linear regression models stratified by ethnic group. RESULTS: Among 1614 older adults (mean [SD] age, 66.3 [8.7] years; 980 women [60.7%]), 853 were Mexican American (mean [SD] age, 63.9 [7.9] years; 566 women [66.4%]), and 761 were non-Hispanic White (mean [SD] age, 69.1 [8.7] years; 414 women [54.4%]). Older Mexican American adults were more likely to reside in the most disadvantaged areas (ADI quintiles 3-5), with 280 individuals (32.8%) living in ADI quintile 5, whereas a large proportion of older non-Hispanic White adults resided in ADI quintile 1 (296 individuals [38.9%]). Mexican American individuals living in more disadvantaged areas had worse performance than those living in ADI quintile 1 on 7 of 11 cognitive tests, including SEVLT Learning (ADI quintile 5: beta = -2.50; 95% CI, -4.46 to -0.54), SEVLT Delayed Recall (eg, ADI quintile 3: beta = -1.11; 95% CI, -1.97 to -0.24), WMS-III Digit Span Forward (eg, ADI quintile 4: beta = -1.14; 95% CI, -1.60 to -0.67), TMT part A (ADI quintile 5: beta = 7.85; 95% CI, 1.28-14.42), TMT part B (eg, ADI quintile 5: beta = 31.5; 95% CI, 12.16-51.35), Letter Fluency (ADI quintile 4: beta = -2.91; 95% CI, -5.39 to -0.43), and DSST (eg, ADI quintile 5: beta = -4.45; 95% CI, -6.77 to -2.14). In contrast, only non-Hispanic White individuals living in ADI quintile 4 had worse performance than those living in ADI quintile 1 on 4 of 11 cognitive tests, including SEVLT Learning (beta = -2.35; 95% CI, -4.40 to -0.30), SEVLT Delayed Recall (beta = -0.95; 95% CI, -1.73 to -0.17), TMT part B (beta = 15.95; 95% CI, 2.47-29.44), and DSST (beta = -3.96; 95% CI, -6.49 to -1.43). CONCLUSIONS AND RELEVANCE: In this cross-sectional study, aging in a disadvantaged area was associated with worse cognitive functioning, particularly for older Mexican American adults. Future studies examining the implications of exposure to neighborhood disadvantage across the life span will be important for improving cognitive outcomes in diverse populations.
Hyperparameter Tuning with High Performance Computing Machine Learning for Imbalanced Alzheimer's Disease Data
(MDPI, 2022-11-17) Zhang, Fan; Petersen, Melissa E.; Johnson, Leigh A.; Hall, James R.; O'Bryant, Sid E.
Accurate detection is still a challenge in machine learning (ML) for Alzheimer's disease (AD). Class imbalance in imbalanced AD data is another big challenge for machine-learning algorithms working under the assumption that the data are evenly distributed within classes. Here, we present a hyperparameter tuning workflow with high-performance computing (HPC) for imbalanced data related to prevalent mild cognitive impairment (MCI) and AD in the Health and Aging Brain Study-Health Disparities (HABS-HD) project. We applied a single-node multicore parallel mode to hyperparameter tuning of gamma, cost, and class weight using a support vector machine (SVM) model with 10 times repeated fivefold cross-validation. We executed the hyperparameter tuning workflow with R's bigmemory, foreach, and doParallel packages on Texas Advanced Computing Center (TACC)'s Lonestar6 system. The computational time was dramatically reduced by up to 98.2% for the high-performance SVM hyperparameter tuning model, and the performance of cross-validation was also improved (the positive predictive value and the negative predictive value at base rate 12% were, respectively, 16.42% and 92.72%). Our results show that a single-node multicore parallel structure and high-performance SVM hyperparameter tuning model can deliver efficient and fast computation and achieve outstanding agility, simplicity, and productivity for imbalanced data in AD applications.
Predicting overall survival in diffuse glioma from the presurgical connectome
(Springer Nature, 2022-11-06) Kesler, Shelli R.; Harrison, Rebecca A.; Rao, Vikram; Dyson, Hannah; Petersen, Melissa E.; Prinsloo, Sarah
Diffuse gliomas are incurable brain tumors, yet there is significant heterogeneity in patient survival. Advanced computational techniques such as radiomics show potential for presurgical prediction of survival and other outcomes from neuroimaging. However, these techniques ignore non-lesioned brain features that could be essential for improving prediction accuracy. Gray matter covariance network (connectome) features were retrospectively identified from the T1-weighted MRIs of 305 adult patients diagnosed with diffuse glioma. These features were entered into a Cox proportional hazards model to predict overall survival with 10-folds cross-validation. The mean time-dependent area under the curve (AUC) of the connectome model was compared with the mean AUCs of clinical and radiomic models using a pairwise t-test with Bonferroni correction. One clinical model included only features that are known presurgery (clinical) and another included an advantaged set of features that are not typically known presurgery (clinical +). The median survival time for all patients was 134.2 months. The connectome model (AUC 0.88 +/- 0.01) demonstrated superior performance (P < 0.001, corrected) compared to the clinical (AUC 0.61 +/- 0.02), clinical + (AUC 0.79 +/- 0.01) and radiomic models (AUC 0.75 +/- 0.02). These findings indicate that the connectome is a feasible and reliable early biomarker for predicting survival in patients with diffuse glioma. Connectome and other whole-brain models could be valuable tools for precision medicine by informing patient risk stratification and treatment decision-making.
Application of Structural Retinal Biomarkers to Detect Cognitive Impairment in a Primary Care Setting
(IOS Press, 2023-02-02) Mozdbar, Sima; Petersen, Melissa E.; Zhang, Fan; Johnson, Leigh A.; Tolman, Alex; Nyalakonda, Ramyashree; Gutierrez, Alejandra; O'Bryant, Sid E.
BACKGROUND: Despite the diagnostic accuracy of advanced neurodiagnostic procedures, the detection of Alzheimer's disease (AD) remains poor in primary care. There is an urgent need for screening tools to aid in the detection of early AD. OBJECTIVE: This study examines the predictive ability of structural retinal biomarkers in detecting cognitive impairment in a primary care setting. METHODS: Participants were recruited from Alzheimer's Disease in Primary Care (ADPC) study. As part of the ADPC Retinal Biomarker Study (ADPC RBS), visual acuity, an ocular history questionnaire, eye pressure, optical coherence tomography (OCT) imaging, and fundus imaging was performed. RESULTS: Data were examined on n = 91 participants. The top biomarkers for predicting cognitive impairment included the inferior quadrant of the outer retinal layers, all four quadrants of the peripapillary retinal nerve fiber layer, and the inferior quadrant of the macular retinal nerve fiber layer. CONCLUSION: The current data provides strong support for continued investigation into structural retinal biomarkers, particularly the retinal nerve fiber layer, as screening tools for AD.
A Precision Medicine Approach to Treating Alzheimer's Disease Using Rosiglitazone Therapy: A Biomarker Analysis of the REFLECT Trials
(IOS Press, 2021-05-18) O'Bryant, Sid E.; Zhang, Fan; Petersen, Melissa E.; Johnson, Leigh A.; Hall, James R.; Rissman, Robert A.
Background: The REFLECT trials were conducted to examine the treatment of mild-to-moderate Alzheimer's disease utilizing a peroxisome proliferator-activated receptor gamma agonist. Objective: To generate a predictive biomarker indicative of positive treatment response using samples from the previously conducted REFLECT trials. Methods: Data were analyzed on 360 participants spanning multiple negative REFLECT trials, which included treatment with rosiglitazone and rosiglitazone XR. Support vector machine analyses were conducted to generate a predictive biomarker profile. Results: A pre-defined 6-protein predictive biomarker (IL6, IL10, CRP, TNFɑ, FABP-3, and PPY) correctly classified treatment response with 100% accuracy across study arms for REFLECT Phase II trial (AVA100193) and multiple Phase III trials (AVA105640, AV102672, and AVA102670). When the data was combined across all rosiglitazone trial arms, a global RSG-predictive biomarker with the same 6-protein predictive biomarker was able to accurately classify 98%of treatment responders. Conclusion: A predictive biomarker comprising of metabolic and inflammatory markers was highly accurate in identifying those patients most likely to experience positive treatment response across the REFLECT trials. This study provides additional proof-of-concept that a predictive biomarker can be utilized to help with screening and predicting treatment response, which holds tremendous benefit for clinical trials.
Accelerating Hyperparameter Tuning in Machine Learning for Alzheimer's Disease With High Performance Computing
(Frontiers Media S.A., 2021-12-08) Zhang, Fan; Petersen, Melissa E.; Johnson, Leigh A.; Hall, James R.; O'Bryant, Sid E.
Driven by massive datasets that comprise biomarkers from both blood and magnetic resonance imaging (MRI), the need for advanced learning algorithms and accelerator architectures, such as GPUs and FPGAs has increased. Machine learning (ML) methods have delivered remarkable prediction for the early diagnosis of Alzheimer's disease (AD). Although ML has improved accuracy of AD prediction, the requirement for the complexity of algorithms in ML increases, for example, hyperparameters tuning, which in turn, increases its computational complexity. Thus, accelerating high performance ML for AD is an important research challenge facing these fields. This work reports a multicore high performance support vector machine (SVM) hyperparameter tuning workflow with 100 times repeated 5-fold cross-validation for speeding up ML for AD. For demonstration and evaluation purposes, the high performance hyperparameter tuning model was applied to public MRI data for AD and included demographic factors such as age, sex and education. Results showed that computational efficiency increased by 96%, which helped to shed light on future diagnostic AD biomarker applications. The high performance hyperparameter tuning model can also be applied to other ML algorithms such as random forest, logistic regression, xgboost, etc.
Neurodegeneration from the AT(N) framework is different among Mexican Americans compared to non-Hispanic Whites: A Health & Aging Brain among Latino Elders (HABLE) Study
(Wiley Periodicals, LLC, 2022-02-09) O'Bryant, Sid E.; Zhang, Fan; Petersen, Melissa E.; Hall, James R.; Johnson, Leigh A.; Yaffe, Kristine; Braskie, Meredith N.; Rissman, Robert A.; Vig, Rocky; Toga, Arthur W.
Introduction: We sought to examine a magnetic resonance imaging (MRI)-based marker of neurodegeneration from the AT(N) (amyloid/tau/neurodegeneration) framework among a multi-ethnic, community-dwelling cohort. Methods: Community-dwelling Mexican Americans and non-Hispanic White adults and elders were recruited. All participants underwent comprehensive assessments including an interview, functional exam, clinical labs, informant interview, neuropsychological testing and 3T MRI of the brain. A neurodegeneration MRI meta-region of interest (ROI) biomarker for the AT(N) framework was calculated. Results: Data were examined from n = 1305 participants. Mexican Americans experienced N at significantly younger ages. The N biomarker was significantly associated with cognitive outcomes. N was significantly impacted by cardiovascular factors (e.g., total cholesterol, low-density lipoprotein) among non-Hispanic Whites whereas diabetes (glucose, HbA1c, duration of diabetes) and sociocultural (household income, acculturation) factors were strongly associated with N among Mexican Americans. Discussion: The prevalence, progression, timing, and sequence of the AT(N) biomarkers must be examined across diverse populations.
Proteomic profiles of prevalent mild cognitive impairment and Alzheimer's disease among adults with Down syndrome
(Wiley Periodicals, Inc., 2020-04-17) Petersen, Melissa E.; Zhang, Fan; Krinsky-McHale, Sharon J.; Silverman, Wayne; Lee, Joseph H.; Pang, Deborah; Hall, James R.; Schupf, Nicole; O'Bryant, Sid E.
Introduction: We sought to determine if a proteomic profile approach developed to detect Alzheimer's disease (AD) in the general population would apply to adults with Down syndrome (DS). Methods: Plasma samples were obtained from 398 members of a community-based cohort of adults with DS. A total of n = 186 participants were determined to be non-demented and without mild cognitive impairment (MCI) at baseline and throughout follow-up; n = 50 had prevalent MCI; n = 42 had prevalent AD. Results: The proteomic profile yielded an area under the curve (AUC) of 0.92, sensitivity (SN) = 0.80, and specificity (SP) = 0.98 detecting prevalent MCI. For detecting prevalent AD, the proteomic profile yielded an AUC of 0.89, SN = 0.81, and SP = 0.97. The overall profile closely resembled our previously published profile of AD in the general population. Discussion: These data provide evidence of the applicability of our blood-based algorithm for detecting MCI/AD among adults with DS.
Proteomic profiles for Alzheimer's disease and mild cognitive impairment among adults with Down syndrome spanning serum and plasma: An Alzheimer's Biomarker Consortium-Down Syndrome (ABC-DS) study
(Wiley Periodicals, Inc., 2020-06-30) Petersen, Melissa E.; Zhang, Fan; Schupf, Nicole; Krinsky-McHale, Sharon J.; Hall, James R.; Mapstone, Mark; Cheema, Amrita; Silverman, Wayne; Lott, Ira; Rafii, Michael S.; Handen, Benjamin; Klunk, William; Head, Elizabeth; Christian, Bradley; Foroud, Tatiana; Lai, Florence; Rosas, H. Diana; Zaman, Shahid; Ances, Beau M.; Wang, Mei-Cheng; Tycko, Benjamin; Lee, Joseph H.; O'Bryant, Sid E.
Introduction: Previously generated serum and plasma proteomic profiles were examined among adults with Down syndrome (DS) to determine whether these profiles could discriminate those with mild cognitive impairment (MCI-DS) and Alzheimer's disease (DS-AD) from those cognitively stable (CS). Methods: Data were analyzed on n = 305 (n = 225 CS; n = 44 MCI-DS; n = 36 DS-AD) enrolled in the Alzheimer's Biomarker Consortium-Down Syndrome (ABC-DS). Results: Distinguishing MCI-DS from CS, the serum profile produced an area under the curve (AUC) = 0.95 (sensitivity [SN] = 0.91; specificity [SP] = 0.99) and an AUC = 0.98 (SN = 0.96; SP = 0.97) for plasma when using an optimized cut-off score. Distinguishing DS-AD from CS, the serum profile produced an AUC = 0.93 (SN = 0.81; SP = 0.99) and an AUC = 0.95 (SN = 0.86; SP = 1.0) for plasma when using an optimized cut-off score. AUC remained unchanged to slightly improved when age and sex were included. Eotaxin3, interleukin (IL)-10, C-reactive protein, IL-18, serum amyloid A , and FABP3 correlated fractions at r2 > = 0.90. Discussion: Proteomic profiles showed excellent detection accuracy for MCI-DS and DS-AD.
Characterization of the Meal-Stimulated Incretin Response and Relationship With Structural Brain Outcomes in Aging and Alzheimer's Disease
(Frontiers Media S.A., 2020-11-30) Morris, Jill K.; John, Casey S.; Green, Zachary D.; Wilkins, Heather M.; Wang, Xiaowan; Kamat, Ashwini; Swerdlow, Russell S.; Vidoni, Eric D.; Petersen, Melissa E.; O'Bryant, Sid E.; Honea, Robyn A.; Burns, Jeffrey M.
Background: Individuals with Alzheimer's Disease (AD) are often characterized by systemic markers of insulin resistance; however, the broader effects of AD on other relevant metabolic hormones, such as incretins that affect insulin secretion and food intake, remains less clear. Methods: Here, we leveraged a physiologically relevant meal tolerance test to assess diagnostic differences in these metabolic responses in cognitively healthy older adults (CH; n = 32) and AD (n = 23) participants. All individuals also underwent a comprehensive clinical examination, cognitive evaluation, and structural magnetic resonance imaging. Results: The meal-stimulated response of glucose, insulin, and peptide tyrosine tyrosine (PYY) was significantly greater in individuals with AD as compared to CH. Voxel-based morphometry revealed negative relationships between brain volume and the meal-stimulated response of insulin, C-Peptide, and glucose-dependent insulinotropic polypeptide (GIP) in primarily parietal brain regions. Conclusion: Our findings are consistent with prior work that shows differences in metabolic regulation in AD and relationships with cognition and brain structure.
Plasma Total Tau and Neurobehavioral Symptoms of Cognitive Decline in Cognitively Normal Older Adults
(Frontiers Media S.A., 2021-11-05) Hall, James R.; Petersen, Melissa E.; Johnson, Leigh A.; O'Bryant, Sid E.
Depression and related neurobehavioral symptoms are common features of Alzheimer's disease and other dementias. The presence of these potentially modifiable neurobehavioral symptoms in cognitively intact older adults may represent an early indication of pathophysiological processes in the brain. Tau pathology is a key feature of a number of dementias. A number of studies have found an association between tau and neurobehavioral symptoms. The current study investigated the relationship of a blood-based biomarker of tau and symptoms of depression, anxiety, worry, and sleep disturbances in 538 community based, cognitively normal older adults. Logistic regression revealed no significant relationship between plasma total tau and any measures of neurobehavioral symptoms. To assess the impact of level of tau on these relationships, participants were divided into those in the highest quintile of tau and those in the lower four quintiles. Regression analyses showed a significant relationship between level of plasma total tau and measures of depression, apathy, anxiety, worry and sleep. The presence of higher levels of plasma tau and elevated neurobehavioral symptoms may be an early indicator of cognitive decline and prodromal Alzheimer's disease. Longitudinal research is needed to evaluate the impact of these factors on the development of dementia and may suggest areas for early intervention.
Acute Regression in Down Syndrome
(MDPI, 2021-08-23) Handen, Benjamin; Clare, Isabel; Laymon, Charles; Petersen, Melissa E.; Zaman, Shahid; O'Bryant, Sid E.; Minhas, Davneet; Tudorascu, Dana; Brown, Stephanie; Christian, Bradley
Acute regression has been reported in some individuals with Down syndrome (DS), typically occurring between the teenage years and mid to late 20s. Characterized by sudden, and often unexplained, reductions in language skills, functional living skills and reduced psychomotor activity, some individuals have been incorrectly diagnosed with Alzheimer's disease (AD).|This paper compares five individuals with DS who previously experienced acute regression with a matched group of 15 unaffected individuals with DS using a set of AD biomarkers.|While the sample was too small to conduct statistical analyses, findings suggest there are possible meaningful differences between the groups on proteomics biomarkers (e.g., NfL, total tau). Hippocampal, caudate and putamen volumes were slightly larger in the regression group, the opposite of what was hypothesized. A slightly lower amyloid load was found on the PET scans for the regression group, but no differences were noted on tau PET.|Some proteomics biomarker findings suggest that individuals with DS who experience acute regression may be at increased risk for AD at an earlier age in comparison to unaffected adults with DS. However, due to the age of the group (mean 38 years), it may be too early to observe meaningful group differences on image-based biomarkers.
The Health & Aging Brain among Latino Elders (HABLE) study methods and participant characteristics
(Wiley Periodicals, LLC, 2021-06-21) O'Bryant, Sid E.; Johnson, Leigh A.; Barber, Robert C.; Braskie, Meredith N.; Christian, Bradley; Hall, James R.; Hazra, Nalini; King, Kevin; Kothapalli, Deydeep; Large, Stephanie; Mason, David; Matsiyevskiy, Elizabeth; McColl, Roderick; Nandy, Rajesh; Palmer, Raymond; Petersen, Melissa E.; Philips, Nicole; Rissman, Robert A.; Shi, Yonggang; Toga, Arthur W.; Vintimilla, Raul; Vig, Rocky; Zhang, Fan; Yaffe, Kristine
Introduction: Mexican Americans remain severely underrepresented in Alzheimer's disease (AD) research. The Health & Aging Brain among Latino Elders (HABLE) study was created to fill important gaps in the existing literature. Methods: Community-dwelling Mexican Americans and non-Hispanic White adults and elders (age 50 and above) were recruited. All participants underwent comprehensive assessments including an interview, functional exam, clinical labs, informant interview, neuropsychological testing, and 3T magnetic resonance imaging (MRI) of the brain. Amyloid and tau positron emission tomography (PET) scans were added at visit 2. Blood samples were stored in the Biorepository. Results: Data was examined from n = 1705 participants. Significant group differences were found in medical, demographic, and sociocultural factors. Cerebral amyloid and neurodegeneration imaging markers were significantly different between Mexican Americans and non-Hispanic Whites. Discussion: The current data provide strong support for continued investigations that examine the risk factors for and biomarkers of AD among diverse populations.

Browse

Recent Submissions